date stringdate 2023-05-04 00:00:00 2025-08-27 00:00:00 | arxiv_id stringlengths 10 10 | votes int32 0 110M | title stringlengths 8 206 | abstract stringlengths 165 1.92k | url stringlengths 40 40 |
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2023-05-26 | 2305.15798 | 4 | On Architectural Compression of Text-to-Image Diffusion Models | Exceptional text-to-image (T2I) generation results of Stable Diffusion models
(SDMs) come with substantial computational demands. To resolve this issue,
recent research on efficient SDMs has prioritized reducing the number of
sampling steps and utilizing network quantization. Orthogonal to these
directions, this study ... | https://huggingface.co/papers/2305.15798 |
2023-05-26 | 2305.15779 | 3 | Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models | Text-to-image diffusion models can generate diverse, high-fidelity images
based on user-provided text prompts. Recent research has extended these models
to support text-guided image editing. While text guidance is an intuitive
editing interface for users, it often fails to ensure the precise concept
conveyed by users. ... | https://huggingface.co/papers/2305.15779 |
2023-05-26 | 2305.15719 | 2 | Efficient Neural Music Generation | Recent progress in music generation has been remarkably advanced by the
state-of-the-art MusicLM, which comprises a hierarchy of three LMs,
respectively, for semantic, coarse acoustic, and fine acoustic modelings. Yet,
sampling with the MusicLM requires processing through these LMs one by one to
obtain the fine-grained... | https://huggingface.co/papers/2305.15719 |
2023-05-26 | 2305.15586 | 2 | Manifold Diffusion Fields | We present Manifold Diffusion Fields (MDF), an approach to learn generative
models of continuous functions defined over Riemannian manifolds. Leveraging
insights from spectral geometry analysis, we define an intrinsic coordinate
system on the manifold via the eigen-functions of the Laplace-Beltrami
Operator. MDF repres... | https://huggingface.co/papers/2305.15586 |
2023-05-26 | 2305.15581 | 2 | Unsupervised Semantic Correspondence Using Stable Diffusion | Text-to-image diffusion models are now capable of generating images that are
often indistinguishable from real images. To generate such images, these models
must understand the semantics of the objects they are asked to generate. In
this work we show that, without any training, one can leverage this semantic
knowledge ... | https://huggingface.co/papers/2305.15581 |
2023-05-29 | 2305.16311 | 7 | Break-A-Scene: Extracting Multiple Concepts from a Single Image | Text-to-image model personalization aims to introduce a user-provided concept
to the model, allowing its synthesis in diverse contexts. However, current
methods primarily focus on the case of learning a single concept from multiple
images with variations in backgrounds and poses, and struggle when adapted to a
differen... | https://huggingface.co/papers/2305.16311 |
2023-05-29 | 2305.17098 | 4 | ControlVideo: Adding Conditional Control for One Shot Text-to-Video
Editing | In this paper, we present ControlVideo, a novel method for text-driven video
editing. Leveraging the capabilities of text-to-image diffusion models and
ControlNet, ControlVideo aims to enhance the fidelity and temporal consistency
of videos that align with a given text while preserving the structure of the
source video... | https://huggingface.co/papers/2305.17098 |
2023-05-29 | 2305.16380 | 4 | Scan and Snap: Understanding Training Dynamics and Token Composition in
1-layer Transformer | Transformer architecture has shown impressive performance in multiple
research domains and has become the backbone of many neural network models.
However, there is limited understanding on how it works. In particular, with a
simple predictive loss, how the representation emerges from the gradient
training dynamics rema... | https://huggingface.co/papers/2305.16380 |
2023-05-29 | 2305.17126 | 3 | Large Language Models as Tool Makers | Recent research shows the potential of enhancing the problem-solving ability
of large language models (LLMs) through the use of external tools. However,
prior work along this line depends on the availability of existing tools. In
this work, we take an initial step towards removing this dependency by
proposing a closed-... | https://huggingface.co/papers/2305.17126 |
2023-05-29 | 2305.17066 | 3 | Mindstorms in Natural Language-Based Societies of Mind | Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire
diverse societies of large multimodal neural networks (NNs) that solve problems
by interviewing each other in a "mindstorm." Recent implementations of NN-based
societies of minds consist of large language models (LLMs) and other NN-based
expe... | https://huggingface.co/papers/2305.17066 |
2023-05-29 | 2305.16960 | 3 | Training Socially Aligned Language Models in Simulated Human Society | Social alignment in AI systems aims to ensure that these models behave
according to established societal values. However, unlike humans, who derive
consensus on value judgments through social interaction, current language
models (LMs) are trained to rigidly replicate their training corpus in
isolation, leading to subpa... | https://huggingface.co/papers/2305.16960 |
2023-05-29 | 2305.16381 | 3 | DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion
Models | Learning from human feedback has been shown to improve text-to-image models.
These techniques first learn a reward function that captures what humans care
about in the task and then improve the models based on the learned reward
function. Even though relatively simple approaches (e.g., rejection sampling
based on rewar... | https://huggingface.co/papers/2305.16381 |
2023-05-29 | 2305.16367 | 3 | Role-Play with Large Language Models | As dialogue agents become increasingly human-like in their performance, it is
imperative that we develop effective ways to describe their behaviour in
high-level terms without falling into the trap of anthropomorphism. In this
paper, we foreground the concept of role-play. Casting dialogue agent behaviour
in terms of r... | https://huggingface.co/papers/2305.16367 |
2023-05-29 | 2305.16355 | 3 | PandaGPT: One Model To Instruction-Follow Them All | We present PandaGPT, an approach to emPower large lANguage moDels with visual
and Auditory instruction-following capabilities. Our pilot experiments show
that PandaGPT can perform complex tasks such as detailed image description
generation, writing stories inspired by videos, and answering questions about
audios. More ... | https://huggingface.co/papers/2305.16355 |
2023-05-29 | 2305.16338 | 3 | Think Before You Act: Decision Transformers with Internal Working Memory | Large language model (LLM)-based decision-making agents have shown the
ability to generalize across multiple tasks. However, their performance relies
on massive data and compute. We argue that this inefficiency stems from the
forgetting phenomenon, in which a model memorizes its behaviors in parameters
throughout train... | https://huggingface.co/papers/2305.16338 |
2023-05-29 | 2305.16999 | 2 | Three Towers: Flexible Contrastive Learning with Pretrained Image Models | We introduce Three Towers (3T), a flexible method to improve the contrastive
learning of vision-language models by incorporating pretrained image
classifiers. While contrastive models are usually trained from scratch, LiT
(Zhai et al., 2022) has recently shown performance gains from using pretrained
classifier embeddin... | https://huggingface.co/papers/2305.16999 |
2023-05-29 | 2305.16958 | 2 | MixCE: Training Autoregressive Language Models by Mixing Forward and
Reverse Cross-Entropies | Autoregressive language models are trained by minimizing the cross-entropy of
the model distribution Q relative to the data distribution P -- that is,
minimizing the forward cross-entropy, which is equivalent to maximum likelihood
estimation (MLE). We have observed that models trained in this way may
"over-generalize",... | https://huggingface.co/papers/2305.16958 |
2023-05-29 | 2305.16867 | 2 | Playing repeated games with Large Language Models | Large Language Models (LLMs) are transforming society and permeating into
diverse applications. As a result, LLMs will frequently interact with us and
other agents. It is, therefore, of great societal value to understand how LLMs
behave in interactive social settings. Here, we propose to use behavioral game
theory to s... | https://huggingface.co/papers/2305.16867 |
2023-05-29 | 2305.16843 | 2 | Randomized Positional Encodings Boost Length Generalization of
Transformers | Transformers have impressive generalization capabilities on tasks with a
fixed context length. However, they fail to generalize to sequences of
arbitrary length, even for seemingly simple tasks such as duplicating a string.
Moreover, simply training on longer sequences is inefficient due to the
quadratic computation co... | https://huggingface.co/papers/2305.16843 |
2023-05-29 | 2305.16349 | 2 | Lexinvariant Language Models | Token embeddings, a mapping from discrete lexical symbols to continuous
vectors, are at the heart of any language model (LM). However, lexical symbol
meanings can also be determined and even redefined by their structural role in
a long context. In this paper, we ask: is it possible for a language model to
be performant... | https://huggingface.co/papers/2305.16349 |
2023-05-29 | 2305.16806 | 1 | Do GPTs Produce Less Literal Translations? | Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose
language models capable of addressing many natural language generation or
understanding tasks. On the task of Machine Translation (MT), multiple works
have investigated few-shot prompting mechanisms to elicit better translations
from LLMs. Howev... | https://huggingface.co/papers/2305.16806 |
2023-05-29 | 2305.16765 | 1 | Backpack Language Models | We present Backpacks: a new neural architecture that marries strong modeling
performance with an interface for interpretability and control. Backpacks learn
multiple non-contextual sense vectors for each word in a vocabulary, and
represent a word in a sequence as a context-dependent, non-negative linear
combination of ... | https://huggingface.co/papers/2305.16765 |
2023-05-29 | 2305.16704 | 1 | A Closer Look at In-Context Learning under Distribution Shifts | In-context learning, a capability that enables a model to learn from input
examples on the fly without necessitating weight updates, is a defining
characteristic of large language models. In this work, we follow the setting
proposed in (Garg et al., 2022) to better understand the generality and
limitations of in-contex... | https://huggingface.co/papers/2305.16704 |
2023-05-29 | 2305.16635 | 1 | Impossible Distillation: from Low-Quality Model to High-Quality Dataset
& Model for Summarization and Paraphrasing | We present Impossible Distillation, a novel framework for paraphrasing and
sentence summarization, that distills a high-quality dataset and model from a
low-quality teacher that itself cannot perform these tasks. Unlike prior works
that rely on an extreme-scale teacher model (e.g., GPT3) or task-specific
architecture, ... | https://huggingface.co/papers/2305.16635 |
2023-05-29 | 2305.16411 | 1 | ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image | Recent advancements in text-to-image generation have enabled significant
progress in zero-shot 3D shape generation. This is achieved by score
distillation, a methodology that uses pre-trained text-to-image diffusion
models to optimize the parameters of a 3D neural presentation, e.g. Neural
Radiance Field (NeRF). While ... | https://huggingface.co/papers/2305.16411 |
2023-05-29 | 2305.16334 | 1 | OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities | In most current research, large language models (LLMs) are able to perform
reasoning tasks by generating chains of thought through the guidance of
specific prompts. However, there still exists a significant discrepancy between
their capability in solving complex reasoning problems and that of humans. At
present, most a... | https://huggingface.co/papers/2305.16334 |
2023-05-30 | 2305.18295 | 8 | RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths | Text-to-image generation has recently witnessed remarkable achievements. We
introduce a text-conditional image diffusion model, termed RAPHAEL, to generate
highly artistic images, which accurately portray the text prompts, encompassing
multiple nouns, adjectives, and verbs. This is achieved by stacking tens of
mixture-... | https://huggingface.co/papers/2305.18295 |
2023-05-30 | 2305.17216 | 7 | Generating Images with Multimodal Language Models | We propose a method to fuse frozen text-only large language models (LLMs)
with pre-trained image encoder and decoder models, by mapping between their
embedding spaces. Our model demonstrates a wide suite of multimodal
capabilities: image retrieval, novel image generation, and multimodal dialogue.
Ours is the first appr... | https://huggingface.co/papers/2305.17216 |
2023-05-30 | 2305.18292 | 5 | Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept
Customization of Diffusion Models | Public large-scale text-to-image diffusion models, such as Stable Diffusion,
have gained significant attention from the community. These models can be
easily customized for new concepts using low-rank adaptations (LoRAs). However,
the utilization of multiple concept LoRAs to jointly support multiple
customized concepts... | https://huggingface.co/papers/2305.18292 |
2023-05-30 | 2305.17493 | 5 | Model Dementia: Generated Data Makes Models Forget | Stable Diffusion revolutionised image creation from descriptive text. GPT-2,
GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of
language tasks. ChatGPT introduced such language models to the general public.
It is now clear that large language models (LLMs) are here to stay, and will
bring abou... | https://huggingface.co/papers/2305.17493 |
2023-05-30 | 2305.18752 | 4 | GPT4Tools: Teaching Large Language Model to Use Tools via
Self-instruction | This paper aims to efficiently enable Large Language Models (LLMs) to use
multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have
shown great potential for tool usage through sophisticated prompt engineering.
Nevertheless, these models typically rely on prohibitive computational costs
and publicly ... | https://huggingface.co/papers/2305.18752 |
2023-05-30 | 2305.18274 | 4 | Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning
and Diffusion Priors | We present MindEye, a novel fMRI-to-image approach to retrieve and
reconstruct viewed images from brain activity. Our model comprises two parallel
submodules that are specialized for retrieval (using contrastive learning) and
reconstruction (using a diffusion prior). MindEye can map fMRI brain activity
to any high dime... | https://huggingface.co/papers/2305.18274 |
2023-05-30 | 2305.18247 | 4 | TaleCrafter: Interactive Story Visualization with Multiple Characters | Accurate Story visualization requires several necessary elements, such as
identity consistency across frames, the alignment between plain text and visual
content, and a reasonable layout of objects in images. Most previous works
endeavor to meet these requirements by fitting a text-to-image (T2I) model on a
set of vide... | https://huggingface.co/papers/2305.18247 |
2023-05-30 | 2305.18098 | 4 | BigTrans: Augmenting Large Language Models with Multilingual Translation
Capability over 100 Languages | Large language models (LLMs) demonstrate promising translation performance
among various natural languages. However, many LLMs especially the open-sourced
ones, such as BLOOM and LLaMA, are English-dominant and support only dozens of
natural languages, making the potential of LLMs on language translation less
explored.... | https://huggingface.co/papers/2305.18098 |
2023-05-30 | 2305.18365 | 4 | What indeed can GPT models do in chemistry? A comprehensive benchmark on
eight tasks | Large Language Models (LLMs) with strong abilities in natural language
processing tasks have emerged and have been rapidly applied in various kinds of
areas such as science, finance and software engineering. However, the
capability of LLMs to advance the field of chemistry remains unclear. In this
paper,we establish a ... | https://huggingface.co/papers/2305.18365 |
2023-05-30 | 2305.18583 | 3 | Controllable Text-to-Image Generation with GPT-4 | Current text-to-image generation models often struggle to follow textual
instructions, especially the ones requiring spatial reasoning. On the other
hand, Large Language Models (LLMs), such as GPT-4, have shown remarkable
precision in generating code snippets for sketching out text inputs
graphically, e.g., via TikZ. I... | https://huggingface.co/papers/2305.18583 |
2023-05-30 | 2305.18286 | 3 | Photoswap: Personalized Subject Swapping in Images | In an era where images and visual content dominate our digital landscape, the
ability to manipulate and personalize these images has become a necessity.
Envision seamlessly substituting a tabby cat lounging on a sunlit window sill
in a photograph with your own playful puppy, all while preserving the original
charm and ... | https://huggingface.co/papers/2305.18286 |
2023-05-30 | 2305.18264 | 3 | Gen-L-Video: Multi-Text to Long Video Generation via Temporal
Co-Denoising | Leveraging large-scale image-text datasets and advancements in diffusion
models, text-driven generative models have made remarkable strides in the field
of image generation and editing. This study explores the potential of extending
the text-driven ability to the generation and editing of multi-text conditioned
long vi... | https://huggingface.co/papers/2305.18264 |
2023-05-30 | 2305.17390 | 3 | SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex
Interactive Tasks | We introduce SwiftSage, a novel agent framework inspired by the dual-process
theory of human cognition, designed to excel in action planning for complex
interactive reasoning tasks. SwiftSage integrates the strengths of behavior
cloning and prompting large language models (LLMs) to enhance task completion
performance. ... | https://huggingface.co/papers/2305.17390 |
2023-05-30 | 2305.17333 | 3 | Fine-Tuning Language Models with Just Forward Passes | Fine-tuning language models (LMs) has yielded success on diverse downstream
tasks, but as LMs grow in size, backpropagation requires a prohibitively large
amount of memory. Zeroth-order (ZO) methods can in principle estimate gradients
using only two forward passes but are theorized to be catastrophically slow for
optim... | https://huggingface.co/papers/2305.17333 |
2023-05-30 | 2305.18259 | 2 | GlyphControl: Glyph Conditional Control for Visual Text Generation | Recently, there has been a growing interest in developing diffusion-based
text-to-image generative models capable of generating coherent and well-formed
visual text. In this paper, we propose a novel and efficient approach called
GlyphControl to address this task. Unlike existing methods that rely on
character-aware te... | https://huggingface.co/papers/2305.18259 |
2023-05-30 | 2305.17306 | 2 | Chain-of-Thought Hub: A Continuous Effort to Measure Large Language
Models' Reasoning Performance | As large language models (LLMs) are continuously being developed, their
evaluation becomes increasingly important yet challenging. This work proposes
Chain-of-Thought Hub, an open-source evaluation suite on the multi-step
reasoning capabilities of large language models. We are interested in this
setting for two reasons... | https://huggingface.co/papers/2305.17306 |
2023-05-30 | 2305.17144 | 2 | Ghost in the Minecraft: Generally Capable Agents for Open-World
Enviroments via Large Language Models with Text-based Knowledge and Memory | The captivating realm of Minecraft has attracted substantial research
interest in recent years, serving as a rich platform for developing intelligent
agents capable of functioning in open-world environments. However, the current
research landscape predominantly focuses on specific objectives, such as the
popular "Obtai... | https://huggingface.co/papers/2305.17144 |
2023-05-30 | 2305.17359 | 1 | DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of
GPT-Generated Text | Large language models (LLMs) have notably enhanced the fluency and diversity
of machine-generated text. However, this progress also presents a significant
challenge in detecting the origin of a given text, and current research on
detection methods lags behind the rapid evolution of LLMs. Conventional
training-based met... | https://huggingface.co/papers/2305.17359 |
2023-05-30 | 2305.18231 | 1 | High-Fidelity Image Compression with Score-based Generative Models | Despite the tremendous success of diffusion generative models in
text-to-image generation, replicating this success in the domain of image
compression has proven difficult. In this paper, we demonstrate that diffusion
can significantly improve perceptual quality at a given bit-rate, outperforming
state-of-the-art appro... | https://huggingface.co/papers/2305.18231 |
2023-05-31 | 2305.20030 | 8 | Tree-Ring Watermarks: Fingerprints for Diffusion Images that are
Invisible and Robust | Watermarking the outputs of generative models is a crucial technique for
tracing copyright and preventing potential harm from AI-generated content. In
this paper, we introduce a novel technique called Tree-Ring Watermarking that
robustly fingerprints diffusion model outputs. Unlike existing methods that
perform post-ho... | https://huggingface.co/papers/2305.20030 |
2023-05-31 | 2305.18654 | 7 | Faith and Fate: Limits of Transformers on Compositionality | Transformer large language models (LLMs) have sparked admiration for their
exceptional performance on tasks that demand intricate multi-step reasoning.
Yet, these models simultaneously show failures on surprisingly trivial
problems. This begs the question: Are these errors incidental, or do they
signal more substantial... | https://huggingface.co/papers/2305.18654 |
2023-05-31 | 2305.18766 | 6 | HiFA: High-fidelity Text-to-3D with Advanced Diffusion Guidance | Automatic text-to-3D synthesis has achieved remarkable advancements through
the optimization of 3D models. Existing methods commonly rely on pre-trained
text-to-image generative models, such as diffusion models, providing scores for
2D renderings of Neural Radiance Fields (NeRFs) and being utilized for
optimizing NeRFs... | https://huggingface.co/papers/2305.18766 |
2023-05-31 | 2305.19012 | 4 | StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity
3D Avatar Generation | The recent advancements in image-text diffusion models have stimulated
research interest in large-scale 3D generative models. Nevertheless, the
limited availability of diverse 3D resources presents significant challenges to
learning. In this paper, we present a novel method for generating high-quality,
stylized 3D avat... | https://huggingface.co/papers/2305.19012 |
2023-05-31 | 2305.18802 | 4 | LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus | This paper introduces a new speech dataset called ``LibriTTS-R'' designed for
text-to-speech (TTS) use. It is derived by applying speech restoration to the
LibriTTS corpus, which consists of 585 hours of speech data at 24 kHz sampling
rate from 2,456 speakers and the corresponding texts. The constituent samples
of Libr... | https://huggingface.co/papers/2305.18802 |
2023-05-31 | 2305.18729 | 4 | Real-World Image Variation by Aligning Diffusion Inversion Chain | Recent diffusion model advancements have enabled high-fidelity images to be
generated using text prompts. However, a domain gap exists between generated
images and real-world images, which poses a challenge in generating
high-quality variations of real-world images. Our investigation uncovers that
this domain gap origi... | https://huggingface.co/papers/2305.18729 |
2023-05-31 | 2305.19234 | 3 | Grammar Prompting for Domain-Specific Language Generation with Large
Language Models | Large language models (LLMs) can learn to perform a wide range of natural
language tasks from just a handful of in-context examples. However, for
generating strings from highly structured languages (e.g., semantic parsing to
complex domain-specific languages), it is challenging for the LLM to generalize
from just a few... | https://huggingface.co/papers/2305.19234 |
2023-05-31 | 2305.18565 | 3 | PaLI-X: On Scaling up a Multilingual Vision and Language Model | We present the training recipe and results of scaling up PaLI-X, a
multilingual vision and language model, both in terms of size of the components
and the breadth of its training task mixture. Our model achieves new levels of
performance on a wide-range of varied and complex tasks, including multiple
image-based captio... | https://huggingface.co/papers/2305.18565 |
2023-05-31 | 2305.18474 | 3 | Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation | Large diffusion models have been successful in text-to-audio (T2A) synthesis
tasks, but they often suffer from common issues such as semantic misalignment
and poor temporal consistency due to limited natural language understanding and
data scarcity. Additionally, 2D spatial structures widely used in T2A works
lead to u... | https://huggingface.co/papers/2305.18474 |
2023-05-31 | 2305.19245 | 2 | AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation | This paper presents a method that can quickly adapt dynamic 3D avatars to
arbitrary text descriptions of novel styles. Among existing approaches for
avatar stylization, direct optimization methods can produce excellent results
for arbitrary styles but they are unpleasantly slow. Furthermore, they require
redoing the op... | https://huggingface.co/papers/2305.19245 |
2023-05-31 | 2305.19164 | 2 | LANCE: Stress-testing Visual Models by Generating Language-guided
Counterfactual Images | We propose an automated algorithm to stress-test a trained visual model by
generating language-guided counterfactual test images (LANCE). Our method
leverages recent progress in large language modeling and text-based image
editing to augment an IID test set with a suite of diverse, realistic, and
challenging test image... | https://huggingface.co/papers/2305.19164 |
2023-05-31 | 2305.18415 | 2 | Geometric Algebra Transformers | Problems involving geometric data arise in a variety of fields, including
computer vision, robotics, chemistry, and physics. Such data can take numerous
forms, such as points, direction vectors, planes, or transformations, but to
date there is no single architecture that can be applied to such a wide variety
of geometr... | https://huggingface.co/papers/2305.18415 |
2023-05-31 | 2305.19066 | 1 | Nested Diffusion Processes for Anytime Image Generation | Diffusion models are the current state-of-the-art in image generation,
synthesizing high-quality images by breaking down the generation process into
many fine-grained denoising steps. Despite their good performance, diffusion
models are computationally expensive, requiring many neural function
evaluations (NFEs). In th... | https://huggingface.co/papers/2305.19066 |
2023-05-31 | 2305.18373 | 1 | KAFA: Rethinking Image Ad Understanding with Knowledge-Augmented Feature
Adaptation of Vision-Language Models | Image ad understanding is a crucial task with wide real-world applications.
Although highly challenging with the involvement of diverse atypical scenes,
real-world entities, and reasoning over scene-texts, how to interpret image ads
is relatively under-explored, especially in the era of foundational
vision-language mod... | https://huggingface.co/papers/2305.18373 |
2023-06-01 | 2306.00890 | 11 | LLaVA-Med: Training a Large Language-and-Vision Assistant for
Biomedicine in One Day | Conversational generative AI has demonstrated remarkable promise for
empowering biomedical practitioners, but current investigations focus on
unimodal text. Multimodal conversational AI has seen rapid progress by
leveraging billions of image-text pairs from the public web, but such
general-domain vision-language models... | https://huggingface.co/papers/2306.00890 |
2023-06-01 | 2306.00983 | 7 | StyleDrop: Text-to-Image Generation in Any Style | Pre-trained large text-to-image models synthesize impressive images with an
appropriate use of text prompts. However, ambiguities inherent in natural
language and out-of-distribution effects make it hard to synthesize image
styles, that leverage a specific design pattern, texture or material. In this
paper, we introduc... | https://huggingface.co/papers/2306.00983 |
2023-06-01 | 2305.19452 | 4 | Bigger, Better, Faster: Human-level Atari with human-level efficiency | We introduce a value-based RL agent, which we call BBF, that achieves
super-human performance in the Atari 100K benchmark. BBF relies on scaling the
neural networks used for value estimation, as well as a number of other design
choices that enable this scaling in a sample-efficient manner. We conduct
extensive analyses... | https://huggingface.co/papers/2305.19452 |
2023-06-01 | 2305.20086 | 3 | Understanding and Mitigating Copying in Diffusion Models | Images generated by diffusion models like Stable Diffusion are increasingly
widespread. Recent works and even lawsuits have shown that these models are
prone to replicating their training data, unbeknownst to the user. In this
paper, we first analyze this memorization problem in text-to-image diffusion
models. While it... | https://huggingface.co/papers/2305.20086 |
2023-06-01 | 2305.19370 | 3 | Blockwise Parallel Transformer for Long Context Large Models | Transformers have emerged as the cornerstone of state-of-the-art natural
language processing models, showcasing exceptional performance across a wide
range of AI applications. However, the memory demands posed by the
self-attention mechanism and the large feedforward network in Transformers
limit their ability to handl... | https://huggingface.co/papers/2305.19370 |
2023-06-01 | 2306.00622 | 2 | ReviewerGPT? An Exploratory Study on Using Large Language Models for
Paper Reviewing | Given the rapid ascent of large language models (LLMs), we study the
question: (How) can large language models help in reviewing of scientific
papers or proposals? We first conduct some pilot studies where we find that (i)
GPT-4 outperforms other LLMs (Bard, Vicuna, Koala, Alpaca, LLaMa, Dolly,
OpenAssistant, StableLM)... | https://huggingface.co/papers/2306.00622 |
2023-06-01 | 2305.20088 | 2 | Improving CLIP Training with Language Rewrites | Contrastive Language-Image Pre-training (CLIP) stands as one of the most
effective and scalable methods for training transferable vision models using
paired image and text data. CLIP models are trained using contrastive loss,
which typically relies on data augmentations to prevent overfitting and
shortcuts. However, in... | https://huggingface.co/papers/2305.20088 |
2023-06-01 | 2305.20082 | 2 | Control4D: Dynamic Portrait Editing by Learning 4D GAN from 2D
Diffusion-based Editor | Recent years have witnessed considerable achievements in editing images with
text instructions. When applying these editors to dynamic scene editing, the
new-style scene tends to be temporally inconsistent due to the frame-by-frame
nature of these 2D editors. To tackle this issue, we propose Control4D, a novel
approach... | https://huggingface.co/papers/2305.20082 |
2023-06-01 | 2305.20081 | 2 | Efficient Diffusion Policies for Offline Reinforcement Learning | Offline reinforcement learning (RL) aims to learn optimal policies from
offline datasets, where the parameterization of policies is crucial but often
overlooked. Recently, Diffsuion-QL significantly boosts the performance of
offline RL by representing a policy with a diffusion model, whose success
relies on a parametri... | https://huggingface.co/papers/2305.20081 |
2023-06-01 | 2305.20091 | 1 | Humans in 4D: Reconstructing and Tracking Humans with Transformers | We present an approach to reconstruct humans and track them over time. At the
core of our approach, we propose a fully "transformerized" version of a network
for human mesh recovery. This network, HMR 2.0, advances the state of the art
and shows the capability to analyze unusual poses that have in the past been
difficu... | https://huggingface.co/papers/2305.20091 |
2023-06-01 | 2305.20010 | 1 | Human or Not? A Gamified Approach to the Turing Test | We present "Human or Not?", an online game inspired by the Turing test, that
measures the capability of AI chatbots to mimic humans in dialog, and of humans
to tell bots from other humans. Over the course of a month, the game was played
by over 1.5 million users who engaged in anonymous two-minute chat sessions
with ei... | https://huggingface.co/papers/2305.20010 |
2023-06-01 | 2305.19835 | 1 | Deliberate then Generate: Enhanced Prompting Framework for Text
Generation | Large language models (LLMs) have shown remarkable success across a wide
range of natural language generation tasks, where proper prompt designs make
great impacts. While existing prompting methods are normally restricted to
providing correct information, in this paper, we encourage the model to
deliberate by proposing... | https://huggingface.co/papers/2305.19835 |
2023-06-01 | 2305.19472 | 1 | PlaSma: Making Small Language Models Better Procedural Knowledge Models
for (Counterfactual) Planning | Procedural planning, which entails decomposing a high-level goal into a
sequence of temporally ordered steps, is an important yet intricate task for
machines. It involves integrating common-sense knowledge to reason about
complex contextualized situations that are often counterfactual, e.g.
"scheduling a doctor's appoi... | https://huggingface.co/papers/2305.19472 |
2023-06-02 | 2306.00739 | 20 | SQL-PaLM: Improved Large Language ModelAdaptation for Text-to-SQL | One impressive emergent capability of large language models (LLMs) is
generation of code, including Structured Query Language (SQL) for databases.
For the task of converting natural language text to SQL queries, Text-to-SQL,
adaptation of LLMs is of paramount importance, both in in-context learning and
fine-tuning sett... | https://huggingface.co/papers/2306.00739 |
2023-06-02 | 2306.00980 | 15 | SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two
Seconds | Text-to-image diffusion models can create stunning images from natural
language descriptions that rival the work of professional artists and
photographers. However, these models are large, with complex network
architectures and tens of denoising iterations, making them computationally
expensive and slow to run. As a re... | https://huggingface.co/papers/2306.00980 |
2023-06-02 | 2306.00637 | 12 | Wuerstchen: Efficient Pretraining of Text-to-Image Models | We introduce W\"urstchen, a novel architecture for text-to-image synthesis
that combines competitive performance with unprecedented cost-effectiveness for
large-scale text-to-image diffusion models. A key contribution of our work is
to develop a latent diffusion technique in which we learn a detailed but
extremely comp... | https://huggingface.co/papers/2306.00637 |
2023-06-02 | 2306.00378 | 7 | Example-based Motion Synthesis via Generative Motion Matching | We present GenMM, a generative model that "mines" as many diverse motions as
possible from a single or few example sequences. In stark contrast to existing
data-driven methods, which typically require long offline training time, are
prone to visual artifacts, and tend to fail on large and complex skeletons,
GenMM inher... | https://huggingface.co/papers/2306.00378 |
2023-06-02 | 2306.00238 | 6 | Bytes Are All You Need: Transformers Operating Directly On File Bytes | Modern deep learning approaches usually transform inputs into a
modality-specific form. For example, the most common deep learning approach to
image classification involves decoding image file bytes into an RGB tensor
which is passed into a neural network. Instead, we investigate performing
classification directly on f... | https://huggingface.co/papers/2306.00238 |
2023-06-02 | 2306.00966 | 5 | The Hidden Language of Diffusion Models | Text-to-image diffusion models have demonstrated an unparalleled ability to
generate high-quality, diverse images from a textual concept (e.g., "a doctor",
"love"). However, the internal process of mapping text to a rich visual
representation remains an enigma. In this work, we tackle the challenge of
understanding con... | https://huggingface.co/papers/2306.00966 |
2023-06-02 | 2306.00943 | 5 | Make-Your-Video: Customized Video Generation Using Textual and
Structural Guidance | Creating a vivid video from the event or scenario in our imagination is a
truly fascinating experience. Recent advancements in text-to-video synthesis
have unveiled the potential to achieve this with prompts only. While text is
convenient in conveying the overall scene context, it may be insufficient to
control precise... | https://huggingface.co/papers/2306.00943 |
2023-06-02 | 2306.00984 | 4 | StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual
Representation Learners | We investigate the potential of learning visual representations using
synthetic images generated by text-to-image models. This is a natural question
in the light of the excellent performance of such models in generating
high-quality images. We consider specifically the Stable Diffusion, one of the
leading open source t... | https://huggingface.co/papers/2306.00984 |
2023-06-02 | 2306.00971 | 4 | ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image
Generation | Personalized text-to-image generation using diffusion models has recently
been proposed and attracted lots of attention. Given a handful of images
containing a novel concept (e.g., a unique toy), we aim to tune the generative
model to capture fine visual details of the novel concept and generate
photorealistic images f... | https://huggingface.co/papers/2306.00971 |
2023-06-02 | 2306.00107 | 4 | MERT: Acoustic Music Understanding Model with Large-Scale
Self-supervised Training | Self-supervised learning (SSL) has recently emerged as a promising paradigm
for training generalisable models on large-scale data in the fields of vision,
text, and speech. Although SSL has been proven effective in speech and audio,
its application to music audio has yet to be thoroughly explored. This is
primarily due... | https://huggingface.co/papers/2306.00107 |
2023-06-02 | 2306.00926 | 3 | Inserting Anybody in Diffusion Models via Celeb Basis | Exquisite demand exists for customizing the pretrained large text-to-image
model, e.g., Stable Diffusion, to generate innovative concepts, such
as the users themselves. However, the newly-added concept from previous
customization methods often shows weaker combination abilities than the
original ones even given several... | https://huggingface.co/papers/2306.00926 |
2023-06-02 | 2306.00986 | 2 | Diffusion Self-Guidance for Controllable Image Generation | Large-scale generative models are capable of producing high-quality images
from detailed text descriptions. However, many aspects of an image are
difficult or impossible to convey through text. We introduce self-guidance, a
method that provides greater control over generated images by guiding the
internal representatio... | https://huggingface.co/papers/2306.00986 |
2023-06-02 | 2306.00802 | 2 | Birth of a Transformer: A Memory Viewpoint | Large language models based on transformers have achieved great empirical
successes. However, as they are deployed more widely, there is a growing need
to better understand their internal mechanisms in order to make them more
reliable. These models appear to store vast amounts of knowledge from their
training data, and... | https://huggingface.co/papers/2306.00802 |
2023-06-02 | 2306.00110 | 2 | MuseCoco: Generating Symbolic Music from Text | Generating music from text descriptions is a user-friendly mode since the
text is a relatively easy interface for user engagement. While some approaches
utilize texts to control music audio generation, editing musical elements in
generated audio is challenging for users. In contrast, symbolic music offers
ease of editi... | https://huggingface.co/papers/2306.00110 |
2023-06-02 | 2306.00029 | 2 | CodeTF: One-stop Transformer Library for State-of-the-art Code LLM | Code intelligence plays a key role in transforming modern software
engineering. Recently, deep learning-based models, especially Transformer-based
large language models (LLMs), have demonstrated remarkable potential in
tackling these tasks by leveraging massive open-source code data and
programming language features. H... | https://huggingface.co/papers/2306.00029 |
2023-06-02 | 2306.00964 | 1 | Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image
Generation | Text-conditional diffusion models are able to generate high-fidelity images
with diverse contents. However, linguistic representations frequently exhibit
ambiguous descriptions of the envisioned objective imagery, requiring the
incorporation of additional control signals to bolster the efficacy of
text-guided diffusion... | https://huggingface.co/papers/2306.00964 |
2023-06-02 | 2306.00956 | 1 | The ObjectFolder Benchmark: Multisensory Learning with Neural and Real
Objects | We introduce the ObjectFolder Benchmark, a benchmark suite of 10 tasks for
multisensory object-centric learning, centered around object recognition,
reconstruction, and manipulation with sight, sound, and touch. We also
introduce the ObjectFolder Real dataset, including the multisensory
measurements for 100 real-world ... | https://huggingface.co/papers/2306.00956 |
2023-06-02 | 2306.00148 | 1 | SafeDiffuser: Safe Planning with Diffusion Probabilistic Models | Diffusion model-based approaches have shown promise in data-driven planning,
but there are no safety guarantees, thus making it hard to be applied for
safety-critical applications. To address these challenges, we propose a new
method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy
specifications ... | https://huggingface.co/papers/2306.00148 |
2023-06-02 | 2306.00008 | 1 | Brainformers: Trading Simplicity for Efficiency | Transformers are central to recent successes in natural language processing
and computer vision. Transformers have a mostly uniform backbone where layers
alternate between feed-forward and self-attention in order to build a deep
network. Here we investigate this design choice and find that more complex
blocks that have... | https://huggingface.co/papers/2306.00008 |
2023-06-05 | 2306.02707 | 47 | Orca: Progressive Learning from Complex Explanation Traces of GPT-4 | Recent research has focused on enhancing the capability of smaller models
through imitation learning, drawing on the outputs generated by large
foundation models (LFMs). A number of issues impact the quality of these
models, ranging from limited imitation signals from shallow LFM outputs; small
scale homogeneous traini... | https://huggingface.co/papers/2306.02707 |
2023-06-05 | 2306.01116 | 38 | The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora
with Web Data, and Web Data Only | Large language models are commonly trained on a mixture of filtered web data
and curated high-quality corpora, such as social media conversations, books, or
technical papers. This curation process is believed to be necessary to produce
performant models with broad zero-shot generalization abilities. However, as
larger ... | https://huggingface.co/papers/2306.01116 |
2023-06-05 | 2306.01567 | 8 | Segment Anything in High Quality | The recent Segment Anything Model (SAM) represents a big leap in scaling up
segmentation models, allowing for powerful zero-shot capabilities and flexible
prompting. Despite being trained with 1.1 billion masks, SAM's mask prediction
quality falls short in many cases, particularly when dealing with objects that
have in... | https://huggingface.co/papers/2306.01567 |
2023-06-05 | 2306.02561 | 6 | LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and
Generative Fusion | We present LLM-Blender, an ensembling framework designed to attain
consistently superior performance by leveraging the diverse strengths of
multiple open-source large language models (LLMs). Our framework consists of
two modules: PairRanker and GenFuser, addressing the observation that optimal
LLMs for different exampl... | https://huggingface.co/papers/2306.02561 |
2023-06-05 | 2306.03082 | 5 | InstructZero: Efficient Instruction Optimization for Black-Box Large
Language Models | Large language models~(LLMs) are instruction followers, but it can be
challenging to find the best instruction for different situations, especially
for black-box LLMs on which backpropagation is forbidden. Instead of directly
optimizing the discrete instruction, we optimize a low-dimensional soft prompt
applied to an o... | https://huggingface.co/papers/2306.03082 |
2023-06-05 | 2306.01693 | 3 | Fine-Grained Human Feedback Gives Better Rewards for Language Model
Training | Language models (LMs) often exhibit undesirable text generation behaviors,
including generating false, toxic, or irrelevant outputs. Reinforcement
learning from human feedback (RLHF) - where human preference judgments on LM
outputs are transformed into a learning signal - has recently shown promise in
addressing these ... | https://huggingface.co/papers/2306.01693 |
2023-06-05 | 2306.01684 | 3 | Harnessing large-language models to generate private synthetic text | Differentially private (DP) training methods like DP-SGD can protect
sensitive training data by ensuring that ML models will not reveal private
information. An alternative approach, which this paper studies, is to use a
sensitive dataset to generate a new synthetic dataset which is differentially
private with respect t... | https://huggingface.co/papers/2306.01684 |
2023-06-05 | 2306.03024 | 2 | PokemonChat: Auditing ChatGPT for Pokémon Universe Knowledge | The recently released ChatGPT model demonstrates unprecedented capabilities
in zero-shot question-answering. In this work, we probe ChatGPT for its
conversational understanding and introduce a conversational framework
(protocol) that can be adopted in future studies. The Pok\'emon universe serves
as an ideal testing gr... | https://huggingface.co/papers/2306.03024 |
2023-06-05 | 2306.01694 | 2 | Evaluating Language Models for Mathematics through Interactions | The standard methodology of evaluating large language models (LLMs) based on
static pairs of inputs and outputs is insufficient for developing assistants:
this kind of assessments fails to take into account the essential interactive
element in their deployment, and therefore limits how we understand language
model capa... | https://huggingface.co/papers/2306.01694 |
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