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Collections including paper arxiv:2501.05453

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6, 2024 • 28
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6, 2024 • 13
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7, 2024 • 44
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7, 2024 • 23

An Empirical Study of Autoregressive Pre-training from Videos

Paper • 2501.05453 • Published Jan 9 • 42

Multimodal-Grounding

Migician: Revealing the Magic of Free-Form Multi-Image Grounding in Multimodal Large Language Models

Paper • 2501.05767 • Published Jan 10 • 30
An Empirical Study of Autoregressive Pre-training from Videos

Paper • 2501.05453 • Published Jan 9 • 42
Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos

Paper • 2501.04001 • Published Jan 7 • 47

Foundation Models Empirical Analysis

An Empirical Study of Autoregressive Pre-training from Videos

Paper • 2501.05453 • Published Jan 9 • 42
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs

Paper • 2501.06186 • Published Jan 10 • 66
Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains

Paper • 2501.05707 • Published Jan 10 • 20
The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?

Paper • 2502.17535 • Published Feb 24 • 8

An Empirical Study of Autoregressive Pre-training from Videos

Paper • 2501.05453 • Published Jan 9 • 42

An Empirical Study of Autoregressive Pre-training from Videos

Paper • 2501.05453 • Published Jan 9 • 42
Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k

Paper • 2503.09642 • Published Mar 12 • 17

Image-Video General Tasks

Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos

Paper • 2501.04001 • Published Jan 7 • 47
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

Paper • 2501.03895 • Published Jan 7 • 53
An Empirical Study of Autoregressive Pre-training from Videos

Paper • 2501.05453 • Published Jan 9 • 42
MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training

Paper • 2501.07556 • Published Jan 13 • 5

2025 LLM Papers on Hugging Face with Japanese Memos

MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models

Paper • 2501.02955 • Published Jan 6 • 45
2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining

Paper • 2501.00958 • Published Jan 1 • 107
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding

Paper • 2501.12380 • Published Jan 21 • 86
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos

Paper • 2501.09781 • Published Jan 16 • 29

Video Creation by Demonstration

Paper • 2412.09551 • Published Dec 12, 2024 • 9
DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation

Paper • 2412.07589 • Published Dec 10, 2024 • 49
Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation

Paper • 2412.06531 • Published Dec 9, 2024 • 72
APOLLO: SGD-like Memory, AdamW-level Performance

Paper • 2412.05270 • Published Dec 6, 2024 • 38

How to Synthesize Text Data without Model Collapse?

Paper • 2412.14689 • Published Dec 19, 2024 • 53
SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator

Paper • 2412.12094 • Published Dec 16, 2024 • 11
StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models

Paper • 2306.07691 • Published Jun 13, 2023 • 8
iSTFTNet: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating Inverse Short-Time Fourier Transform

Paper • 2203.02395 • Published Mar 4, 2022

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