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Collections including paper arxiv:2405.20340

MotionLLM: Understanding Human Behaviors from Human Motions and Videos

Paper • 2405.20340 • Published May 30 • 19
MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model

Paper • 2404.19759 • Published Apr 30 • 24
Seamless Human Motion Composition with Blended Positional Encodings

Paper • 2402.15509 • Published Feb 23 • 14
Generating Fine-Grained Human Motions Using ChatGPT-Refined Descriptions

Paper • 2312.02772 • Published Dec 5, 2023 • 6

mDPO: Conditional Preference Optimization for Multimodal Large Language Models

Paper • 2406.11839 • Published Jun 17 • 36
Pandora: Towards General World Model with Natural Language Actions and Video States

Paper • 2406.09455 • Published Jun 12 • 14
WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17 • 14
In-Context Editing: Learning Knowledge from Self-Induced Distributions

Paper • 2406.11194 • Published Jun 17 • 15

Video Understanding

Vript: A Video Is Worth Thousands of Words

Paper • 2406.06040 • Published Jun 10 • 22
ShareGPT4Video: Improving Video Understanding and Generation with Better Captions

Paper • 2406.04325 • Published Jun 6 • 71
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Paper • 2406.01574 • Published Jun 3 • 42
Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Paper • 2405.21075 • Published May 31 • 18

MotionLLM: Understanding Human Behaviors from Human Motions and Videos

Paper • 2405.20340 • Published May 30 • 19
Spectrally Pruned Gaussian Fields with Neural Compensation

Paper • 2405.00676 • Published May 1 • 8
Paint by Inpaint: Learning to Add Image Objects by Removing Them First

Paper • 2404.18212 • Published Apr 28 • 27
LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report

Paper • 2405.00732 • Published Apr 29 • 118

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iVideoGPT: Interactive VideoGPTs are Scalable World Models

Paper • 2405.15223 • Published May 24 • 11
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models

Paper • 2405.15574 • Published May 24 • 52
An Introduction to Vision-Language Modeling

Paper • 2405.17247 • Published May 27 • 84
Matryoshka Multimodal Models

Paper • 2405.17430 • Published May 27 • 30

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

Paper • 2402.04252 • Published Feb 6 • 25
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6 • 12
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7 • 36
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7 • 19

motion generation

Scaling Up Dynamic Human-Scene Interaction Modeling

Paper • 2403.08629 • Published Mar 13 • 14
Motion Mamba: Efficient and Long Sequence Motion Generation with Hierarchical and Bidirectional Selective SSM

Paper • 2403.07487 • Published Mar 12 • 13
Seamless Human Motion Composition with Blended Positional Encodings

Paper • 2402.15509 • Published Feb 23 • 14
MotionLLM: Understanding Human Behaviors from Human Motions and Videos

Paper • 2405.20340 • Published May 30 • 19

PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models

Paper • 2402.08714 • Published Feb 13 • 10
Data Engineering for Scaling Language Models to 128K Context

Paper • 2402.10171 • Published Feb 15 • 21
RLVF: Learning from Verbal Feedback without Overgeneralization

Paper • 2402.10893 • Published Feb 16 • 10
Coercing LLMs to do and reveal (almost) anything

Paper • 2402.14020 • Published Feb 21 • 12

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