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Collections including paper arxiv:2406.16338

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

BLINK: Multimodal Large Language Models Can See but Not Perceive

Paper • 2404.12390 • Published Apr 18 • 24
SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension

Paper • 2404.16790 • Published Apr 25 • 7
Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots

Paper • 2405.07990 • Published May 13 • 16
MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding

Paper • 2406.09411 • Published Jun 13 • 18

video llm works

PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning

Paper • 2404.16994 • Published Apr 25 • 35
VideoMamba: State Space Model for Efficient Video Understanding

Paper • 2403.06977 • Published Mar 11 • 27
VideoAgent: Long-form Video Understanding with Large Language Model as Agent

Paper • 2403.10517 • Published Mar 15 • 31
Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding

Paper • 2403.09626 • Published Mar 14 • 13

video understanding

VideoPrism: A Foundational Visual Encoder for Video Understanding

Paper • 2402.13217 • Published Feb 20 • 21
Sora Generates Videos with Stunning Geometrical Consistency

Paper • 2402.17403 • Published Feb 27 • 16
Video as the New Language for Real-World Decision Making

Paper • 2402.17139 • Published Feb 27 • 18
VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models

Paper • 2406.16338 • Published Jun 24 • 25

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 • 38
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7 • 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

Vision-Language

SILC: Improving Vision Language Pretraining with Self-Distillation

Paper • 2310.13355 • Published Oct 20, 2023 • 6
Woodpecker: Hallucination Correction for Multimodal Large Language Models

Paper • 2310.16045 • Published Oct 24, 2023 • 14
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Paper • 2201.12086 • Published Jan 28, 2022 • 3
ImageNetVC: Zero-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories

Paper • 2305.15028 • Published May 24, 2023 • 1

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