Papers Video MME
“Video MME” 태그가 달린 논문 26편 · 필터 해제
Flash-VStream: Efficient Real-Time Understanding for Long Video Streams
Benefiting from the advances in large language models and cross-modal alignment, existing multimodal large language models have achieved prominent performance in image and short video understanding. However, the understa…
cross-modal alignmentEgoSchemaMMEMVBench+2Q-Frame: Query-aware Frame Selection and Multi-Resolution Adaptation for Video-LLMs
Multimodal Large Language Models (MLLMs) have demonstrated significant success in visual understanding tasks. However, challenges persist in adapting these models for video comprehension due to the large volume of data a…
MMEVideo MMEVideo UnderstandingVideoDeepResearch: Long Video Understanding With Agentic Tool Using
Long video understanding (LVU) presents a significant challenge for current multi-modal large language models (MLLMs) due to the task's inherent complexity and context window constraint. It is widely assumed that address…
MMEVideo MMEVideo UnderstandingDynTok: Dynamic Compression of Visual Tokens for Efficient and Effective Video Understanding
Typical video modeling methods, such as LLava, represent videos as sequences of visual tokens, which are then processed by the LLM backbone for effective video understanding. However, this approach leads to a massive num…
MMEVideo MMEVideo UnderstandingSiLVR: A Simple Language-based Video Reasoning Framework
Recent advances in test-time optimization have led to remarkable reasoning capabilities in Large Language Models (LLMs), enabling them to solve highly complex problems in math and coding. However, the reasoning capabilit…
MathMMEMMLUToken Reduction+2VideoEval-Pro: Robust and Realistic Long Video Understanding Evaluation
Large multimodal models (LMMs) have recently emerged as a powerful tool for long video understanding (LVU), prompting the development of standardized LVU benchmarks to evaluate their performance. However, our investigati…
MMEMultiple-choiceVideo MMEVideo UnderstandingFRAG: Frame Selection Augmented Generation for Long Video and Long Document Understanding
There has been impressive progress in Large Multimodal Models (LMMs). Recent works extend these models to long inputs, including multi-page documents and long videos. However, the model size and performance of these long…
document understandingMMEVideo MMETimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming Videos
The rapid growth of online video platforms, particularly live streaming services, has created an urgent need for real-time video understanding systems. These systems must process continuous video streams and respond to u…
MMEVideo MMEVideo UnderstandingEagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
We introduce Eagle 2.5, a family of frontier vision-language models (VLMs) for long-context multimodal learning. Our work addresses the challenges in long video comprehension and high-resolution image understanding, intr…
MMEVideo MMEVideo UnderstandingAn LMM for Efficient Video Understanding via Reinforced Compression of Video Cubes
Large Multimodal Models (LMMs) uniformly perceive video frames, creating computational inefficiency for videos with inherently varying temporal information density. This paper present \textbf{Quicksviewer}, an LMM with n…
MMEVideo MMEVideo UnderstandingSpaceR: Reinforcing MLLMs in Video Spatial Reasoning
Video spatial reasoning, which involves inferring the underlying spatial structure from observed video frames, poses a significant challenge for existing Multimodal Large Language Models (MLLMs). This limitation stems pr…
MMESpatial ReasoningVideo MMEVideo UnderstandingBOLT: Boost Large Vision-Language Model Without Training for Long-form Video Understanding
Large video-language models (VLMs) have demonstrated promising progress in various video understanding tasks. However, their effectiveness in long-form video analysis is constrained by limited context windows. Traditiona…
FormLanguage ModelingLanguage ModellingMME+2Improving LLM Video Understanding with 16 Frames Per Second
Human vision is dynamic and continuous. However, in video understanding with multimodal large language models (LLMs), existing methods primarily rely on static features extracted from images sampled at a fixed low frame …
MMEVideo MMEVideo UnderstandingLogic-in-Frames: Dynamic Keyframe Search via Visual Semantic-Logical Verification for Long Video Understanding
Understanding long video content is a complex endeavor that often relies on densely sampled frame captions or end-to-end feature selectors, yet these techniques commonly overlook the logical relationships between textual…
AttributeMMEQuestion AnsweringVideo MME+2QuoTA: Query-oriented Token Assignment via CoT Query Decouple for Long Video Comprehension
Recent advances in long video understanding typically mitigate visual redundancy through visual token pruning based on attention distribution. However, while existing methods employ post-hoc low-response token pruning in…
AutoMLDecoderMMEVideo MME+1Temporal Preference Optimization for Long-Form Video Understanding
Despite significant advancements in video large multimodal models (video-LMMs), achieving effective temporal grounding in long-form videos remains a challenge for existing models. To address this limitation, we propose T…
FormMMEVideo MMEVideo UnderstandingApollo: An Exploration of Video Understanding in Large Multimodal Models
Despite the rapid integration of video perception capabilities into Large Multimodal Models (LMMs), the underlying mechanisms driving their video understanding remain poorly understood. Consequently, many design decision…
MMEVideo MMEVideo UnderstandingLyra: An Efficient and Speech-Centric Framework for Omni-Cognition
As Multi-modal Large Language Models (MLLMs) evolve, expanding beyond single-domain capabilities is essential to meet the demands for more versatile and efficient AI. However, previous omni-models have insufficiently exp…
EgoSchemaMMEMM-Vet+5SAVEn-Vid: Synergistic Audio-Visual Integration for Enhanced Understanding in Long Video Context
Endeavors have been made to explore Large Language Models for video analysis (Video-LLMs), particularly in understanding and interpreting long videos. However, existing Video-LLMs still face challenges in effectively int…
Large Language ModelMMEVideo MMEVideo UnderstandingVideo-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension
Existing large video-language models (LVLMs) struggle to comprehend long videos correctly due to limited context. To address this problem, fine-tuning long-context LVLMs and employing GPT-based agents have emerged as pro…
GPUMMEobject-detectionObject Detection+9