Papers MME
“MME” 태그가 달린 논문 95편 · 필터 해제
High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning
State-of-the-art large multi-modal models (LMMs) face challenges when processing high-resolution images, as these inputs are converted into enormous visual tokens, many of which are irrelevant to the downstream task. In …
MMEReinforcement Learning (RL)Visual GroundingVisual ReasoningFlash-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 UnderstandingLanguage-Vision Planner and Executor for Text-to-Visual Reasoning
The advancement in large language models (LLMs) and large vision models has fueled the rapid progress in multi-modal visual-text reasoning capabilities. However, existing vision-language models (VLMs) to date suffer from…
In-Context LearningMMETask PlanningVisual ReasoningDynTok: 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 UnderstandingFast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering
Multimodal large language models (MLLMs) still struggle with complex reasoning tasks in Visual Question Answering (VQA). While current methods have advanced by incorporating visual prompts, our study uncovers critical li…
AllMMEQuestion AnsweringVisual Question Answering+1SiLVR: 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+2EvoMoE: Expert Evolution in Mixture of Experts for Multimodal Large Language Models
Recent advancements have shown that the Mixture of Experts (MoE) approach significantly enhances the capacity of large language models (LLMs) and improves performance on downstream tasks. Building on these promising resu…
Mixture-of-ExpertsMMETextVQAMME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs
Logical reasoning is a fundamental aspect of human intelligence and an essential capability for multimodal large language models (MLLMs). Despite the significant advancement in multimodal reasoning, existing benchmarks f…
Logical ReasoningMMEMultimodal ReasoningEnhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models
Large Vision-Language Models (LVLMs) usually generate texts which satisfy context coherence but don't match the visual input. Such a hallucination issue hinders LVLMs' applicability in the real world. The key to solving …
HallucinationMMEText GenerationVideoEval-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 UnderstandingMitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models
Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucinations-generating content that is inconsistent with the input image. Existing training-free hallucination mi…
HallucinationMMEVISTA: Enhancing Vision-Text Alignment in MLLMs via Cross-Modal Mutual Information Maximization
Current multimodal large language models (MLLMs) face a critical challenge in modality alignment, often exhibiting a bias towards textual information at the expense of other modalities like vision. This paper conducts a …
cross-modal alignmentMMEVisual Instruction Tuning with Chain of Region-of-Interest
High-resolution (HR) images are pivotal for enhancing the recognition and understanding capabilities of multimodal large language models (MLLMs). However, directly increasing image resolution can significantly escalate c…
MMEFRAG: 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 UnderstandingMME-Unify: A Comprehensive Benchmark for Unified Multimodal Understanding and Generation Models
Existing MLLM benchmarks face significant challenges in evaluating Unified MLLMs (U-MLLMs) due to: 1) lack of standardized benchmarks for traditional tasks, leading to inconsistent comparisons; 2) absence of benchmarks f…
BenchmarkingImage GenerationMMEMultimodal Reasoning