VideoRefer 套件：透過 Video LLM 推進時空物件理解
VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM
摘要
Summary
AI-Generated Summary
Paper Overview
The paper introduces the VideoRefer Suite, which enhances spatial-temporal object understanding in videos through a dataset, model, and benchmark. It significantly improves regional video understanding and general video comprehension, outperforming existing methods.
Core Contribution
- Introduction of VideoRefer Suite for fine-grained spatial-temporal object understanding.
- Development of VideoRefer model with a spatial-temporal object encoder.
- Creation of VideoRefer-700K dataset using a multi-agent data engine.
- Establishment of VideoRefer-Bench for evaluating spatial-temporal understanding.
- Advancement in video object referring, relationship analysis, and retrieval tasks.
Research Context
The study addresses the limitations of Video Large Language Models (Video LLMs) in capturing fine-grained spatial and temporal details, focusing on enhancing regional video understanding and general video comprehension.
Keywords
VideoRefer Suite, Video LLMs, spatial-temporal object understanding, VideoRefer-700K dataset, VideoRefer model, VideoRefer-Bench, fine-grained video comprehension
Background
The research aims to improve video understanding by overcoming the limitations of Video LLMs in capturing detailed spatial and temporal information. It introduces the VideoRefer Suite to enhance regional video comprehension and general video understanding.
Research Gap
Existing Video LLMs struggle with fine-grained spatial and temporal details, necessitating the development of VideoRefer for improved object-level video instruction comprehension.
Technical Challenges
Challenges include capturing precise regional and sequential representations, integrating object embeddings with temporal cues, and evaluating spatial-temporal understanding capabilities accurately.
Prior Approaches
Previous methods lacked the capability to address fine-grained spatial-temporal object understanding, prompting the need for the VideoRefer Suite with its dataset, model, and benchmark.
Methodology
The methodology involves creating the VideoRefer-700K dataset, developing the VideoRefer model with a spatial-temporal object encoder, and implementing the VideoRefer-Bench for evaluation.
Theoretical Foundation
The study is based on enhancing spatial-temporal object understanding in videos through a spatial-temporal object encoder and object-level representations across video scenes.
Technical Architecture
The VideoRefer model incorporates a spatial-temporal object encoder (REnc) supporting single-frame and multi-frame modes for capturing spatial features and aggregating temporal information.
Implementation Details
The methodology includes a hybrid training strategy with the siglip-so400m-patch14-384 vision encoder and Qwen-2 LLM, involving pre-training and tuning stages for model development.
Innovation Points
Innovations include the Spatial Token Extractor, Temporal Token Merge Module, and the VideoRefer-Bench for evaluating model performance in various video comprehension tasks.
Experimental Validation
The experimental validation includes setting up configurations, defining metrics, presenting results, and conducting comparative analyses to demonstrate the effectiveness of VideoRefer.
Setup
Exact configurations involve using the siglip-so400m-patch14-384 vision encoder and Qwen-2 LLM for training the VideoRefer model on the VideoRefer-700K dataset.
Metrics
Metrics include evaluation criteria like Subject Correspondence, Appearance Description, Temporal Description, and Hallucination Detection for assessing model performance.
Results
Quantitative and qualitative findings show VideoRefer outperforming previous methods in regional-temporal video understanding tasks on VideoRefer-BenchD and VideoRefer-BenchQ.
Comparative Analysis
Comparisons with existing models demonstrate VideoRefer's superiority in basic questions, relationship questions, reasoning questions, and future predictions, showcasing enhanced video comprehension capabilities.
Impact and Implications
The study's impact lies in advancing spatial-temporal understanding in video comprehension, improving fine-grained regional video understanding, and enhancing general video comprehension.
Key Findings
VideoRefer excels in subject correspondence, appearance description, temporal description, and hallucination detection, demonstrating superior performance in both single-frame and multi-frame modes.
Limitations
The system lacks grounding abilities for identifying and associating objects within dynamic contexts, indicating a potential area for improvement.
Future Directions
Future work aims to integrate grounding abilities into the framework to enhance practical applicability and further improve video comprehension tasks.
Practical Significance
VideoRefer enables basic video object referring, complex relationship analysis, and object retrieval tasks, enhancing user interactivity and advancing video understanding capabilities.