Evaluation Framework for Video Large Language Models: Standardized Assessment System and Multi-Dimensional Capability Analysis

This article introduces the video-llm-evaluation-harness project on GitHub. Addressing the lack of unified standards for video LLM evaluation, it provides a standardized, reproducible, multi-dimensional assessment system that supports scenarios such as model R&D debugging, selection comparison, and academic benchmark testing, serving as an important infrastructure for the video LLM field.

With the rapid development of multi-modal LLMs like GPT-4V, Gemini, and Qwen-VL, video understanding has become a cutting-edge focus. However, different teams use different test datasets, metrics, and experimental setups, making it difficult to compare results horizontally. This framework aims to resolve this dilemma by providing a comprehensive and reproducible evaluation solution.

1. Standardization and Reproducibility: Unify configuration formats, random seeds, and preprocessing processes to ensure consistent results; 2. Modularity and Extensibility: Support rapid addition of new models or evaluation tasks; 3. Multi-dimensional Capability Coverage: Finely evaluate subtasks like temporal reasoning and action recognition to provide a comprehensive capability profile.

Covers five major dimensions: 1. Temporal understanding (sorting, localization, reasoning); 2. Action recognition and classification (single/multi-action recognition, localization); 3. Spatial-temporal joint reasoning (trajectory prediction, interaction recognition, scene change detection); 4. Long video understanding (cross-segment integration, summary generation, question answering); 5. Multi-modal alignment and fusion (vision-language alignment, instruction following, hallucination detection).

1. Dataset management: Supports mainstream datasets like MSR-VTT and ActivityNet, providing unified interfaces and custom access; 2. Model interface abstraction: Compatible with multiple architectures such as CLIP-based, VideoMAE, and end-to-end; 3. Evaluation metric system: Covers multiple types of metrics including classification (accuracy/F1), generation (BLEU/ROUGE), and localization (IoU/mAP); 4. Distributed evaluation: Multi-GPU parallel acceleration for large-scale testing.

1. R&D debugging: Finely diagnose model weaknesses to guide improvements; 2. Selection comparison: Objective benchmarks help balance model capabilities and costs; 3. Academic publication: Enhance the credibility and comparability of results.

Limitations: Existing datasets have distribution biases; Future directions: Dataset debiasing, dynamic evaluation (continuous learning), multi-language cross-cultural evaluation, real-time evaluation (inference latency).

This framework is an important infrastructure in the video LLM field, advocating a comprehensive, fine-grained, and reproducible assessment methodology. It is recommended that researchers/practitioners use it as a standard tool to promote the healthy development of the field. The framework will continue to evolve in the future, covering more emerging capability dimensions.