REVIEW 6 cited by
MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The advent of large vision-language models (LVLMs) has spurred research into their applications in multi-modal contexts, particularly in video understanding. Traditional VideoQA benchmarks, despite providing quantitative metrics, often fail to encompass the full spectrum of video content and inadequately assess models' temporal comprehension. To address these limitations, we introduce MMBench-Video, a quantitative benchmark designed to rigorously evaluate LVLMs' proficiency in video understanding. MMBench-Video incorporates lengthy videos from YouTube and employs free-form questions, mirroring practical use cases. The benchmark is meticulously crafted to probe the models' temporal reasoning skills, with all questions human-annotated according to a carefully constructed ability taxonomy. We employ GPT-4 for automated assessment, demonstrating superior accuracy and robustness over earlier LLM-based evaluations. Utilizing MMBench-Video, we have conducted comprehensive evaluations that include both proprietary and open-source LVLMs for images and videos. MMBench-Video stands as a valuable resource for the research community, facilitating improved evaluation of LVLMs and catalyzing progress in the field of video understanding. The evalutation code of MMBench-Video will be integrated into VLMEvalKit: https://github.com/open-compass/VLMEvalKit.
Forward citations
Cited by 6 Pith papers
-
Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding
MF2 evaluates long-movie understanding by asking models to classify fact/fib claim pairs; the best model trails humans by 23.5 points in pairwise accuracy.
-
Efficient Frame Selection for Long Videos at Test Time with Attention-Based MLLM Selectors
Attention maps from a small MLLM can serve as a training-free, query-conditioned frame selector, improving long-video QA accuracy under fixed frame budgets.
-
Step-Level Visual Grounding Faithfulness Predicts Out-of-Distribution Generalization in Long-Horizon Vision-Language Models
A new step-level measure of visual grounding in long-horizon vision-language models predicts out-of-distribution generalization (r=0.83), and varies independently of model scale and in-distribution accuracy.
-
Towards Video Thinking Test: A Holistic Benchmark for Advanced Video Reasoning and Understanding
A new 1,000-video benchmark with adversarial question variants shows video LLMs remain far below human accuracy and robustness on short real-world videos.
-
ProactiveVideoQA: A Comprehensive Benchmark Evaluating Proactive Interactions in Video Large Language Models
ProactiveVideoQA is a benchmark for proactive video question answering, and the proposed PAUC metric jointly scores response timing and content, claiming better alignment with human preferences.
-
Iterative Zoom-In: Temporal Interval Exploration for Long Video Understanding
A confidence-guided iterative zoom-in framework improves long-video question answering for multimodal LLMs without any training.
Discussion (0). Sign in to comment.