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VidOmni-Bench is a new benchmark for fine‑grained video understanding that asks models to verify whether each event in dense video captions is supported by the video. It contains 500 videos covering five complexity types and durations from 4 seconds to 90 minutes, and uses human‑verified sentence‑level labels to create hard negatives. Experiments show that Video‑LLMs often hallucinate events, struggle to detect incorrect descriptions, and exhibit varying weaknesses depending on video complexity and duration.
The paper introduces OACap, a caption-guided active observation system for detailed video captioning that requests targeted observations to fill missing information and iteratively refines its captions. OACap consistently improves performance on general video-understanding benchmarks while using only a text‑only reader. The authors also present FSCap‑Bench, a new evaluation suite with Core‑Support Factuality Score and Cross‑Modal Semantic Round‑Trip Score to assess factual and semantic preservation in captions.
arXiv:2606.29997v2 Announce Type: replace Abstract: Automatic evaluation of image and video captioning is essential for benchmarking multimodal systems, although standard evaluation metrics show limi...
Automatic evaluation of image and video captioning is essential for benchmarking multimodal systems, although standard evaluation metrics show limited alignment with human judgments. Recent approaches using large language models (LLMs), commonly referred to as LLM-as-a-Judge, have improved alignment with human judgments but still suffer from a mismatch between large-vocabulary language modeling and evaluation over a small label set.
The paper introduces CodeCap-Bench, a benchmark for evaluating whether executable code can serve as an effective video caption. It compares code captions to natural language captions on tasks such as question answering and video reconstruction, finding that code captions yield higher reasoning accuracy and reconstruction fidelity. The study also assesses current multimodal large language models, noting that while proprietary models show promise, they still struggle with complex temporal dynamics, and open‑source models lag significantly.
DEPICT is a new training‑free metric for evaluating text‑to‑image alignment. It replaces fixed reference answers with an agreement rule that compares image‑based and caption‑only responses, weighting questions by how decisively the caption determines them. By merging this agreement score with a holistic score, DEPICT improves negation accuracy dramatically and outperforms existing training‑free metrics while matching or exceeding fine‑tuned evaluators on several benchmarks.