CaRe
Collection
CaReBench data, CaRe models and all the contrastively trained MLLMs (including InternVL2, MiniCPM-V 2.6, LLaVA NeXT Video, Qwen2-VL and Tariser). β’ 6 items β’ Updated β’ 2
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CaReBench: A Fine-grained Benchmark for Video Captioning and Retrieval
Yifan Xu, Xinhao Li, Yichun Yang, Desen Meng, Rui Huang, Limin Wang
π Introduction
π CaReBench is a fine-grained benchmark comprising 1,000 high-quality videos with detailed human-annotated captions, including manually separated spatial and temporal descriptions for independent spatiotemporal bias evaluation.
π ReBias and CapST Metrics are designed specifically for retrieval and captioning tasks, providing a comprehensive evaluation framework for spatiotemporal understanding in video-language models.
β‘ CaRe: A Unified Baseline for fine-grained video retrieval and captioning, achieving competitive performance through two-stage Supervised Fine-Tuning (SFT). CaRe excels in both generating detailed video descriptions and extracting robust video features.
π State-of-the-art performance on both detailed video captioning and fine-grained video retrieval. CaRe outperforms CLIP-based retrieval models and popular MLLMs in captioning tasks.
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