CaReBench: A Fine-Grained Benchmark for Video Captioning and Retrieval

• URL: http://arxiv.org/abs/2501.00513v2
• Date: Tue, 18 Mar 2025 16:01:24 GMT
• Title: CaReBench: A Fine-Grained Benchmark for Video Captioning and Retrieval
• Authors: Yifan Xu, Xinhao Li, Yichun Yang, Desen Meng, Rui Huang, Limin Wang,
• Abstract summary: We present CaReBench, a testing benchmark for fine-grained video captioning and retrieval.<n>Uniquely, it provides manually separated spatial annotations and temporal annotations for each video.<n>Based on this design, we introduce two evaluation metrics, ReBias and CapST, specifically tailored for video retrieval and video captioning tasks.
• Score: 24.203328970223527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video understanding, including video captioning and retrieval, is still a great challenge for video-language models (VLMs). The existing video retrieval and caption benchmarks only include short descriptions, limits their ability of detailed video understanding evaluation. To address this problem, we present CaReBench, a testing benchmark for fine-grained video captioning and retrieval with 1,000 high-quality pairs of videos and human-annotated detailed captions. Uniquely, it provides manually separated spatial annotations and temporal annotations for each video. Based on this design, we introduce two evaluation metrics, ReBias and CapST, specifically tailored for video retrieval and video captioning tasks, respectively. These metrics enable a comprehensive investigation into the spatial and temporal biases inherent in VLMs. In addition, to handle both video retrieval and video captioning tasks in a unified framework, we develop a simple baseline based on a Multimodal Language Model (MLLM). By implementing a two-stage Supervised Fine-Tuning (SFT), we fully unlock the potential of MLLM, enabling it not only to generate detailed video descriptions but also to extract video features. Surprisingly, experimental results demonstrate that, compared to the CLIP-based models designed for retrieval and the popular MLLMs skilled in video captioning, our baseline shows competitive performance in both fine-grained video retrieval and video detailed captioning.

Related papers

• Video Summarization with Large Language Models [41.51242348081083]
  We propose a new video summarization framework that leverages the capabilities of recent Large Language Models (LLMs) Our method, dubbed LLM-based Video Summarization (LLMVS), translates video frames into a sequence of captions using a Muti-modal Large Language Model (MLLM) Our experimental results demonstrate the superiority of the proposed method over existing ones in standard benchmarks.
  arXiv  Detail & Related papers  (2025-04-15T13:56:14Z)
• SCBench: A Sports Commentary Benchmark for Video LLMs [19.13963551534595]
  We develop a benchmark for sports video commentary generation for Video Large Language Models (Video LLMs) $textbfSCBench$ is a six-dimensional metric specifically designed for our task, upon which we propose a GPT-based evaluation method. Our results found InternVL-Chat-2 achieves the best performance with 5.44, surpassing the second-best by 1.04.
  arXiv  Detail & Related papers  (2024-12-23T15:13:56Z)
• VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding [44.382937324454254]
  Existing Video Corpus Moment Retrieval (VCMR) is limited to coarse-grained understanding. We propose a more challenging fine-grained VCMR benchmark requiring methods to localize the best-matched moment from the corpus. With VERIFIED, we construct a more challenging fine-grained VCMR benchmark containing Charades-FIG, DiDeMo-FIG, and ActivityNet-FIG.
  arXiv  Detail & Related papers  (2024-10-11T07:42:36Z)
• AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark [89.73538448786405]
  We propose AuroraCap, a video captioner based on a large multimodal model. We implement the token merging strategy, reducing the number of input visual tokens. AuroraCap shows superior performance on various video and image captioning benchmarks.
  arXiv  Detail & Related papers  (2024-10-04T00:13:54Z)
• MLLM as Video Narrator: Mitigating Modality Imbalance in Video Moment Retrieval [53.417646562344906]
  Video Moment Retrieval (VMR) aims to localize a specific temporal segment within an untrimmed long video given a natural language query. Existing methods often suffer from inadequate training annotations, i.e., the sentence typically matches with a fraction of the prominent video content in the foreground with limited wording diversity. This intrinsic modality imbalance leaves a considerable portion of visual information remaining unaligned with text. In this work, we take an MLLM as a video narrator to generate plausible textual descriptions of the video, thereby mitigating the modality imbalance and boosting the temporal localization.
  arXiv  Detail & Related papers  (2024-06-25T18:39:43Z)
• Needle In A Video Haystack: A Scalable Synthetic Evaluator for Video MLLMs [20.168429351519055]
  Video understanding is a crucial next step for multimodal large language models (LMLMs) We propose VideoNIAH (Video Needle In A Haystack), a benchmark construction framework through synthetic video generation. We conduct a comprehensive evaluation of both proprietary and open-source models, uncovering significant differences in their video understanding capabilities.
  arXiv  Detail & Related papers  (2024-06-13T17:50:05Z)
• LongVLM: Efficient Long Video Understanding via Large Language Models [55.813206751150716]
  LongVLM is a simple yet powerful VideoLLM for long video understanding. We encode video representations that incorporate both local and global information. Our model produces more precise responses for long video understanding.
  arXiv  Detail & Related papers  (2024-04-04T11:33:29Z)
• VidLA: Video-Language Alignment at Scale [48.665918882615195]
  We propose VidLA, an approach for video-language alignment at scale. Our proposed approach surpasses state-of-the-art methods on multiple retrieval benchmarks.
  arXiv  Detail & Related papers  (2024-03-21T22:36:24Z)
• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization [52.63845811751936]
  Video pre-training is challenging due to the modeling of its dynamics video. In this paper, we address such limitations in video pre-training with an efficient video decomposition. Our framework is both capable of comprehending and generating image and video content, as demonstrated by its performance across 13 multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-02-05T16:30:49Z)
• Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos [58.53311308617818]
  We present a new multi-shot video understanding benchmark Shot2Story with detailed shot-level captions, comprehensive video summaries and question-answering pairs. Preliminary experiments show some challenges to generate a long and comprehensive video summary for multi-shot videos. The generated imperfect summaries can already achieve competitive performance on existing video understanding tasks.
  arXiv  Detail & Related papers  (2023-12-16T03:17:30Z)
• VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding [63.075626670943116]
  We introduce a cutting-edge framework, VaQuitA, designed to refine the synergy between video and textual information. At the data level, instead of sampling frames uniformly, we implement a sampling method guided by CLIP-score rankings. At the feature level, we integrate a trainable Video Perceiver alongside a Visual-Query Transformer.
  arXiv  Detail & Related papers  (2023-12-04T19:48:02Z)
• HowToCaption: Prompting LLMs to Transform Video Annotations at Scale [72.69268311756082]
  We propose to leverage the capabilities of large language models (LLMs) to obtain high-quality video descriptions aligned with videos at scale. We introduce a prompting method that is able to take into account a longer text of subtitles, allowing us to capture the contextual information beyond one single sentence. We apply our method to the subtitles of the HowTo100M dataset, creating a new large-scale dataset, HowToCaption.
  arXiv  Detail & Related papers  (2023-10-07T19:32:55Z)
• MVMR: A New Framework for Evaluating Faithfulness of Video Moment Retrieval against Multiple Distractors [24.858928681280634]
  We propose the MVMR (Massive Videos Moment Retrieval for Faithfulness Evaluation) task. It aims to retrieve video moments within a massive video set, including multiple distractors, to evaluate the faithfulness of VMR models. For this task, we suggest an automated massive video pool construction framework to categorize negative (distractors) and positive (false-negative) video sets.
  arXiv  Detail & Related papers  (2023-08-15T17:38:55Z)
• Temporal Perceiving Video-Language Pre-training [112.1790287726804]
  This work introduces a novel text-video localization pre-text task to enable fine-grained temporal and semantic alignment. Specifically, text-video localization consists of moment retrieval, which predicts start and end boundaries in videos given the text description. Our method connects the fine-grained frame representations with the word representations and implicitly distinguishes representations of different instances in the single modality.
  arXiv  Detail & Related papers  (2023-01-18T12:15:47Z)
• TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency [133.75876535332003]
  We focus on summarizing instructional videos, an under-explored area of video summarization. Existing video summarization datasets rely on manual frame-level annotations. We propose an instructional video summarization network that combines a context-aware temporal video encoder and a segment scoring transformer.
  arXiv  Detail & Related papers  (2022-08-14T04:07:40Z)
• A Hierarchical Multi-Modal Encoder for Moment Localization in Video Corpus [31.387948069111893]
  We show how to identify a short segment in a long video that semantically matches a text query. To tackle this problem, we propose the HierArchical Multi-Modal EncodeR (HAMMER) that encodes a video at both the coarse-grained clip level and the fine-trimmed frame level. We conduct extensive experiments to evaluate our model on moment localization in video corpus on ActivityNet Captions and TVR datasets.
  arXiv  Detail & Related papers  (2020-11-18T02:42:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.