Papers with fine-grained understanding
RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning (2025.emnlp-industry)
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Deyi Ji, Yuekui Yang, Liqun Liu, Peng Shu, Haiyang Wu, Shaogang Tang, Xudong Chen, Shaoping Ma, Tianrun Chen, Lanyun Zhu
| Challenge: | Recent advances in large language models have improved the detection of non-compliant content, but critical gaps persist in fine-grained understanding, explainability, and generalization. |
| Approach: | They propose a framework that combines active reinforcement learning, fine-grained violation understanding and progressive multi-stage training. |
| Outcome: | The proposed framework outperforms general-purpose LLMs and specialized models in fine-grained violation understanding, explainability, and generalization. |
Do Video Language Models really understand the video contexts? (2025.naacl-srw)
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| Challenge: | Recent advances in VideoQA performance have shown that visual language models are effective but the processes of understanding and reasoning in VLMs remain under-explored. |
| Approach: | They propose a framework that incorporates a fine-grained question generation and answering process to measure how well VLMs understand video question answering tasks. |
| Outcome: | The proposed framework incorporates a fine-grained question generation and answering process to measure how well the responses generated by VLMs align with what the model understands. |
TemporalVLM: Video LLMs for Temporal Reasoning in Long Videos (2026.findings-acl)
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| Challenge: | Several video understanding applications require the ability of temporal reasoning. |
| Approach: | They propose a video large language model for temporal reasoning and fine-grained understanding in long videos. |
| Outcome: | The proposed model outperforms existing methods in time and motion studies and temporal action segmentation evaluations. |
TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos (2025.acl-long)
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Fanheng Kong, Jingyuan Zhang, Hongzhi Zhang, Shi Feng, Daling Wang, Linhao Yu, Xingguang Ji, Yu Tian, V. W., Fuzheng Zhang
| Challenge: | Existing benchmarks for video understanding often focus on specific aspects, overlooking the holistic nature of video content. |
| Approach: | They propose a temporal-oriented benchmark for fine-grained understanding on dense dynamic videos with two complementary tasks: captioning and QA. |
| Outcome: | The proposed model performs well on diverse video scenarios and dynamic videos, with interpretable and robust evaluation criteria. |
Timeline-based Sentence Decomposition with In Context Learning for Temporal Fact Extraction (2024.acl-long)
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| Challenge: | Recent research on temporal fact extraction fails to establish time-to-fact correspondences in complex sentences. |
| Approach: | They propose a timeline-based sentence decomposition strategy using large language models with in-context learning to extract temporal facts from natural language text. |
| Outcome: | The proposed method achieves state-of-the-art on a complex temporal fact extraction dataset. |
Understanding Fine-grained Distortions in Reports of Scientific Findings (2024.findings-acl)
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| Challenge: | a fine-grained understanding of how scientific findings are reported is crucial, says a new study . a recent study found that tweets distort scientific findings more often than news reports . |
| Approach: | They propose to annotate 1,600 scientific findings from academic papers paired with corresponding tweets . they also establish baselines for automatically detecting these characteristics . |
| Outcome: | The proposed method outperforms few-shot prompting in detecting distortions in unpaired data. |
GHAN: Graph-Based Hierarchical Aggregation Network for Text-Video Retrieval (2022.emnlp-main)
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| Challenge: | Existing approaches to text-video retrieval are limited due to structural and semantic differences between text and video. |
| Approach: | They propose an end-to-end graph-based hierarchical aggregation network for text-video retrieval according to the hierarchy possessed by text and video. |
| Outcome: | The proposed model achieves Recall@1 of 73.0%, 65.6%, and 64.0% better than the current state-of-the-art model. |
YouMakeup: A Large-Scale Domain-Specific Multimodal Dataset for Fine-Grained Semantic Comprehension (D19-1)
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| Challenge: | Multimodal semantic comprehension has attracted increasing research interest recently such as visual question answering and caption generation. |
| Approach: | They propose to use a large-scale multimodal instructional video dataset to support fine-grained comprehension research in specific domain. |
| Outcome: | The proposed dataset contains 2,800 videos from YouTube, spanning more than 420 hours in total. |