| Challenge: | Recent advances in text-to-video generation highlight the critical role of high-quality video-text pairs in training models capable of producing coherent and instruction-aligned videos. |
| Approach: | They propose a caption optimization framework tailored to the needs of T2V models. |
| Outcome: | The proposed framework improves video caption quality and video generation performance. |
Similar Papers
VidCapBench: A Comprehensive Benchmark of Video Captioning for Controllable Text-to-Video Generation (2025.findings-acl)
Copied to clipboard
Xinlong Chen, Yuanxing Zhang, Chongling Rao, Yushuo Guan, Jiaheng Liu, Fuzheng Zhang, Chengru Song, Qiang Liu, Di Zhang, Tieniu Tan
| Challenge: | Existing studies have not identified a link between video caption evaluation and T2V generation. |
| Approach: | They propose a video caption evaluation scheme specifically designed for T2V generation that integrates video annotation with caption evaluation. |
| Outcome: | The proposed system is agnostic to any particular caption format and can be used for training. |
Pretrained Image-Text Models are Secretly Video Captioners (2025.naacl-short)
Copied to clipboard
| Challenge: | Current video captioning methods often incorporate intricate designs tailored to video inputs. |
| Approach: | They adapt an image-based captioning model to address dynamic video sequences without modifications. |
| Outcome: | The proposed model outperforms specialised captioning systems on major benchmarks. |
Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning (2020.emnlp-main)
Copied to clipboard
| Challenge: | Observable changes in the scene are reflected in captions, but actions are also linked to social aspects such as intentions, effects, and attributes that describe the agent. |
| Approach: | They propose to generate captions from videos that describe latent aspects of the human agent's actions. |
| Outcome: | The proposed model can be used to describe latent aspects of human actions in video clips and answer questions about videos. |
ELIOT: Zero-Shot Video-Text Retrieval through Relevance-Boosted Captioning and Structural Information Extraction (2025.naacl-srw)
Copied to clipboard
| Challenge: | Recent advances in video-text retrieval (VTR) have relied on supervised learning and fine-tuning. |
| Approach: | They propose a zero-shot video-text retrieval framework that leverages off-the-shelf captioners, large language models, and text retrieval methods without additional training or annotated data. |
| Outcome: | The proposed framework outperforms existing methods on video-text retrieval benchmarks without data. |
A Challenging Multimodal Video Summary: Simultaneously Extracting and Generating Keyframe-Caption Pairs from Video (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to summarize video content have only considered video and image data, and the trend towards multimodal video summarization is changing. |
| Approach: | They propose a multimodal video summarization task setting and a dataset to train and evaluate the task. |
| Outcome: | The proposed task is useful as a practical application and presents a highly challenging problem worthy of study. |
End-to-end Dense Video Captioning as Sequence Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for dense video captioning use a two-stage generative process . but, more complex tasks are not able to fully utilize this powerful paradigm . |
| Approach: | They propose to model two subtasks of dense video captioning as one sequence generation task and predict the events and the corresponding descriptions. |
| Outcome: | Experiments on YouCook2 and ViTT show that the proposed model can be used on any video platform. |
Fighting FIRe with FIRE: Assessing the Validity of Text-to-Video Retrieval Benchmarks (2023.findings-eacl)
Copied to clipboard
Pedro Rodriguez, Mahmoud Azab, Becka Silvert, Renato Sanchez, Linzy Labson, Hardik Shah, Seungwhan Moon
| Challenge: | Existing benchmarks for text-to-video retrieval are incomplete, resulting in false negatives . a recent state-of-the-art model gains 25% recall points, but this is not the case for TVR. |
| Approach: | They propose to retire video captioning datasets as TVR benchmarks . they propose to annotate and release additional caption-video pairs to mitigate this flaw . |
| Outcome: | The proposed method fails to accurately reflect reality, despite lack of purpose-built benchmarks. |
ViLL-E: Video LLM Embeddings for Retrieval (2026.acl-long)
Copied to clipboard
| Challenge: | Video Large Language Models excel at video understanding tasks where outputs are textual . however, they underperform specialized embedding-based models in Retrieval tasks . |
| Approach: | They propose a video-LLM-based model with an embedding generation mechanism that allows the model to "think longer" for complex videos and stop early for easy ones. |
| Outcome: | The proposed model outperforms specialized embedding-based models in video understanding tasks while remaining competitive on VideoQA tasks. |
Captioning for Text-Video Retrieval via Dual-Group Direct Preference Optimization (2025.findings-emnlp)
Copied to clipboard
| Challenge: | auxiliary captions are generic and indistinguishable across visually similar videos . conventional captioning approaches are evaluated using language relevance scores . |
| Approach: | They propose a retrieval framework that directly optimizes caption generation using retrieval relevance scores. |
| Outcome: | The proposed retrieval framework optimizes caption generation using retrieval relevance scores . dual-group direct preference optimization is a learning strategy that supervises captioning . |
Sali4Vid: Saliency-Aware Video Reweighting and Adaptive Caption Retrieval for Dense Video Captioning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent work proposes end-to-end models but suffer from limitations . prior work focused on generating captions from long video streams . |
| Approach: | They propose a saliency-aware framework that localizes events and generates captions for each event. |
| Outcome: | The proposed framework achieves state-of-the-art results on YouCook2 and ViTT. |