LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary Captioning
Not on the map. Why?
Abstract (arXiv)
Our winning entry for the CVPR 2023 Generic Event Boundary Captioning (GEBC) competition is detailed in this paper. Unlike conventional video captioning tasks, GEBC demands that the captioning model possess an understanding of immediate changes in status around the designated video boundary, making it a difficult task. This paper proposes an effective model LLMVA-GEBC (Large Language Model with Video Adapter for Generic Event Boundary Captioning): (1) We utilize a pretrained LLM for generating human-like captions with high quality. (2) To adapt the model to the GEBC task, we take the video Q-former as an adapter and train it with the frozen visual feature extractors and LLM. Our proposed method achieved a 76.14 score on the test set and won the first place in the challenge. Our code is available at https://github.com/zjr2000/LLMVA-GEBC .
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 10 of its 13 references that are in the map. First posted on arXiv 17 Jun 2023.
Builds on
10 of 13 references.
References · 10
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
- 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
- 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
- 2023Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understandingcited by 1,471
- 2021Scaling Vision Transformerscited by 999
- 2022InternVideo: General Video Foundation Models via Generative and Discriminative Learningcited by 447
- 2022Omnivore: A Single Model for Many Visual Modalitiescited by 215
- 2022Exploiting Context Information for Generic Event Boundary Captioningcited by 3
- 2022Dual-Stream Transformer for Generic Event Boundary Captioningcited by 3
Built on by
Citing papers per year: 2023: 1; 2024: 8.
9 papers.
- 2023Video Understanding with Large Language Models: A Surveycited by 206
- 2024CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputscited by 56
- 2024V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuningcited by 46
- 2024ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Modelscited by 21
- 2024An Empirical Analysis on Large Language Models in Debate Evaluationcited by 20
- 2024Empowering LLMs with Pseudo-Untrimmed Videos for Audio-Visual Temporal Understandingcited by 18
- 2024OSCaR: Object State Captioning and State Change Representationcited by 16
- 2024EAGLE: Egocentric AGgregated Language-video Enginecited by 16
- 2024Investigating the Scaling Effect of Instruction Templates for Training Multimodal Language Modelcited by 1
- 2024Investigating the Scaling Effect of Instruction Templates for Training Multimodal Language Model11 Dec 2024
- 2024ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models9 Dec 2024
- 2024EAGLE: Egocentric AGgregated Language-video Engine26 Sep 2024
- 2024An Empirical Analysis on Large Language Models in Debate Evaluation28 May 2024
- 2024V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning18 Apr 2024
- 2024Empowering LLMs with Pseudo-Untrimmed Videos for Audio-Visual Temporal Understanding24 Mar 2024
- 2024OSCaR: Object State Captioning and State Change Representation27 Feb 2024
- 2024CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs5 Jan 2024
- 2023Video Understanding with Large Language Models: A Survey29 Dec 2023