The Solution for the ICCV 2023 Perception Test Challenge 2023 -- Task 6 -- Grounded videoQA
원문 초록
In this paper, we introduce a grounded video question-answering solution. Our research reveals that the fixed official baseline method for video question answering involves two main steps: visual grounding and object tracking. However, a significant challenge emerges during the initial step, where selected frames may lack clearly identifiable target objects. Furthermore, single images cannot address questions like "Track the container from which the person pours the first time." To tackle this issue, we propose an alternative two-stage approach:(1) First, we leverage the VALOR model to answer questions based on video information.(2) concatenate the answered questions with their respective answers. Finally, we employ TubeDETR to generate bounding boxes for the targets.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document Images
video-SALMONN 2: Captioning-Enhanced Audio-Visual Large Language Models
CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.