The Solution for Single Object Tracking Task of Perception Test Challenge 2024
원문 초록
This report presents our method for Single Object Tracking (SOT), which aims to track a specified object throughout a video sequence. We employ the LoRAT method. The essence of the work lies in adapting LoRA, a technique that fine-tunes a small subset of model parameters without adding inference latency, to the domain of visual tracking. We train our model using the extensive LaSOT and GOT-10k datasets, which provide a solid foundation for robust performance. Additionally, we implement the alpha-refine technique for post-processing the bounding box outputs. Although the alpha-refine method does not yield the anticipated results, our overall approach achieves a score of 0.813, securing first place in the competition.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning
Adversarial Attack for RGB-Event based Visual Object Tracking
Less is More: Token Context-aware Learning for Object Tracking
Exploring Enhanced Contextual Information for Video-Level Object Tracking
A Distractor-Aware Memory for Visual Object Tracking with SAM2
SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
토론
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