6 accepted papers
In this paper, we present Generative Extension Positive Pairs (GEPP), a novel approach to enhance unsupervised person re-identification (re-id) through contrastive learning. Data generation and pair selection methods significantly impact model performance in contrastive learning. To improve positive…
In the domain of Unsupervised Domain Adaptation (UDA), the training process leverages both labeled data from the source domain and unlabeled data from the target domain to facilitate the transfer of knowledge to the target domain. Previous UDA methods have sought to improve robust visual recognition…
Multi-Camera Multi-Object Tracking (MC-MOT) utilizes information from multiple views to better handle problems with occlusion and crowded scenes. Recently, the use of graph-based approaches to solve tracking problems has become very popular. However, many current graph-based methods do not effective…
Most methods address the multi-object tracking (MOT) problem by tracking-by-detection paradigm, which tracks objects from the detected windows by associating detection boxes whose scores are higher than a given threshold. As such, the confidence score becomes the only indicator of bounding boxes whe…
In recent years, novel view synthesis methods has been proposed and combined with many models in different computer vision tasks. Previous works solve the problem by using additional 3D information. In this paper, a novel view synthesis method is proposed based on Generative Adversarial Networks (GA…
A novel Hierarchical Structured Dictionary Learning (HSDL) algorithm is proposed in this paper. It aims to learn class-specific dictionaries for all classes simultaneously in a hierarchical structure. A discriminative term based on Fisher discrimination criterion is jointly considered for both the c…