5 accepted papers
Person search is a challenging computer vision task that aims to simultaneously detect and re-identify individuals from uncropped gallery images. However, most existing approaches are limited by restricted receptive fields, leading to distorted local feature representations under occlusions or compl
Multi-modal object Re-identification (ReID) aims to retrieve individuals by leveraging complementary information from different modalities. Recent CLIP-based approaches show promising results, but they usually employ prompt-based or hybrid prompt-adapter tuning and still face the problems of heterog
Text-to-Image Person Retrieval (TIPR) aims to utilize natural language descriptions as queries to retrieve pedestrian images. However, existing methods only concentrated on aligning individual text-image pairs and ignored the specific self-representations within both visible images and textual descr…
Previous advances in vehicle re-identification (ReID) are mostly reported under favorable lighting conditions while cross-day-and-night performance is neglected which greatly hinders the development of related traffic intelligence applications. This work instead develops a novel Day-Night Dual-domai…
Symmetries are prevalent in deep learning and can significantly influence the learning dynamics of neural networks. In this paper, we examine how exponential symmetries -- a broad subclass of continuous symmetries present in the model architecture or loss function -- interplay with stochastic gradie…