9 accepted papers
Text-to-image person retrieval (TIPR) aims to find images of the same identity that match a given text description. Current TIPR methods mainly focus on mining the association between images and texts, ignoring their potential complementarity. Besides, existing matching losses treat all positive pai
Visible-infrared person re-identification (VIReID) retrieves pedestrian images with the same identity across different modalities. Existing methods learn visual features solely from images, failing to align them into the modality-invariant semantic space. In this paper, we propose a novel framework,
Cloth-changing Person Re-identification aims to retrieve target pedestrians across different cameras under clothing-changing scenarios. In recent years, many scholars have made significant explorations in this field. However, existing methods often overlook the semantic consistency and integrity of…
Traditional text-image person retrieval methods heavily rely on fully matched and identity-annotated multimodal data, representing an ideal yet limited scenario. The issues of handling incomplete multimodal data and the complexities of labeling multimodal data are common challenges encountered in re…
Cross-modal retrieval often faces the challenges of eliminating modality gap, learning robust modality invariance and semantic discrimination. Existing self-supervised crossmodal approaches still suffer from the faulty negative sample selection strategy and the lack of reliable high-level semantic d…
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images. In this paper, we respond to the intriguing learning-related question -- if leveraging both accessible unpaired over/underexposed images and high-le…
Using only one deep model for cross scene video foreground segmentation is still very challenging because existing methods are scene-dependent, which restricts the consistent segmentation. In this paper, we propose a cross scene video foreground segmentation framework to extend the generalization ca…
Siamese network based trackers formulate the visual tracking task as a similarity matching problem. Almost all popular Siamese trackers realize the similarity learning via convolutional feature cross-correlation between a target branch and a search branch. However, since the size of target feature r…
Modern supervised learning relies on a large amount of training data, yet there are many noisy annotations in real datasets. For semantic segmentation tasks, pixel-level annotation noise is typically located at the edge of an object, while pixels within objects are fine-annotated. We argue the coars…