Abstract Object Re-identification (Re-ID) aims to identify specific objects across different
times and scenes, which is a widely researched task in computer vision. For a prolonged …

Text-Image Person Re-identification (TIReID) aims to retrieve the image corresponding to
the given text query from a pool of candidate images. Existing methods employ prior …

Human-centric assembly is emerging as a promising paradigm for achieving mass
personalization in the context of Industry 5.0, as it fully capitalizes on the advantages of …

Gait recognition is beneficial for a variety of applications, including video surveillance, crime
scene investigation, and social security, to mention a few. However, gait recognition often …

Although recent studies empirically show that injecting Convolutional Neural Networks
(CNNs) into Vision Transformers (ViTs) can improve the performance of person re …

In person re-identification (re-ID), extracting part-level features from person images has
been verified to be crucial to offer fine-grained information. Most of the existing CNN-based …

In the field of fine-grained visual recognition (FGVR), the ability to resolve minute and often
subtle differences between highly similar object categories is paramount. The advent of …

Pre-trained vision-language models like CLIP have recently shown superior performances
on various downstream tasks, including image classification and segmentation. However, in …

Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal
community which aims to retrieve the target person based on a textual query. Although …

Multimodal visual information fusion aims to integrate the multi-sensor data into a single
image which contains more complementary information and less redundant features …