Fine-grained visual classification (FGVC) strives to distinguish images from distinct sub-
classes within the same overarching meta-class, which is significant in various practical …

Objective Fine-grained classification of historical traditional villages plays a crucial role in
guiding the future development and construction of urban and rural areas. This study aims to …

Recent advances in pre-trained vision-language models have successfully boosted the
performance of unsupervised image representation in many vision tasks. Most of existing …

Differentiable architecture search (DARTS) provides a fast solution to Neural Architecture
Search (NAS) by using gradient descent over a continuous search space. Despite its high …

Gait recognition aims to identify individuals at a distance based on their biometric gait
patterns. While offering flexibility in network input, existing set-based methods often overlook …

Nighttime semantic segmentation has been playing a critical role in intelligent transportation,
building safety and urban management. However, nighttime scenes present some …

Complex image scenes are a challenge in the collaborative saliency object detection task in
the field of saliency detection, such as the inability to accurately locate salient object …

Recently, crowd counting has been continuously a hot topic in the field of computer vision
and achieved significant progress using deep learning techniques. Nevertheless, it is still a …

Underwater images often suffer from substantial image blur and color distortion due to the
variability of water conditions and the physical location of optical equipment, which …

The challenge in fine-grained image classification tasks lies in distinguishing subtle
differences among fine-grained images. Existing image classification methods often only …