Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …

Face forgery detection is raising ever-increasing interest in computer vision since facial
manipulation technologies cause serious worries. Though recent works have reached …

Attention mechanisms are now widely used in image captioning models. However, most
attention models only focus on visual features. When generating syntax related words, little …

Nowadays, Deep Neural Networks are among the main tools used in various sciences.
Convolutional Neural Network is a special type of DNN consisting of several convolution …

The performance of text-video retrieval has been significantly improved by vision-language
cross-modal learning schemes. The typical solution is to directly align the global video-level …

Food recognition plays an important role in food choice and intake, which is essential to the
health and well‐being of humans. It is thus of importance to the computer vision community …

There still remains an extreme performance gap between Vision Transformers (ViTs) and
Convolutional Neural Networks (CNNs) when training from scratch on small datasets, which …

Fine-grained image recognition is a longstanding computer vision challenge that focuses on
differentiating objects belonging to multiple subordinate categories within the same meta …

Deep clustering integrates embedding and clustering together to obtain the optimal
nonlinear embedding space, which is more effective in real-world scenarios compared with …

The challenge of fine-grained visual recognition often lies in discovering the key
discriminative regions. While such regions can be automatically identified from a large-scale …