Vision Transformers (ViTs) are becoming more popular and dominating technique for
various vision tasks, compare to Convolutional Neural Networks (CNNs). As a demanding …

Deforestation has become a major cause of climate change, and as a result, both
characterizing the drivers and estimating segmentation maps of deforestation have piqued …

Recently, self-attention mechanisms have shown impressive performance in various NLP
and CV tasks, which can help capture sequential characteristics and derive global …

Weakly supervised object localization (WSOL) remains challenging when learning object
localization models from image category labels. Conventional methods that discriminatively …

Fine-grained bird image classification (FBIC) is not only meaningful for endangered bird
observation and protection but also a prevalent task for image classification in multimedia …

The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet
discriminative features. Most previous works achieve this by explicitly selecting the …

Mixup-based augmentation has been found to be effective for generalizing models during
training, especially for Vision Transformers (ViTs) since they can easily overfit. However …

Interpreting and explaining the behavior of deep neural networks is critical for many tasks.
Explainable AI provides a way to address this challenge, mostly by providing per-pixel …

Since the introduction of the Vision Transformer (ViT), researchers have sought to make ViTs
more efficient by removing redundant information in the processed tokens. While different …

Vision transformers (ViTs), which have demonstrated a state-of-the-art performance in image
classification, can also visualize global interpretations through attention-based contributions …