Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

Light traveling through water results in strong scattering across color channels, restricting
visibility in underwater images. Many cutting-edge underwater image enhancement …

This paper proposes a new transformer-based framework to learn class-specific object
localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS) …

The class activation maps are generated from the final convolutional layer of CNN. They can
highlight discriminative object regions for the class of interest. These discovered object …

Identifying and characterizing vascular plants in time and space is required in various
disciplines, eg in forestry, conservation and agriculture. Remote sensing emerged as a key …

Self-attention techniques, and specifically Transformers, are dominating the field of text
processing and are becoming increasingly popular in computer vision classification tasks. In …

Learning semantic segmentation from weakly-labeled (eg, image tags only) data is
challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …

Inspired by the human cognitive system, attention is a mechanism that imitates the human
cognitive awareness about specific information, amplifying critical details to focus more on …

Mining precise class-aware attention maps, aka, class activation maps, is essential for
weakly supervised semantic segmentation. In this paper, we present L2G, a simple online …