Weakly supervised semantic segmentation (WSSS) models relying on class activation maps
(CAMs) have achieved desirable performance comparing to the non-CAMs-based …

In the past decade, convolutional neural networks (CNNs) have shown prominence for
semantic segmentation. Although CNN models have very impressive performance, the …

Both Convolutional Neural Networks (CNNs) and Transformers have shown great success
in semantic segmentation tasks. Efforts have been made to integrate CNNs with Transformer …

Transformers have shown impressive performance in various natural language processing
and computer vision tasks, due to the capability of modeling long-range dependencies …

Depth-aware panoptic segmentation is an emerging topic in computer vision which
combines semantic and geometric understanding for more robust scene interpretation …

Existing Visual Object Tracking (VOT) only takes the target area in the first frame as a
template. This causes tracking to inevitably fail in fast-changing and crowded scenes, as it …

Real-time semantic segmentation is essential for many practical applications, which utilizes
attention-based feature aggregation into lightweight structures to improve accuracy and …

Real-time perception, or streaming perception, is a crucial aspect of autonomous driving that
has yet to be thoroughly explored in existing research. To address this gap, we present …

The image-level label has prevailed in weakly supervised semantic segmentation tasks due
to its easy availability. Since image-level labels can only indicate the existence or absence …

Semantic segmentation, which aims to accurately identify each pixel, is a meaningful and
challenging task. Recently, we witness a strong tendency to improve model efficiency in low …