The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …

Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …

Multiple instance learning (MIL) has been increasingly used in the classification of
histopathology whole slide images (WSIs). However, MIL approaches for this specific …

We design an open-vocabulary image segmentation model to organize an image into
meaningful regions indicated by arbitrary texts. Recent works (CLIP and ALIGN), despite …

Weakly-supervised semantic segmentation (WSSS) with image-level labels is an important
and challenging task. Due to the high training efficiency, end-to-end solutions for WSSS …

We address the challenging problem of whole slide image (WSI) classification. WSIs have
very high resolutions and usually lack localized annotations. WSI classification can be cast …

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 …

Large-scale training data with high-quality annotations is critical for training semantic and
instance segmentation models. Unfortunately, pixel-wise annotation is labor-intensive and …

Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level
weak supervision have several limitations: sparse object coverage, inaccurate object …

Image-level weakly supervised semantic segmentation is a challenging problem that has
been deeply studied in recent years. Most of advanced solutions exploit class activation map …