Instance segmentation requires labor-intensive manual labeling of the contours of complex
objects in images for training. The labels can also be provided incrementally in practice to …

Fine-grained visual classification is a challenging task due to the high similarity between
categories and distinct differences among data within one single category. To address the …

Recent progress in weakly supervised object detection is featured by a combination of
multiple instance detection networks (MIDN) and ordinal online refinement. However, with …

Despite weakly supervised object detection (WSOD) being a promising step toward evading
strong instance-level annotations, its capability is confined to closed-set categories within a …

Single-point annotation in visual tasks with the goal of minimizing labeling costs is becoming
increasingly prominent in research. Recently visual foundation models such as Segment …

Weakly-supervised visual recognition using inexact supervision is a critical yet challenging
learning problem. It significantly reduces human labeling costs and traditionally relies on …

Abstract Automated detection of Gallbladder Cancer (GBC) from Ultrasound (US) images is
an important problem, which has drawn increased interest from researchers. However, most …

Weakly supervised object detection (WSOD) in remote sensing images (RSIs) is used to
detect high-value objects by utilizing image-level labels. However, the current models still …

Abstract Weakly Supervised Object Detection (WSOD) has attracted increasing attention due
to the convenience and low-cost of acquiring image-level annotations. Most existing WSOD …

Weakly supervised object detection (WSOD) in remote sensing images (RSIs) aims to detect
high-value targets by solely utilizing image-level category labels; however, two problems …