Recent advances in learning multi-modal representation have witnessed the success in
biomedical domains. While established techniques enable handling multi-modal …

Generating diverse plausible outputs from a single input is crucial for addressing visual
ambiguities, exemplified in medical imaging where experts may provide varying semantic …

Domain Adaptive Object Detection (DAOD) transfers an object detector from a labeled
source domain to a novel unlabeled target domain. Recent works bridge the domain gap by …

Colonoscopy is currently one of the most sensitive screening methods for colorectal cancer.
This study investigates the frontiers of intelligent colonoscopy techniques and their …

Open-vocabulary object detection (OVD) utilizes imagelevel cues to expand the linguistic
space of region proposals, thereby facilitating the detection of diverse novel classes. Recent …

Domain adaptive object detection (DAOD) aims to develop a detector trained on labeled
source domains to identify objects in unlabeled target domains. A primary challenge in …

To address the performance degradation of cross-domain object detection under various
illumination conditions and adverse weather scenarios, this paper introduces a novel …

Detection transformers such as DETR (Carion et al., 2020) have recently exhibited
promising performance for many object detection tasks, but the generalization ability of …

In the field of intelligent healthcare, the accessibility of medical data is severely constrained
by privacy concerns, high costs, and limited patient cases, significantly hindering automated …

Domain Adaptive Object Detection (DAOD) alleviates the challenge of labor-intensive
annotations by transferring semantic information from a labeled source domain to an …