Deep learning has been overwhelmingly successful in computer vision (CV), natural
language processing, and video/speech recognition. In this paper, our focus is on CV. We …

Abstract The integration of Deep Learning (DL) and the Internet of Things (IoT) has
revolutionized technology in the twenty-first century, enabling humans and machines to …

Referring video object segmentation aims to segment a referent throughout a video
sequence according to a natural language expression. It requires aligning the natural …

Given a natural language expression and an image/video, the goal of referring
segmentation is to produce the pixel-level masks of the entities described by the subject of …

Referring segmentation aims to generate a segmentation mask for the target instance
indicated by a natural language expression. There are typically two kinds of existing …

Existing interactive object segmentation methods mainly take spatial interactions such as
bounding boxes or clicks as input. However, these interactions do not contain information …

Many RGB-T trackers attempt to attain robust feature representation by utilizing an adaptive
weighting scheme (or attention mechanism). Different from these works, we propose a new …

Robust and reliable 3D multi-object tracking (MOT) is essential for autonomous driving in
crowded urban road scenes. In those scenarios, accurate data association between tracked …

Multi-task learning is a successful learning framework which improves the performance of
prediction models by leveraging knowledge among related tasks. Referring expression …

Abstract This paper proposes Panoptic Narrative Grounding, a spatially fine and general
formulation of the natural language visual grounding problem. We establish an experimental …