Video object detection has been an important yet challenging topic in computer vision. Traditional methods mainly focus on designing the image-level or box-level feature propagation strategies to exploit temporal information. This paper argues that with a more effective and efficient framework, video detectors can gain improvement terms of both accuracy speed. For this purpose, studies object-l...

Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using model trained only on image-level annotations. Current state-of-the-art models benefit from self-supervised instance-level supervision, but since weak supervision does not include count or location information, the most common “argmax” labeling method often ignores many instances of objects. To alleviate ...

Viewing various stereo images under different viewing conditions has escalated the need for effective object-level remapping techniques. In this paper, we propose a new object spatial mapping scheme, which adjusts depth and size of selected to match user preference conditions. Existing warping-based methods often distort shape important objects or cannot faithfully adjust depth/size due imprope...