362 citations · 473 across the 6 of their papers we have counts for
6 papers
Activating the Discriminability of Novel Classes for Few-shot Segmentation
Dianwen Mei, Wei Zhuo, Jiandong Tian +2
Despite the remarkable success of existing methods for few-shot segmentation, there remain two crucial challenges. First, the feature learning for novel classes is suppressed durin…
Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations
Wenjie Pei, Shuang Wu, Dianwen Mei +3
While fine-tuning based methods for few-shot object detection have achieved remarkable progress, a crucial challenge that has not been addressed well is the potential class-specifi…
Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection
Shuang Wu, Wenjie Pei, Dianwen Mei +3
Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and…
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets
Weihong Ren, Xinchao Wang, Jiandong Tian +2
State-of-the-art multi-object tracking~(MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object de…
RGBD Salient Object Detection via Deep Fusion
Liangqiong Qu, Shengfeng He, Jiawei Zhang +3
Numerous efforts have been made to design different low level saliency cues for the RGBD saliency detection, such as color or depth contrast features, background and color compactn…
Pixel-wise Orthogonal Decomposition for Color Illumination Invariant and Shadow-free Image
Liangqiong Qu, Jiandong Tian, Zhi Han +1
In this paper, we propose a novel, effective and fast method to obtain a color illumination invariant and shadow-free image from a single outdoor image. Different from state-of-the…