49 citations · 115 across the 3 of their papers we have counts for
9 papers
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction
Yan Wang, Peng Tang, Yuyin Zhou +3
Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segment…
Shape-Texture Debiased Neural Network Training
Yingwei Li, Qihang Yu, Mingxing Tan +5
Shape and texture are two prominent and complementary cues for recognizing objects. Nonetheless, Convolutional Neural Networks are often biased towards either texture or shape, dep…
Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video
Zhenfang Chen, Lin Ma, Wenhan Luo +2
In this paper, we study the problem of weakly-supervised temporal grounding of sentence in video. Specifically, given an untrimmed video and a query sentence, our goal is to locali…
Proposal Learning for Semi-Supervised Object Detection
Peng Tang, Chetan Ramaiah, Yan Wang +2
In this paper, we focus on semi-supervised object detection to boost performance of proposal-based object detectors (a.k.a. two-stage object detectors) by training on both labeled…
Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models
Hongru Zhu, Peng Tang, Jeongho Park +2
Most objects in the visual world are partially occluded, but humans can recognize them without difficulty. However, it remains unknown whether object recognition models like convol…
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
Peng Tang, Xinggang Wang, Song Bai +4
Weakly Supervised Object Detection (WSOD), using only image-level annotations to train object detectors, is of growing importance in object recognition. In this paper, we propose a…