Search Results for author: Peng Tang

Found 15 papers, 5 papers with code

Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction

no code implementations31 May 2021 Yan Wang, Peng Tang, Yuyin Zhou, Wei Shen, Elliot K. Fishman, Alan L. Yuille

We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier.

Multiple Instance Learning

Shape-Texture Debiased Neural Network Training

1 code implementation ICLR 2021 Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan Yuille, Cihang Xie

To prevent models from exclusively attending on a single cue in representation learning, we augment training data with images with conflicting shape and texture information (eg, an image of chimpanzee shape but with lemon texture) and, most importantly, provide the corresponding supervisions from shape and texture simultaneously.

Adversarial Robustness Data Augmentation +2

Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video

no code implementations25 Jan 2020 Zhenfang Chen, Lin Ma, Wenhan Luo, Peng Tang, Kwan-Yee K. Wong

In this paper, we study the problem of weakly-supervised temporal grounding of sentence in video.

Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models

1 code implementation11 May 2019 Hongru Zhu, Peng Tang, Jeongho Park, Soojin Park, Alan Yuille

We test both humans and the above-mentioned computational models in a challenging task of object recognition under extreme occlusion, where target objects are heavily occluded by irrelevant real objects in real backgrounds.

Object Recognition

Weakly Supervised Region Proposal Network and Object Detection

no code implementations ECCV 2018 Peng Tang, Xinggang Wang, Angtian Wang, Yongluan Yan, Wenyu Liu, Junzhou Huang, Alan Yuille

The Convolutional Neural Network (CNN) based region proposal generation method (i. e. region proposal network), trained using bounding box annotations, is an essential component in modern fully supervised object detectors.

Region Proposal Weakly Supervised Object Detection

PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

3 code implementations9 Jul 2018 Peng Tang, Xinggang Wang, Song Bai, Wei Shen, Xiang Bai, Wenyu Liu, Alan Yuille

The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one.

Multiple Instance Learning Object Recognition +1

Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training

no code implementations7 Apr 2018 Yuyin Zhou, Yan Wang, Peng Tang, Song Bai, Wei Shen, Elliot K. Fishman, Alan L. Yuille

In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain.

Semantic Segmentation

Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound

no code implementations7 Apr 2018 Yan Wang, Yuyin Zhou, Peng Tang, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Based on the fact that very hard samples might have annotation errors, we propose a new sample selection policy, named Relaxed Upper Confident Bound (RUCB).

Medical Image Segmentation

Object Detection in Videos by High Quality Object Linking

no code implementations30 Jan 2018 Peng Tang, Chunyu Wang, Xinggang Wang, Wenyu Liu, Wen-Jun Zeng, Jingdong Wang

In particular, our method improves results by 8. 8% over the static image detector for fast moving objects.

General Classification Object Detection

Rethink ReLU to Training Better CNNs

no code implementations19 Sep 2017 Gangming Zhao, Zhao-Xiang Zhang, He Guan, Peng Tang, Jingdong Wang

Most of convolutional neural networks share the same characteristic: each convolutional layer is followed by a nonlinear activation layer where Rectified Linear Unit (ReLU) is the most widely used.

Deep Patch Learning for Weakly Supervised Object Classification and Discovery

1 code implementation6 May 2017 Peng Tang, Xinggang Wang, Zilong Huang, Xiang Bai, Wenyu Liu

Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background.

Classification General Classification +3

Multiple Instance Detection Network with Online Instance Classifier Refinement

3 code implementations CVPR 2017 Peng Tang, Xinggang Wang, Xiang Bai, Wenyu Liu

We propose a novel online instance classifier refinement algorithm to integrate MIL and the instance classifier refinement procedure into a single deep network, and train the network end-to-end with only image-level supervision, i. e., without object location information.

Multiple Instance Learning Object Recognition +1

Revisiting Multiple Instance Neural Networks

no code implementations8 Oct 2016 Xinggang Wang, Yongluan Yan, Peng Tang, Xiang Bai, Wenyu Liu

We propose a new multiple instance neural network to learn bag representations, which is different from the existing multiple instance neural networks that focus on estimating instance label.

Multiple Instance Learning

Deep FisherNet for Object Classification

no code implementations31 Jul 2016 Peng Tang, Xinggang Wang, Baoguang Shi, Xiang Bai, Wenyu Liu, Zhuowen Tu

Our proposed FisherNet combines convolutional neural network training and Fisher Vector encoding in a single end-to-end structure.

Classification General Classification +2

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