401 citations · 916 across the 20 of their papers we have counts for
20 papers
De-Diffusion Makes Text a Strong Cross-Modal Interface
Chen Wei, Chenxi Liu, Siyuan Qiao +3
We demonstrate text as a strong cross-modal interface. Rather than relying on deep embeddings to connect image and language as the interface representation, our approach represents…
STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction
Zhishuai Zhang, Jiyang Gao, Junhua Mao +3
Detecting pedestrians and predicting future trajectories for them are critical tasks for numerous applications, such as autonomous driving. Previous methods either treat the detect…
Localizing Occluders with Compositional Convolutional Networks
Adam Kortylewski, Qing Liu, Huiyu Wang +2
Compositional convolutional networks are generative compositional models of neural network features, that achieve state of the art results when classifying partially occluded objec…
Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation
Yuyin Zhou, Yingwei Li, Zhishuai Zhang +5
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phas…
Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures
Yuyin Zhou, David Dreizin, Yingwei Li +3
Trauma is the worldwide leading cause of death and disability in those younger than 45 years, and pelvic fractures are a major source of morbidity and mortality. Automated segmenta…
Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion
Adam Kortylewski, Qing Liu, Huiyu Wang +2
Deep convolutional neural networks (DCNNs) are powerful models that yield impressive results at object classification. However, recent work has shown that they do not generalize we…
Learning Transferable Adversarial Examples via Ghost Networks
Yingwei Li, Song Bai, Yuyin Zhou +3
Recent development of adversarial attacks has proven that ensemble-based methods outperform traditional, non-ensemble ones in black-box attack. However, as it is computationally pr…
Robust Face Detection via Learning Small Faces on Hard Images
Zhishuai Zhang, Wei Shen, Siyuan Qiao +3
Recent anchor-based deep face detectors have achieved promising performance, but they are still struggling to detect hard faces, such as small, blurred and partially occluded faces…
Adversarial Attacks and Defences Competition
Alexey Kurakin, Ian Goodfellow, Samy Bengio +20
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop…
Deep Co-Training for Semi-Supervised Image Recognition
Siyuan Qiao, Wei Shen, Zhishuai Zhang +2
In this paper, we study the problem of semi-supervised image recognition, which is to learn classifiers using both labeled and unlabeled images. We present Deep Co-Training, a deep…
Improving Transferability of Adversarial Examples with Input Diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou +4
Though CNNs have achieved the state-of-the-art performance on various vision tasks, they are vulnerable to adversarial examples --- crafted by adding human-imperceptible perturbati…
Single-Shot Object Detection with Enriched Semantics
Zhishuai Zhang, Siyuan Qiao, Cihang Xie +3
We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within…
Visual Concepts and Compositional Voting
Jianyu Wang, Zhishuai Zhang, Cihang Xie +5
It is very attractive to formulate vision in terms of pattern theory \cite{Mumford2010pattern}, where patterns are defined hierarchically by compositions of elementary building blo…
Gradually Updated Neural Networks for Large-Scale Image Recognition
Siyuan Qiao, Zhishuai Zhang, Wei Shen +2
Depth is one of the keys that make neural networks succeed in the task of large-scale image recognition. The state-of-the-art network architectures usually increase the depths by c…
Mitigating Adversarial Effects Through Randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang +2
Convolutional neural networks have demonstrated high accuracy on various tasks in recent years. However, they are extremely vulnerable to adversarial examples. For example, imperce…
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
Zhishuai Zhang, Cihang Xie, Jianyu Wang +2
In this paper, we study the task of detecting semantic parts of an object, e.g., a wheel of a car, under partial occlusion. We propose that all models should be trained without see…
Detecting Semantic Parts on Partially Occluded Objects
Jianyu Wang, Cihang Xie, Zhishuai Zhang +3
In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested…
Adversarial Examples for Semantic Segmentation and Object Detection
Cihang Xie, Jianyu Wang, Zhishuai Zhang +3
It has been well demonstrated that adversarial examples, i.e., natural images with visually imperceptible perturbations added, generally exist for deep networks to fail on image cl…
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
Nicolas Papernot, Fartash Faghri, Nicholas Carlini +23
CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be us…
Unsupervised learning of object semantic parts from internal states of CNNs by population encoding
Jianyu Wang, Zhishuai Zhang, Cihang Xie +2
We address the key question of how object part representations can be found from the internal states of CNNs that are trained for high-level tasks, such as object classification. T…