7.5k citations · 21.2k across the 43 of their papers we have counts for
5 papers · 2 filters
Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net
Fangting Xia, Peng Wang, Liang-Chieh Chen +1
Parsing articulated objects, e.g. humans and animals, into semantic parts (e.g. body, head and arms, etc.) from natural images is a challenging and fundamental problem for computer…
ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering
Kan Chen, Jiang Wang, Liang-Chieh Chen +3
We propose a novel attention based deep learning architecture for visual question answering task (VQA). Given an image and an image related natural language question, VQA generates…
Attention to Scale: Scale-aware Semantic Image Segmentation
Liang-Chieh Chen, Yi Yang, Jiang Wang +2
Incorporating multi-scale features in fully convolutional neural networks (FCNs) has been a key element to achieving state-of-the-art performance on semantic image segmentation. On…
Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform
Liang-Chieh Chen, Jonathan T. Barron, George Papandreou +2
Deep convolutional neural networks (CNNs) are the backbone of state-of-art semantic image segmentation systems. Recent work has shown that complementing CNNs with fully-connected c…
Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation
George Papandreou, Liang-Chieh Chen, Kevin Murphy +1
Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic i…