284 citations · 916 across the 9 of their papers we have counts for
9 papers
From IC Layout to Die Photo: A CNN-Based Data-Driven Approach
Hao-Chiang Shao, Chao-Yi Peng, Jun-Rei Wu +4
We propose a deep learning-based data-driven framework consisting of two convolutional neural networks: i) LithoNet that predicts the shape deformations on a circuit due to IC fabr…
An End-to-End Network for Panoptic Segmentation
Huanyu Liu, Chao Peng, Changqian Yu +4
Panoptic segmentation, which needs to assign a category label to each pixel and segment each object instance simultaneously, is a challenging topic. Traditionally, the existing app…
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
Changqian Yu, Jingbo Wang, Chao Peng +3
Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inf…
Learning a Discriminative Feature Network for Semantic Segmentation
Changqian Yu, Jingbo Wang, Chao Peng +3
Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, w…
DetNet: A Backbone network for Object Detection
Zeming Li, Chao Peng, Gang Yu +3
Recent CNN based object detectors, no matter one-stage methods like YOLO, SSD, and RetinaNe or two-stage detectors like Faster R-CNN, R-FCN and FPN are usually trying to directly f…
ExFuse: Enhancing Feature Fusion for Semantic Segmentation
Zhenli Zhang, Xiangyu Zhang, Chao Peng +2
Modern semantic segmentation frameworks usually combine low-level and high-level features from pre-trained backbone convolutional models to boost performance. In this paper, we fir…
Light-Head R-CNN: In Defense of Two-Stage Object Detector
Zeming Li, Chao Peng, Gang Yu +3
In this paper, we first investigate why typical two-stage methods are not as fast as single-stage, fast detectors like YOLO and SSD. We find that Faster R-CNN and R-FCN perform an…
MegDet: A Large Mini-Batch Object Detector
Chao Peng, Tete Xiao, Zeming Li +5
The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network,…
Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network
Chao Peng, Xiangyu Zhang, Gang Yu +2
One of recent trends [30, 31, 14] in network architec- ture design is stacking small filters (e.g., 1x1 or 3x3) in the entire network because the stacked small filters is more ef-…