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Works78 from public data
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks76,422
This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification21,102
This work proposes a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit and derives a robust initialization method that particularly considers the rectifier nonlinearities.
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- 6,240
- 4,187
- Cascaded Pyramid Network for Multi-person Pose Estimation1,799
A novel network structure called Cascaded Pyramid Network (CPN) is presented which targets to relieve the problem from these "hard" keypoints, with state-of-art results on the COCO keypoint benchmark, with average precision at 73.0.
- Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network1,671
This work proposes a Global Convolutional Network to address both the classification and localization issues for the semantic segmentation and suggests a residual-based boundary refinement to further refine the object boundaries.
- Convolutional neural networks at constrained time cost1,509
This paper investigates the accuracy of CNNs under constrained time cost, and presents an architecture that achieves very competitive accuracy in the ImageNet dataset, yet is 20% faster than “AlexNet” [14] (16.0% top-5 error, 10-view test).
- Instance-Aware Semantic Segmentation via Multi-task Network Cascades1,279
This paper presents Multitask Network Cascades for instance-aware semantic segmentation, which consists of three networks, respectively differentiating instances, estimating masks, and categorizing objects, and develops an algorithm for the nontrivial end-to-end training of this causal, cascaded structure.
- ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation1,154
This paper proposes to use scribbles to annotate images, and develops an algorithm to train convolutional networks for semantic segmentation supervised by scribbles, which shows excellent results on the PASCALCONTEXT dataset thanks to extra inexpensive scribble annotations.
- BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation1,125
This paper proposes a method that achieves competitive accuracy but only requires easily obtained bounding box annotations, and yields state-of-the-art results on PASCAL VOC 2012 and PASCal-CONTEXT.
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- Convolutional feature masking for joint object and stuff segmentation456
This paper proposes a joint method to handle objects and “stuff” (e.g., grass, sky, water) in the same framework and presents state-of-the-art results on benchmarks of PASCAL VOC and new PASCal-CONTEXT.
- 415
- Instance-Sensitive Fully Convolutional Networks413
This paper develops FCNs that are capable of proposing instance-level segment candidates that do not have any high-dimensional layer related to the mask resolution, but instead exploits image local coherence for estimating instances.
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- MegDet: A Large Mini-Batch Object Detector338
This paper proposes a Large Mini-Batch Object Detector (MegDet) to enable the training with a large minibatch size up to 256, so that it can effectively utilize at most 128 GPUs to significantly shorten the training time.
- Efficient and accurate approximations of nonlinear convolutional networks270
This paper aims to accelerate the test-time computation of deep convolutional neural networks (CNNs), and takes the nonlinear units into account, subject to a low-rank constraint which helps to reduce the complexity of filters.
- 259
- Learning Delicate Local Representations for Multi-person Pose Estimation242
This paper proposes an efficient attention mechanism - Pose Refine Machine (PRM) to make a trade-off between local and global representations in output features and further refine the keypoint locations.
- 216
- Learning Dynamic Routing for Semantic Segmentation190
A conceptually new method to alleviate the scale variance in semantic representation, named dynamic routing, which generates data-dependent routes, adapting to the scale distribution of each image, and compares with several static architectures, which can be modeled as special cases in the routing space.
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- Supervised Transformer Network for Efficient Face Detection176
A new cascaded Convolutional Neural Network, dubbed the name Supervised Transformer Network, to address the challenge of large pose variations in real-word face detection, which achieves state-of-the-art detection accuracies on several public benchmarks.
- 154
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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