28 citations · 156 across the 18 of their papers we have counts for
18 papers
RALACs: Action Recognition in Autonomous Vehicles using Interaction Encoding and Optical Flow
Eddy Zhou, Alex Zhuang, Alikasim Budhwani +6
When applied to autonomous vehicle (AV) settings, action recognition can enhance an environment model's situational awareness. This is especially prevalent in scenarios where tradi…
RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features
Gang Zhang, Xin Lu, Jingru Tan +4
The two-stage methods for instance segmentation, e.g. Mask R-CNN, have achieved excellent performance recently. However, the segmented masks are still very coarse due to the downsa…
Fixing the Teacher-Student Knowledge Discrepancy in Distillation
Jiangfan Han, Mengya Gao, Yujie Wang +3
Training a small student network with the guidance of a larger teacher network is an effective way to promote the performance of the student. Despite the different types, the guide…
Differentiable Network Adaption with Elastic Search Space
Shaopeng Guo, Yujie Wang, Kun Yuan +1
In this paper we propose a novel network adaption method called Differentiable Network Adaption (DNA), which can adapt an existing network to a specific computation budget by adjus…
Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection
Jingru Tan, Xin Lu, Gang Zhang +2
Recently proposed decoupled training methods emerge as a dominant paradigm for long-tailed object detection. But they require an extra fine-tuning stage, and the disjointed optimiz…
Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks
Kun Yuan, Quanquan Li, Dapeng Chen +2
One practice of employing deep neural networks is to apply the same architecture to all the input instances. However, a fixed architecture may not be representative enough for data…
MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection
Xin Lu, Quanquan Li, Buyu Li +1
Modern object detection methods can be divided into one-stage approaches and two-stage ones. One-stage detectors are more efficient owing to straightforward architectures, but the…
1st Place Solution of LVIS Challenge 2020: A Good Box is not a Guarantee of a Good Mask
Jingru Tan, Gang Zhang, Hanming Deng +4
This article introduces the solutions of the team lvisTraveler for LVIS Challenge 2020. In this work, two characteristics of LVIS dataset are mainly considered: the long-tailed dis…
Learning Connectivity of Neural Networks from a Topological Perspective
Kun Yuan, Quanquan Li, Jing Shao +1
Seeking effective neural networks is a critical and practical field in deep learning. Besides designing the depth, type of convolution, normalization, and nonlinearities, the topol…
DMCP: Differentiable Markov Channel Pruning for Neural Networks
Shaopeng Guo, Yujie Wang, Quanquan Li +1
Recent works imply that the channel pruning can be regarded as searching optimal sub-structure from unpruned networks. However, existing works based on this observation require tra…
Equalization Loss for Long-Tailed Object Recognition
Jingru Tan, Changbao Wang, Buyu Li +4
Object recognition techniques using convolutional neural networks (CNN) have achieved great success. However, state-of-the-art object detection methods still perform poorly on larg…
Residual Knowledge Distillation
Mengya Gao, Yujun Shen, Quanquan Li +1
Knowledge distillation (KD) is one of the most potent ways for model compression. The key idea is to transfer the knowledge from a deep teacher model (T) to a shallower student (S)…
Equalization Loss for Large Vocabulary Instance Segmentation
Jingru Tan, Changbao Wang, Quanquan Li +1
Recent object detection and instance segmentation tasks mainly focus on datasets with a relatively small set of categories, e.g. Pascal VOC with 20 classes and COCO with 80 classes…
Grid R-CNN Plus: Faster and Better
Xin Lu, Buyu Li, Yuxin Yue +2
Grid R-CNN is a well-performed objection detection framework. It transforms the traditional box offset regression problem into a grid point estimation problem. With the guidance of…
WIDER Face and Pedestrian Challenge 2018: Methods and Results
Chen Change Loy, Dahua Lin, Wanli Ouyang +49
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurat…
An Embarrassingly Simple Approach for Knowledge Distillation
Mengya Gao, Yujun Shen, Quanquan Li +5
Knowledge Distillation (KD) aims at improving the performance of a low-capacity student model by inheriting knowledge from a high-capacity teacher model. Previous KD methods typica…
Grid R-CNN
Xin Lu, Buyu Li, Yuxin Yue +2
This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditi…
POI: Multiple Object Tracking with High Performance Detection and Appearance Feature
Fengwei Yu, Wenbo Li, Quanquan Li +3
Detection and learning based appearance feature play the central role in data association based multiple object tracking (MOT), but most recent MOT works usually ignore them and on…