50 citations · 74 across the 8 of their papers we have counts for
6 papers · 1 filter
Convolutional Fine-Grained Classification with Self-Supervised Target Relation Regularization
Kangjun Liu, Ke Chen, Kui Jia
Fine-grained visual classification can be addressed by deep representation learning under supervision of manually pre-defined targets (e.g., one-hot or the Hadamard codes). Such ta…
Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering
Hui Tang, Xiatian Zhu, Ke Chen +2
Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain, given labeled data on a source domain whose dist…
MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views
Ke Chen, Ryan Oldja, Nikolai Smolyanskiy +5
Autonomous driving requires the inference of actionable information such as detecting and classifying objects, and determining the drivable space. To this end, we present Multi-Vie…
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing
Yixiong Zou, Shanghang Zhang, Ke Chen +3
Few-shot learning (FSL) aims at recognizing novel classes given only few training samples, which still remains a great challenge for deep learning. However, humans can easily recog…
Cascading Convolutional Color Constancy
Huanglin Yu, Ke Chen, Kaiqi Wang +3
Regressing the illumination of a scene from the representations of object appearances is popularly adopted in computational color constancy. However, it's still challenging due to…
Deep Structured-Output Regression Learning for Computational Color Constancy
Yanlin Qian, Ke Chen, Joni-Kristian Kamarainen +2
Computational color constancy that requires esti- mation of illuminant colors of images is a fundamental yet active problem in computer vision, which can be formulated into a regre…