74 citations · 95 across the 10 of their papers we have counts for
12 papers
Cross-Domain Video Anomaly Detection without Target Domain Adaptation
Abhishek Aich, Kuan-Chuan Peng, Amit K. Roy-Chowdhury
Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume that at least few task-relevant target domain training data are available for adaptation from the source t…
Are Deep Neural Networks SMARTer than Second Graders?
Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit +2
Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generati…
Cross-Modal Knowledge Transfer Without Task-Relevant Source Data
Sk Miraj Ahmed, Suhas Lohit, Kuan-Chuan Peng +2
Cost-effective depth and infrared sensors as alternatives to usual RGB sensors are now a reality, and have some advantages over RGB in domains like autonomous navigation and remote…
Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition
Lipeng Ke, Kuan-Chuan Peng, Siwei Lyu
Graph Convolutional Networks (GCNs) have been widely used to model the high-order dynamic dependencies for skeleton-based action recognition. Most existing approaches do not explic…
Iterative Self Knowledge Distillation -- From Pothole Classification to Fine-Grained and COVID Recognition
Kuan-Chuan Peng
Pothole classification has become an important task for road inspection vehicles to save drivers from potential car accidents and repair bills. Given the limited computational powe…
ViewSynth: Learning Local Features from Depth using View Synthesis
Jisan Mahmud, Rajat Vikram Singh, Peri Akiva +3
The rapid development of inexpensive commodity depth sensors has made keypoint detection and matching in the depth image modality an important problem in computer vision. Despite g…
Attention Guided Anomaly Localization in Images
Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh +1
Anomaly localization is an important problem in computer vision which involves localizing anomalous regions within images with applications in industrial inspection, surveillance,…
Learning without Memorizing
Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng +2
Incremental learning (IL) is an important task aimed at increasing the capability of a trained model, in terms of the number of classes recognizable by the model. The key problem i…
Sharpen Focus: Learning with Attention Separability and Consistency
Lezi Wang, Ziyan Wu, Srikrishna Karanam +4
Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks. Despite good localizatio…
Tell Me Where to Look: Guided Attention Inference Network
Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng +2
Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients. These attention maps…
Learning Compositional Visual Concepts with Mutual Consistency
Yunye Gong, Srikrishna Karanam, Ziyan Wu +3
Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data. For instance, we m…
Zero-Shot Deep Domain Adaptation
Kuan-Chuan Peng, Ziyan Wu, Jan Ernst
Domain adaptation is an important tool to transfer knowledge about a task (e.g. classification) learned in a source domain to a second, or target domain. Current approaches assume…