activity
20182022
most citedIterative Self Knowledge Distillation -- From Pothole Classification to Fine-Grained and COVID Recognition

1 citations · 1 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CV2022

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…

cs.CV2022

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…

cs.CV20221 cited

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…

cs.CV2019

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…

cs.CV2019

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,…

cs.CV2018

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…