283 citations · 814 across the 69 of their papers we have counts for
9 papers · 1 filter
Dual-Stream Transformer for Generic Event Boundary Captioning
Xin Gu, Hanhua Ye, Guang Chen +3
This paper describes our champion solution for the CVPR2022 Generic Event Boundary Captioning (GEBC) competition. GEBC requires the captioning model to have a comprehension of inst…
Augmentation Component Analysis: Modeling Similarity via the Augmentation Overlaps
Lu Han, Han-Jia Ye, De-Chuan Zhan
Self-supervised learning aims to learn a embedding space where semantically similar samples are close. Contrastive learning methods pull views of samples together and push differen…
Generalized Knowledge Distillation via Relationship Matching
Han-Jia Ye, Su Lu, De-Chuan Zhan
The knowledge of a well-trained deep neural network (a.k.a. the "teacher") is valuable for learning similar tasks. Knowledge distillation extracts knowledge from the teacher and in…
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning
Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye +1
Real-world applications require the classification model to adapt to new classes without forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a mode…
Identifying Ambiguous Similarity Conditions via Semantic Matching
Han-Jia Ye, Yi Shi, De-Chuan Zhan
Rich semantics inside an image result in its ambiguous relationship with others, i.e., two images could be similar in one condition but dissimilar in another. Given triplets like "…
Selective Cross-Task Distillation
Su Lu, Han-Jia Ye, De-Chuan Zhan
The outpouring of various pre-trained models empowers knowledge distillation by providing abundant teacher resources, but there lacks a developed mechanism to utilize these teacher…
FOSTER: Feature Boosting and Compression for Class-Incremental Learning
Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye +1
The ability to learn new concepts continually is necessary in this ever-changing world. However, deep neural networks suffer from catastrophic forgetting when learning new categori…
Forward Compatible Few-Shot Class-Incremental Learning
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye +3
Novel classes frequently arise in our dynamically changing world, e.g., new users in the authentication system, and a machine learning model should recognize new classes without fo…
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks
Da-Wei Zhou, Han-Jia Ye, Liang Ma +3
New classes arise frequently in our ever-changing world, e.g., emerging topics in social media and new types of products in e-commerce. A model should recognize new classes and mea…