Featured Research Projects
Learning Multiple Dense Prediction Tasks from Partially Annotated Data
CVPR 2022 (Oral, Best Paper Nominee)
A framework for learning multiple dense prediction tasks when only partial annotations are available for each task.
Cross-domain Few-shot Learning with Task-specific Adapters
CVPR 2022
Task-specific adapters enable effective few-shot learning across different domains with minimal parameter updates.
Universal Representation Learning from Multiple Domains for Few-shot Classification
ICCV 2021
Learning universal feature representations that generalize across multiple domains for few-shot classification tasks.
Learning to Impute: A General Framework for Semi-supervised Learning
Preprint 2019
A general framework that learns to impute missing features for semi-supervised learning scenarios.
Knowledge Distillation for Multi-task Learning
ECCV Workshop 2020
Applying knowledge distillation techniques to improve multi-task learning performance and efficiency.
Learning to Learn Relation for Important People Detection in Still Images
CVPR 2019
Meta-learning approach for detecting important people in images by learning relational patterns.
PersonRank: Detecting Important People in Images
FG 2018 (Oral)
A ranking-based approach for automatically identifying the most important people in group photos.
One-pass Person Re-identification by Sketched Online Discriminant Analysis
PR 2019
Efficient online person re-identification using sketched discriminant analysis for real-time applications.