1 citations · 2 across the 8 of their papers we have counts for
8 papers
BAPE: Learning an Explicit Bayes Classifier for Long-tailed Visual Recognition
Chaoqun Du, Yulin Wang, Shiji Song +1
Bayesian decision theory advocates the Bayes classifier as the optimal approach for minimizing the risk in machine learning problems. Current deep learning algorithms usually solve…
OStr-DARTS: Differentiable Neural Architecture Search based on Operation Strength
Le Yang, Ziwei Zheng, Yizeng Han +3
Differentiable architecture search (DARTS) has emerged as a promising technique for effective neural architecture search, and it mainly contains two steps to find the high-performa…
UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation
Chaoqun Du, Yulin Wang, Jiayi Guo +3
Test-Time Adaptation (TTA) aims to adapt pre-trained models to the target domain during testing. In reality, this adaptability can be influenced by multiple factors. Researchers ha…
Rethinking the Architecture Design for Efficient Generic Event Boundary Detection
Ziwei Zheng, Zechuan Zhang, Yulin Wang +3
Generic event boundary detection (GEBD), inspired by human visual cognitive behaviors of consistently segmenting videos into meaningful temporal chunks, finds utility in various ap…
DyFADet: Dynamic Feature Aggregation for Temporal Action Detection
Le Yang, Ziwei Zheng, Yizeng Han +4
Recent proposed neural network-based Temporal Action Detection (TAD) models are inherently limited to extracting the discriminative representations and modeling action instances wi…
EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training
Yulin Wang, Yang Yue, Rui Lu +3
The superior performance of modern visual backbones usually comes with a costly training procedure. We contribute to this issue by generalizing the idea of curriculum learning beyo…
Probabilistic Contrastive Learning for Long-Tailed Visual Recognition
Chaoqun Du, Yulin Wang, Shiji Song +1
Long-tailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Such imbalance issue considerably i…
SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning
Chaoqun Du, Yizeng Han, Gao Huang
Recent advancements in semi-supervised learning have focused on a more realistic yet challenging task: addressing imbalances in labeled data while the class distribution of unlabel…