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Uncertainty-aware Long-tailed Weights Model the Utility of Pseudo-labels for Semi-supervised Learning
Jiaqi Wu, Junbiao Pang, Qingming Huang
Current Semi-supervised Learning (SSL) adopts the pseudo-labeling strategy and further filters pseudo-labels based on confidence thresholds. However, this mechanism has notable dra…
Decorrelating Structure via Adapters Makes Ensemble Learning Practical for Semi-supervised Learning
Jiaqi Wu, Junbiao Pang, Qingming Huang
In computer vision, traditional ensemble learning methods exhibit either a low training efficiency or the limited performance to enhance the reliability of deep neural networks. In…
Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection
Junbiao Pang, Baocheng Xiong, Jiaqi Wu
Crack detection has become an indispensable, interesting yet challenging task in the computer vision community. Specially, pavement cracks have a highly complex spatial structure,…
A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification
Jiaqi Wu, Junbiao Pang, Baochang Zhang +1
Semi-supervised learning (SSL) is a practical challenge in computer vision. Pseudo-label (PL) methods, e.g., FixMatch and FreeMatch, obtain the State Of The Art (SOTA) performances…
In-Distribution Consistency Regularization Improves the Generalization of Quantization-Aware Training
Junbiao Pang, Tianyang Cai, Baochang Zhang +1
Although existing Quantization-Aware Training (QAT) methods intensively depend on knowledge distillation to guarantee performance, QAT still suffers from severe performance drop. T…
Generating Unbiased Pseudo-labels via a Theoretically Guaranteed Chebyshev Constraint to Unify Semi-supervised Classification and Regression
Jiaqi Wu, Junbiao Pang, Qingming Huang
Both semi-supervised classification and regression are practically challenging tasks for computer vision. However, semi-supervised classification methods are barely applied to regr…