← 研踪 PaperTrailCVF · CVPR 2026:cvf:b3aa605bb96dfb8659cea11b0cc3bf50 · 记录 #1
Trust-calibrated Collaborative Learning for Long-Tailed Visual Recognition
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 23:58
摘要
Real-world visual recognition faces the fundamental challenge of long-tailed distributions. While state-of-the-art methods often employ multi-expert models to address different frequency categories, we find that the mutual knowledge distillation used in these models enhances collaboration at the cost of introducing two critical limitations: indiscriminate knowledge transfer leads to bias propagation, where a single expert's error can spread and contaminate others, and error consolidation, where mutual reinforcement of incorrect predictions solidifies erroneous consensus. To overcome these issues, we propose Trust-calibrated Collaborative Learning (TCL). Our framework introduces the trustworthy knowledge orchestration module, which enables reliable distillation and precise collaboration through a knowledge quality gate that blocks erroneous information and a tail-class compensation mechanism that alleviates knowledge scarcity for tail categories. Furthermore, we design a consensus error calibration module that suppresses consensus high-confidence negative classes to correct collective misjudgments and steer optimization in the right direction. Extensive experiments on five long-tailed benchmarks demonstrate that TCL achieves the best performance, raising Top-1 accuracy on CIFAR100-LT to 58.7%, a gain of 2.4% over previous SOTA methods.
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记录 #1 · 2026 · 最新观测版本
Trust-calibrated Collaborative Learning for Long-Tailed Visual Recognition
Real-world visual recognition faces the fundamental challenge of long-tailed distributions. While state-of-the-art methods often employ multi-expert models to address different frequency categories, we find that the mutual knowledge distillation used in these models enhances collaboration at the cost of introducing two critical limitations: indiscriminate knowledge transfer leads to bias propagation, where a single expert's error can spread and contaminate others, and error consolidation, where mutual reinforcement of incorrect predictions solidifies erroneous consensus. To overcome these issues, we propose Trust-calibrated Collaborative Learning (TCL). Our framework introduces the trustworthy knowledge orchestration module, which enables reliable distillation and precise collaboration through a knowledge quality gate that blocks erroneous information and a tail-class compensation mechanism that alleviates knowledge scarcity for tail categories. Furthermore, we design a consensus error calibration module that suppresses consensus high-confidence negative classes to correct collective misjudgments and steer optimization in the right direction. Extensive experiments on five long-tailed benchmarks demonstrate that TCL achieves the best performance, raising Top-1 accuracy on CIFAR100-LT to 58.7%, a gain of 2.4% over previous SOTA methods.
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