NCL++: Nested Collaborative Learning for Long-Tailed Visual Recognition

• URL: http://arxiv.org/abs/2306.16709v3
• Date: Thu, 18 Jul 2024 02:38:49 GMT
• Title: NCL++: Nested Collaborative Learning for Long-Tailed Visual Recognition
• Authors: Zichang Tan, Jun Li, Jinhao Du, Jun Wan, Zhen Lei, Guodong Guo,
• Abstract summary: We propose a Nested Collaborative Learning (NCL++) which tackles the long-tailed learning problem by a collaborative learning. To achieve the collaborative learning in long-tailed learning, the balanced online distillation is proposed. In order to improve the meticulous distinguishing ability on the confusing categories, we further propose a Hard Category Mining.
• Score: 63.90327120065928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-tailed visual recognition has received increasing attention in recent years. Due to the extremely imbalanced data distribution in long-tailed learning, the learning process shows great uncertainties. For example, the predictions of different experts on the same image vary remarkably despite the same training settings. To alleviate the uncertainty, we propose a Nested Collaborative Learning (NCL++) which tackles the long-tailed learning problem by a collaborative learning. To be specific, the collaborative learning consists of two folds, namely inter-expert collaborative learning (InterCL) and intra-expert collaborative learning (IntraCL). In-terCL learns multiple experts collaboratively and concurrently, aiming to transfer the knowledge among different experts. IntraCL is similar to InterCL, but it aims to conduct the collaborative learning on multiple augmented copies of the same image within the single expert. To achieve the collaborative learning in long-tailed learning, the balanced online distillation is proposed to force the consistent predictions among different experts and augmented copies, which reduces the learning uncertainties. Moreover, in order to improve the meticulous distinguishing ability on the confusing categories, we further propose a Hard Category Mining (HCM), which selects the negative categories with high predicted scores as the hard categories. Then, the collaborative learning is formulated in a nested way, in which the learning is conducted on not just all categories from a full perspective but some hard categories from a partial perspective. Extensive experiments manifest the superiority of our method with outperforming the state-of-the-art whether with using a single model or an ensemble. The code will be publicly released.

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