Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification

SuBeen Lee; WonJun Moon; Hyun Seok Seong; Jae-Pil Heo

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arXiv · 2308.00093

Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification

SuBeen Lee·WonJun Moon·Hyun Seok Seong·Jae-Pil Heo

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Abstract

The difficulty of the fine-grained image classification mainly comes from a shared overall appearance across classes. Thus, recognizing discriminative details, such as eyes and beaks for birds, is a key in the task. However, this is particularly challenging when training data is limited. To address this, we propose Task Discrepancy Maximization (TDM), a task-oriented channel attention method tailored for fine-grained few-shot classification with two novel modules Support Attention Module (SAM) and Query Attention Module (QAM). SAM highlights channels encoding class-wise discriminative features, while QAM assigns higher weights to object-relevant channels of the query. Based on these submodules, TDM produces task-adaptive features by focusing on channels encoding class-discriminative details and possessed by the query at the same time, for accurate class-sensitive similarity measure between support and query instances. While TDM influences high-level feature maps by task-adaptive calibration of channel-wise importance, we further introduce Instance Attention Module (IAM) operating in intermediate layers of feature extractors to instance-wisely highlight object-relevant channels, by extending QAM. The merits of TDM and IAM and their complementary benefits are experimentally validated in fine-grained few-shot classification tasks. Moreover, IAM is also shown to be effective in coarse-grained and cross-domain few-shot classifications.

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SuBeen Lee, WonJun Moon, Hyun Seok Seong, Jae-Pil Heo. 2023-07-28. Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification. https://arxiv.org/abs/2308.00093

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