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

• URL: http://arxiv.org/abs/2308.00093v1
• Date: Fri, 28 Jul 2023 08:40:23 GMT
• Title: Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification
• Authors: SuBeen Lee, WonJun Moon, Hyun Seok Seong, and Jae-Pil Heo
• Abstract summary: Task Discrepancy Maximization (TDM) is a task-oriented channel attention method tailored for fine-grained few-shot classification. 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.
• Score: 5.4352987210173955
• License: http://creativecommons.org/licenses/by/4.0/
• 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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