Fine-grained Classification via Categorical Memory Networks

• URL: http://arxiv.org/abs/2012.06793v1
• Date: Sat, 12 Dec 2020 11:50:13 GMT
• Title: Fine-grained Classification via Categorical Memory Networks
• Authors: Weijian Deng, Joshua Marsh, Stephen Gould, Liang Zheng
• Abstract summary: We present a class-specific memory module for fine-grained feature learning. The memory module stores the prototypical feature representation for each category as a moving average. We integrate our class-specific memory module into a standard convolutional neural network, yielding a Categorical Memory Network.
• Score: 42.413523046712896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motivated by the desire to exploit patterns shared across classes, we present a simple yet effective class-specific memory module for fine-grained feature learning. The memory module stores the prototypical feature representation for each category as a moving average. We hypothesize that the combination of similarities with respect to each category is itself a useful discriminative cue. To detect these similarities, we use attention as a querying mechanism. The attention scores with respect to each class prototype are used as weights to combine prototypes via weighted sum, producing a uniquely tailored response feature representation for a given input. The original and response features are combined to produce an augmented feature for classification. We integrate our class-specific memory module into a standard convolutional neural network, yielding a Categorical Memory Network. Our memory module significantly improves accuracy over baseline CNNs, achieving competitive accuracy with state-of-the-art methods on four benchmarks, including CUB-200-2011, Stanford Cars, FGVC Aircraft, and NABirds.

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