Interaction-And-Aggregation Network for Person Re-Identification

By Rui Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen. Computer Vision and Pattern Recognition, 2019

392 citations recorded by the source corpus.

Person re-identification (reID) benefits greatly from deep convolutional neural networks (CNNs) which learn robust feature embeddings. However, CNNs are inherently limited in modeling the large variations in person pose and scale due to their fixed geometric structures. In this paper, we propose a novel network structure, Interaction-and-Aggregation (IA), to enhance the feature representation capability of CNNs. Firstly, Spatial IA (SIA) module is introduced. It models the interdependencies between spatial features and then aggregates the correlated features corresponding to the same body parts. Unlike CNNs which extract features from fixed rectangle regions, SIA can adaptively determine the receptive fields according to the input person pose and scale. Secondly, we introduce Channel IA (CIA) module which selectively aggregates channel features to enhance the feature representation, especially for small-scale visual cues. Further, IA network can be constructed by inserting IA blocks into CNNs at any depth. We validate the effectiveness of our model for person reID by demonstrating its superiority over state-of-the-art methods on three benchmark datasets.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/cca9d893b9ce6f21e2565442680690b99c74fa8f

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Citations in the corpus

Cross-Modality Person Re-Identification With Shared-Specific Feature Transfer (2020, background): “The recent works are mainly based on deep learning for more discriminative features [6, 18, 48, 55].”
Deep Learning for Person Re-Identification: A Survey and Outlook (2020, methodology): “An Interaction-and-Aggregation (IA) [103] models the inter-dependencies between spatial”
DSSL: Deep Surroundings-person Separation Learning for Text-based Person Retrieval (2021, background): “Hou et al. [7] proposed an Interaction-and-Aggregation (IA) Block, which consists of Spatial Interaction-and-Aggregation (SIA) and Channel Interaction-and-Aggregation (CIA) Modules to strengthen the representation capability of the deep neural network.”
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification (2021, neutral): “4 IANet [17] 94.”
Cross Attention Network for Few-shot Classification (2019, background): “It has achieved great success in computer vision applications, such as image classification [12, 41, 27], person re-identification [10, 47, 11], image caption [29, 44, 4] and visual question answering [43, 45, 46, 30].”