Attentive Recurrent Neural Network for Weak-supervised Multi-label Image Classification

By Liang Li, Shuhui Wang, Shuqiang Jiang, Qingming Huang. 2018

39 citations recorded by the source corpus.

Multi-label image classification is a fundamental and challenging task in computer vision, and recently achieved significant progress by exploiting semantic relations among labels. However, the spatial positions of labels for multi-labels images are usually not provided in real scenarios, which brings insuperable barrier to conventional models. In this paper, we propose an end-to-end attentive recurrent neural network for multi-label image classification under only image-level supervision, which learns the discriminative feature representations and models the label relations simultaneously. First, inspired by attention mechanism, we propose a recurrent highlight network (RHN) which focuses on the most related regions in the image to learn the discriminative feature representations for different objects in an iterative manner. Second, we develop a gated recurrent relation extractor (GRRE) to model the label relations using multiplicative gates in a recurrent fashion, which learns to decide how multiple labels of the image influence the relation extraction. Extensive experiments on three benchmark datasets show that our model outperforms the state-of-the-arts, and performs better on small-object categories and under the scenario with large number of labels.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Elucidating image-to-set prediction: An analysis of models, losses and datasets (2019, background): “To overcome the scalability shortcoming while still modeling label co-occurrences, probabilistic classifier chains [9] and recurrent neural network-based approaches [48, 34, 23, 27] decompose the joint distribution into conditionals at the expense of introducing intrinsic label ordering during train”
Rethinking Graph Neural Architecture Search from Message-passing (2021, background): “language tasks [15, 20, 36, 38], including multi-label object recognition [16, 32], detection [8], and sequence prediction [24].”