Self-produced Guidance for Weakly-supervised Object Localization

By Xiaolin Zhang, Yunchao Wei, Guoliang Kang, Yi Yang, Thomas S. Huang. 2018

267 citations recorded by the source corpus.

Weakly supervised methods usually generate localization results based on attention maps produced by classification networks. However, the attention maps exhibit the most discriminative parts of the object which are small and sparse. We propose to generate Self-produced Guidance (SPG) masks which separate the foreground i.e., the object of interest, from the background to provide the classification networks with spatial correlation information of pixels. A stagewise approach is proposed to incorporate high confident object regions to learn the SPG masks. The high confident regions within attention maps are utilized to progressively learn the SPG masks. The masks are then used as an auxiliary pixel-level supervision to facilitate the training of classification networks. Extensive experiments on ILSVRC demonstrate that SPG is effective in producing high-quality object localizations maps. Particularly, the proposed SPG achieves the Top-1 localization error rate of 43.83% on the ILSVRC validation set, which is a new state-of-the-art error rate.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features (2019, methodology): “CutMix also achieves comparable localization accuracies on CUB200-2011 and ImageNet, even when compared against the dedicated state-of-the-art WSOL methods [51, 32, 48, 49, 2] that focus on learning spatially dispersed representations.”
Weakly Supervised Scene Parsing with Point-based Distance Metric Learning (2018, background): “And object region mining and localization is experimented in (Wei et al. 2016), (Wei et al. 2017a), (Zhang et al. 2018b) and (Hou et al. 2018).”
Attention-Based Dropout Layer for Weakly Supervised Object Localization (2019, methodology): “We report the accuracy of ACoL and SPG from their original paper.”
Domain Adaptation for Semantic Segmentation With Maximum Squares Loss (2019, methodology): “We apply the idea in weakly-supervised learning [34] to 1 In this paper, the gradient refers to the magnitude of the gradient.”
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection (2018, unknown): “Many methods are emerging for the WSOD problem [13], [14], [33], [34], [35], [36], [37], [38], [39], [40], [58], [59], [60], [61].”
Crossing the Format Boundary of Text and Boxes: Towards Unified Vision-Language Modeling (2021, background): “The ﬁrst is the “GT-known” [12,50,67,68], where models know the ground-truth class to localize.”
LayerCAM: Exploring Hierarchical Class Activation Maps for Localization (2021, methodology): “Recently, a lot of WSOL methods [59], [60], [1], [8], [60], [61], [62], [63] utilizing attention methods have been proposed, such as CAM [1], Grad-CAM [2], and ACoL [8].”
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling (2021, background): “The “GT-known” [57,75,76,15] setting aims to localize a given ground-truth class.”
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation (2018, background): “Zhou [28] and Zhang [26, 27] propose to discover precise object regions using a classification network with only image-level labels.”
Weakly Supervised Object Localization and Detection: A Survey (2021, unknown): “Zhang-ECCV-2018 [212] Inceptionv3+CAM CNN None ImageNet(tag label) Inceptionv3+CAM Self-produced guidance learning General objects (for ILSVRC and CUB)”