Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature maps directly. We discover that due to the operation of stacking local receptive filed, Convolutional Neural Network causes the discriminative region diffusion in high-level feature maps, which leads to inaccurate discriminative region localization. In this paper, we propose an end-to-end Discriminative Feature-oriented Gaussian Mixture Model (DF-GMM), to address the problem of discriminative region diffusion and find better fine-grained details. Specifically, DF-GMM consists of 1) a low-rank representation mechanism (LRM), which learns a set of low-rank discriminative bases by Gaussian Mixture Model (GMM) to accurately select discriminative details and filter more irrelevant information in high-level semantic feature maps, 2) a low-rank representation reorganization mechanism (LR2M) which resumes the space information of low-rank discriminative bases to reconstruct the low-rank feature maps. By recovering the low-rank discriminative bases into the same embedding space of highlevel feature maps, LR2M alleviates the discriminative region diffusion problem in high-level feature map and discriminative regions can be located more precisely on the new low-rank feature maps. Extensive experiments verify that DF-GMM yields the best performance under the same settings with the most competitive approaches, in CUBBird, Stanford-Cars datasets, and FGVC Aircraft.

Existing semantic segmentation models are trapped in the categories of training sets. Unfortunately, public datasets provide pixel-level annotations only for a small quantity of images and few categories. In this paper, we propose a novel cross-category supervised object segmentation network to explore the similar features among different categories, which can transfer the learned segmentation knowledge from categories with mask annotations to unseen categories that only have bounding boxes. Specifically, we fuse gaussian attention map of an object with guided gradient back-propagation map as an extra input, which gives localizable and discriminative prior cues to obtain precise object segmentation from the bounding box. In addition to computing a segment for each box, we also fuse segments to generate pixel-level labels. Then, without modifying the segmentation training program, the generated labels are still sufficient and achieve about 98.2% of the fully supervised model, in the case of only 50% categories with pixel-level annotations on PASCAL VOC 2012. The proposed method is also effective for interactive segmentation and salient object detection.

Weakly supervised fine-grained image classification (WFGIC) aims at learning to recognize hundreds of subcategories in each basic-level category with only image level labels available. It is extremely challenging and existing methods mainly focus on the discriminative semantic parts or regions localization as the key differences among different subcategories are subtle and local. However, they localize these regions independently while neglecting the fact that regions are mutually correlated and region groups can be more discriminative. Meanwhile, most current work tends to derive features directly from the output of CNN and rarely considers the correlation within the feature vector. To address these issues, we propose an end-to-end Correlation-guided Discriminative Learning (CDL) model to fully mine and exploit the discriminative potentials of correlations for WFGIC globally and locally. From the global perspective, a discriminative region grouping (DRG) sub-network is proposed which first establishes correlation between regions and then enhances each region by weighted aggregating all the correlation from other regions to it. By this means each region's representation encodes the global image-level context and thus is more robust; meanwhile, through learning the correlation between discriminative regions, the network is guided to implicitly discover the discriminative region groups which are more powerful for WFGIC. From the local perspective, a discriminative feature strengthening sub-network (DFS) is proposed to mine and learn the internal spatial correlation among elements of each patch's feature vector, to improve its discriminative power locally by jointly emphasizes informative elements while suppresses the useless ones. Extensive experiments demonstrate the effectiveness of proposed DRG and DFS sub-networks, and show that the CDL model achieves state-of-the-art performance both in accuracy and efficiency.