The key to solving fine-grained image categorization is finding discriminate and local regions that correspond to subtle visual traits. Great strides have been made, with complex networks designed specifically to learn part-level discriminate feature representations. In this paper, we show that it is possible to cultivate subtle details without the need for overly complicated network designs or training mechanisms – a single loss is all it takes. The main trick lies with how we delve into individual feature channels early on, as opposed to the convention of starting from a consolidated feature map. The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component. The discriminality component forces all feature channels belonging to the same class to be discriminative, through a novel channel-wise attention mechanism. The diversity component additionally constraints channels so that they become mutually exclusive across the spatial dimension. The end result is therefore a set of feature channels, each of which reflects different locally discriminative regions for a specific class. The MC-Loss can be trained end-to-end, without the need for any bounding-box/part annotations, and yields highly discriminative regions during inference. Experimental results show our MC-Loss when implemented on top of common base networks can achieve state-of-the-art performance on all four fine-grained categorization datasets (CUB-Birds, FGVC-Aircraft, Flowers-102, and Stanford Cars). Ablative studies further demonstrate the superiority of the MC-Loss when compared with other recently proposed general-purpose losses for visual classification, on two different base networks.

Fine-grained visual classification (FGVC) is much more challenging than traditional classification tasks due to the inherently subtle intra-class object variations. Recent works are mainly part-driven (either explicitly or implicitly), with the assumption that fine-grained information naturally rests within the parts. In this paper, we take a different stance, and show that part operations are not strictly necessary – the key lies with encouraging the network to learn at different granularities and progressively fusing multi-granularity features together. In particular, we propose: (i) a progressive training strategy that effectively fuses features from different granularities, and (ii) a consistent block convolution that encourages the network to learn the category-consistent features at specific granularities. We evaluate on several standard FGVC benchmark datasets, and demonstrate the proposed method consistently outperforms existing alternatives or delivers competitive results. Codes are available at https://github.com/PRIS-CV/PMG-V2 .

Collaborative representation methods, such as sparse subspace clustering (SSC) and sparse representation-based classification (SRC), have achieved great success in face clustering and classification by directly utilizing the training images as the dictionary bases. In this paper, we reveal that the superior performance of collaborative representation relies heavily on the sufficiently large class separability of the controlled face datasets such as Extended Yale B. On the uncontrolled or undersampled dataset, however, collaborative representation suffers from the misleading coefficients of the incorrect classes. To address this limitation, inspired by the success of linear discriminant analysis (LDA), we develop a superposed linear representation classifier (SLRC) to cast the recognition problem by representing the test image in term of a superposition of the class centroids and the shared intra-class differences. In spite of its simplicity and approximation, the new SLRC largely improves the generalization ability of collaborative representation, and competes well with more sophisticated dictionary learning techniques, on the experiments of AR and FRGC databases. Enforced with the sparsity constraint, SLRC achieves the state-of-the-art performance on FERET database using single sample per person.

Unsupervised person re-identification (Re-ID) aims to match pedestrian images from different camera views in an unsupervised setting. Existing methods for unsupervised person Re-ID are usually built upon the pseudo labels from clustering. However, the result of clustering depends heavily on the quality of the learned features, which are overwhelmingly dominated by colors in images. In this paper, we attempt to suppress the negative dominating influence of colors to learn more effective features for unsupervised person Re-ID. Specifically, we propose a Cluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised person Re-ID, in which clustering result is leveraged to guide the feature learning in a properly designed asymmetric contrastive learning framework. In CACL, both instance-level and cluster-level contrastive learning are employed to help the siamese network learn discriminant features with respect to the clustering result within and between different data augmentation views, respectively. In addition, we also present a cluster refinement method, and validate that the cluster refinement step helps CACL significantly. Extensive experiments conducted on three benchmark datasets demonstrate the superior performance of our proposal.

Face super-resolution aims to reconstruct a high-resolution face image from a low-resolution face image. Previous methods typically employ an encoder-decoder structure to extract facial structural features, where the direct downsampling inevitably introduces distortions, especially to high-frequency features such as edges. To address this issue, we propose a wavelet-based feature enhancement network, which mitigates feature distortion by losslessly decomposing the input feature into high and low-frequency components using the wavelet transform and processing them separately. To improve the efficiency of facial feature extraction, a full domain Transformer is further proposed to enhance local, regional, and global facial features. Such designs allow our method to perform better without stacking many modules as previous methods did. Experiments show that our method effectively balances performance, model size, and speed. Code link: https://github.com/PRIS-CV/WFEN.