Data often faces a severe class imbalance issue in the real world, meaning that the number of instances within classes varies greatly, following a long-tailed distribution.In this case, the direct application of supervised learning yields poor performance.Existing long-tailed recognition (LTR) methods often heavily rely on the label information to enhance tail classes' accuracy at the expense of head class by an image-level end-to-end resampling strategy to address data distribution imbalance.Nevertheless, they neglect label bias, which can severely affect the LTR model's accuracy.In this paper, we propose a novel approach, namely Coarse-Fine Decoupled Representation Learning (CFRL) for LTR.Our core idea is to decouple data representations from the classifier and decompose representation learning into two stages: image-level and patch-level.Specifically, in the image-level stage, we leverage unsupervised learning on image-level information to reduce the impact of label bias caused by imbalanced datasets.In the patch-level stage, we introduce patch-level rotation augmentation as negative samples, forcing the model to acquire more comprehensive information.Our theoretical and empirical analyses demonstrate that the approach does not sacrifice the accuracy of head classes while significantly reducing the overfitting of tail classes, improving both of them.We showcase state-of-the-art results on CIFAR, ImageNet, and iNaturalist datasets.Furthermore, we illustrate that this training methodology can be combined with various existing Long-Tailed Recognition (LTR) methods, further enhancing their performance.

Geometric style transfer jointly stylizes the texture and geometry of a content image to better match a style image, which has attracted widespread attention due to its various applications. However, existing style transfer methods either primarily focus on texture and almost entirely ignore geometry, or have various drawbacks and are not suitable for Face Portraits. In the paper, We propose a new two-stage geometric style transfer method dedicated to face portraits, which simultaneously transfer both statistical and structural styles. Our network consists of Geometric deformation module (G) and Texture rendering module (T). G is trained with semantics image pairs, which has loose requirements on the training datasets. Besides, our flexible formulation also allows explicit user guidance and control of stylization tradeoffs. Experiments demonstrate that our method achieves state-of-the-art geometric style transfer for face portraits.

In this paper, a novelDeepMulti-viewJointClustering (DMJC) framework is proposed, where multiple deep embedded features, multi-view fusion mechanism, and clustering assignments can be learned simultaneously. Through the joint learning strategy, the clustering-friendly multi-view features and useful multi-view complementary information can be exploited effectively to improve the clustering performance. Under the proposed joint learning framework, we design two ingenious variants of deep multi-view joint clustering models, whose multi-view fusion is implemented by two kinds of simple yet effective schemes. The first model, called DMJC-S, performs multi-view fusion in an implicit way via a novel multi-view soft assignment distribution. The second model, termed DMJC-T, defines a novel multi-view auxiliary target distribution to conduct the multi-view fusion explicitly. Both DMJC-S and DMJC-T are optimized under a KL divergence objective. Experiments on eight challenging image datasets demonstrate the superiority of both DMJC-S and DMJC-T over single/multi-view baselines and the state-of-the-art multi-view clustering methods, which proves the effectiveness of the proposed DMJC framework. To the best of our knowledge, this is the first work to model the multi-view clustering in a deep joint framework, which will provide a meaningful thinking in unsupervised multi-view learning.

Many state-of-the-art saliency detection algorithms rely on the boundary prior, but these algorithms simply suppose the boundaries around an image as background regions. Here we propose a fast and effective algorithm for salient object detection. First, a novel method is proposed to approximately locate the foreground object by using the convex hull from Harris corner. On this basis, we divide the saliency values of different regions into two parts and generate the corresponding cue maps (foreground and background), which are combined into a convex hull prior map. Then a new prior based on distance to the convex hull center is proposed to replace the center prior. Finally, the convex hull prior map and the convex hull center-biased map are combined to be the saliency map, which is then optimized to get the final result. Compared with eighteen existing algorithms and tested on several datasets, the present algorithm performs well in terms of precision and recall.