Semantic segmentation usually suffers from a long-tail data distribution. Due to the imbalanced number of samples across categories, the features of those tail classes may get squeezed into a narrow area in the feature space. Towards a balanced feature distribution, we introduce category-wise variation into the network predictions in the training phase such that an instance is no longer projected to a feature point, but a small region instead. Such a perturbation is highly dependent on the category scale, which appears as assigning smaller variation to head classes and larger variation to tail classes. In this way, we manage to close the gap between the feature areas of different categories, resulting in a more balanced representation. It is note-worthy that the introduced variation is discarded at the inference stage to facilitate a confident prediction. Although with an embarrassingly simple implementation, our method manifests itself in strong generalizability to various datasets and task settings. Extensive experiments suggest that our plug-in design lends itself well to a range of state-of-the-art approaches and boosts the performance on top of them.11Code: https://github.com/grantword8/BLV.

Gaussian process dynamical systems (GPDSs) have shown their effectiveness in many tasks of machine learning. However, when they address multi-view data, current GPDSs do not explicitly model the dependence between private and shared latent variables. Instead, they introduce structurally and intrinsically discrete segmentation in the latent space. In this paper, we propose the multi-view collaborative Gaussian process dynamical systems (McGPDSs) model, which assumes that the private latent variable for each view is controlled by its dynamical prior and the shared latent variable. The relevance between private and shared latent variables can be automatically learned by optimization in the Bayesian framework. The model is capable of learning an effective latent representation and generating novel data of one view given data of the other view. We evaluate our model on two-view data sets, and our model obtains better performance compared with the state-of-the-art multi-view GPDSs.

The crux of semi-supervised semantic segmentation is to assign adequate pseudo-labels to the pixels of unlabeled images. A common practice is to select the highly confident predictions as the pseudo ground-truth, but it leads to a problem that most pixels may be left unused due to their unreliability. We argue that every pixel matters to the model training, even its prediction is ambiguous. Intuitively, an unreliable prediction may get confused among the top classes (i.e., those with the highest probabilities), however, it should be confident about the pixel not belonging to the remaining classes. Hence, such a pixel can be convincingly treated as a negative sample to those most unlikely categories. Based on this insight, we develop an effective pipeline to make sufficient use of unlabeled data. Concretely, we separate reliable and unreliable pixels via the entropy of predictions, push each unreliable pixel to a category-wise queue that consists of negative samples, and manage to train the model with all candidate pixels. Considering the training evolution, where the prediction becomes more and more accurate, we adaptively adjust the threshold for the reliable-unreliable partition. Experimental results on various benchmarks and training settings demonstrate the superiority of our approach over the state-of-the-art alternatives.11Project: https://haochen-wang409.github.io/U2PL.

Self-training has shown great potential in semi-supervised learning. Its core idea is to use the model learned on labeled data to generate pseudo-labels for unlabeled samples, and in turn teach itself. To obtain valid supervision, active attempts typically employ a momentum teacher for pseudo-label prediction yet observe the confirmation bias issue, where the incorrect predictions may provide wrong supervision signals and get accumulated in the training process. The primary cause of such a drawback is that the prevailing self-training framework acts as guiding the current state with previous knowledge because the teacher is updated with the past student only. To alleviate this problem, we propose a novel self-training strategy, which allows the model to learn from the future. Concretely, at each training step, we first virtually optimize the student (i.e., caching the gradients without applying them to the model weights), then update the teacher with the virtual future student, and finally ask the teacher to produce pseudo-labels for the current student as the guidance. In this way, we manage to improve the quality of pseudo-labels and thus boost the performance. We also develop two variants of our future-self-training (FST) framework through peeping at the future both deeply (FST-D) and widely (FST-W). Taking the tasks of unsupervised domain adaptive semantic segmentation and semi-supervised semantic segmentation as the instances, we experimentally demonstrate the effectiveness and superiority of our approach under a wide range of settings. Code is available at https://github.com/usr922/FST.

Compared with other human biosignatures, iris has more advantages on accuracy, invariability and robustness. However, the performance of existing common iris recognition algorithms is still far from expectations of the community. Although some researchers have attempted to uti-lize deep learning methods which are superior to traditional methods, it is worth exploring better CNN network architecture. In this paper, we propose a novel network architecture based on the dual spatial attention mechanism for iris recognition, called DualSANet. Specifically, the proposed architecture can generate multi-level spatially corresponding feature representations via an encoder-decoder structure. In the meantime, we also propose a new spatial attention feature fusion module, so as to ensemble these features more effectively. Based on these, our architecture can generate dual feature representations which have complementary discriminative information. Extensive experiments are conducted on CASIA-IrisV4-Thousand, CASIA-IrisV4-Distance, and IITD datasets. The experimental results show that our method achieves superior performance compared with the state-of-the-arts.