Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world applications. However, for large-scale problems involving a huge number of samples and extremely high-dimensional features, solving sparse SVM-s remains challenging. By noting that sparse SVMs induce sparsities in both feature and sample spaces, we propose a novel approach, which is based on accurate estimations of the primal and dual optima of sparse SVMs, to simultaneously identify the features and samples that are guaranteed to be irrelevant to the outputs. Thus, we can remove the identified inactive samples and features from the training phase, leading to substantial savings in both the memory usage and computational cost without sacrificing accuracy. To the best of our knowledge, the proposed method is the first static feature and sample reduction method for sparse SVM. Experiments on both synthetic and real datasets (e.g., the kddb dataset with about 20 million samples and 30 million features) demonstrate that our approach significantly outperforms state-of-the-art methods and the speedup gained by our approach can be orders of magnitude.

Many image processing tasks can be formulated as translating images between two image domains such as colorization, super-resolution, and conditional image synthesis. In most of these tasks, an input image may correspond to multiple outputs. However, current existing approaches only show minor stochasticity of the outputs. In this paper, we present a novel approach to synthesize diverse realistic images corresponding to a semantic layout. We introduce a diversity loss objective that maximizes the distance between synthesized image pairs and relates the input noise to the semantic segments in the synthesized images. Thus, our approach can not only produce multiple diverse images but also allow users to manipulate the output images by adjusting the noise manually. The experimental results show that images synthesized by our approach are more diverse than that of the current existing works and equipping our diversity loss does not degrade the reality of the base networks. Moreover, our approach can be applied to unpaired datasets.

In the data collection task, it is more expensive to annotate the instance in multi-label learning problem, since each instance is associated with multiple labels. Therefore it is more important to adopt active learning method in multi-label learning to reduce the labeling cost. Recent researches indicate submodular function optimization works well on subset selection problem and provides theoretical performance guarantees while simultaneously retaining extremely fast optimization. In this paper, we propose a query strategy by constructing a submodular function for the selected instance-label pairs, which can measure and combine the informativeness and representativeness. Thus the active learning problem can be formulated as a submodular function maximization problem, which can be solved efficiently and effectively by a simple greedy lazy algorithm. Experimental results show that the proposed approach outperforms several state-of-the-art multi-label active learning methods.

The Person Re-identification (ReID) task aims to match persons across cameras in a surveillance system. In the past few years, many researches are devoted to ReID and its performance has gained significant improvement. ReID models are usually trained as a joint framework comprising a person feature extractor and a classifier. However, there exists co-adaptation between the feature extractor and the classifier, which prevents the feature extractor from making effective and sufficient optimization and results in inferior retrieval performance. In this paper, we propose a very simple and effective training method, called DeAda, to decouple this co-adaptation. Our main motivation is to construct a series of weak classifiers during training by randomization of parameters, so that optimization on the feature extractor could be strengthened in the training stage. DeAda is easy, effective, and efficient, and could serve as a plug-and-play optimization tool for ReID models, without additional memory and time cost. We also analyze the theoretical property of DeAda and show that it could produce identical features for the same person under some simple assumptions. We demonstrate its effectiveness on three public ReID datasets: Market1501, DukeMTMC-reID and CUHK03 over different ReID models. With DeAda optimization, we finally obtain state-of-the-art results on all the three datasets.

One of the most active areas in computer vision is object detection, which has made significant improvement in recent years. Current state-of-the-art object detection methods mostly adhere to the framework of the regions with convolutional neural network (R-CNN). However, they only take advantage of the local appearance features inside object bounding boxes. Since these approaches ignore the contextual information around the object proposals, the outcome of these detectors may generate a semantically incoherent interpretation of the input image. In this paper, we propose a novel object detection system which incorporates the local appearance and the contextual information. Specifically, the contextual information comprises the relationships among objects and the global scene based contextual feature generated by a convolutional neural network. The whole system is formulated as a fully connected conditional random field (CRF) defined on object proposals. Then the contextual constraints among object proposals are modeled as edges naturally. Furthermore, a fast mean field approximation method is utilized to infer in this CRF model efficiently. The experimental results demonstrate that our algorithm achieves a higher mean average precision (mAP) on PASCAL VOC 2007 datasets compared with the baseline algorithm Faster R-CNN.