The number of mitotic cells present in histopathological slides is an important predictor of tumor proliferation in the diagnosis of breast cancer. However, the current approaches can hardly perform precise pixel-level prediction for mitosis datasets with only weak labels (i.e., only provide the centroid location of mitotic cells), and take no account of the large domain gap across histopathological slides from different pathology laboratories. In this work, we propose a Domain adaptive Box-supervised Instance segmentation Network (DBIN) to address the above issues. In DBIN, we propose a high-performance Box-supervised Instance-Aware (BIA) head with the core idea of redesigning three box-supervised mask loss terms. Furthermore, we add a Pseudo-Mask-supervised Semantic (PMS) head for enriching characteristics extracted from underlying feature maps. Besides, we align the pixel-level feature distributions between source and target domains by a Cross-Domain Adaptive Module (CDAM), so as to adapt the detector learned from one lab can work well on unlabeled data from another lab. The proposed method achieves state-of-the-art performance across four mainstream datasets. A series of analysis and experiments show that our proposed BIA and PMS head can accomplish mitosis pixel-wise localization under weak supervision, and we can boost the generalization ability of our model by CDAM.

To improve the performance of sketch-based image retrieval (SBIR) methods, most existing SBIR methods develop brand new SBIR methods. In fact, a re-ranking approach, which can refine the retrieval results of SBIR methods, is also beneficial. Inspired by this, in this paper, an SBIR re-ranking approach based on multi-clustering is proposed. In order to make the re-ranking approach invisible to users and adaptive to different types of image datasets, we made it an unsupervised method using blind feedback. Distinguished from the existing methods, this re-ranking approach uses the semantic information of three types of images: edge maps, object images (images with black background and natural images’ foreground objects) and natural images themselves. With the initial retrieval results of an SBIR method, our approach first does the clustering operation for three types of images. Then, we utilize the clustering results to generate a cluster score for each initial retrieval result. Finally, the cluster score is used to calculate the final retrieval scores for the initial retrieval results. The experiments on different SBIR datasets are conducted. Experimental results demonstrate that, by implementing our re-ranking approach, the retrieval accuracy of a variety of SBIR methods is increased. Furthermore, the comparisons between our re-ranking method and the existing re-ranking methods are given.

Neural-network Processing Units (NPU), which specializes in the acceleration of deep neural networks (DNN), is of great significance to latency-sensitive areas like robotics or edge computing. However, there are few works focusing on the network design for NPU in recent studies. Most of the popular lightweight structures (e.g. MobileNet) are designed with depthwise convolution, which has less computation in theory but is not friendly to existing hardwares, and the speed tested on NPU is not always satisfactory. Even under similar FLOPs (the number of multiply-accumulates), vanilla convolution operation is always faster than depthwise one. In this paper, we will propose a novel architecture named Serial and Parallel Group Network (SPGNet), which can capture discriminative multi-scale information and at the same time keep the structure compact. Extensive evaluations have been conducted on different computer vision tasks, e.g. image classification (CIFAR and ImageNet), object detection (PASCAL VOC and MS COCO) and person re-identification (Market-1501 and DukeMTMC-ReID). The experimental results show that our proposed SPGNet can achieve comparable performance with the state-of-the-art networks while the speed is 120% faster than MobileNetV2 under similar FLOPS and over 300% faster than GhostNet with similar accuracy on NPU.