The rapidly evolving landscape of products, surfaces, policies, and regulations poses significant challenges for deploying state-of-the-art recommendation models at industry scale, primarily due to data fragmentation across domains and escalating infrastructure costs that hinder sustained quality improvements.

Co-Saliency Detection (CSD) is to explore the concurrent patterns and salient objects from a group of relevant images, while Instance Co-Segmentation (ICS) aims to identify and segment out all of these co-salient instances, generating corresponding mask for each instance. To simultaneously tackle these two tasks, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC) for CSD and ICS, termed as GCAGC-CSD and GCAGC-ICS, respectively. The GCAGC-CSD contains three key model designs: first, we develop a graph convolutional network architecture to extract multi-scale representations to characterize the intra- and inter-image consistency. Second, we propose an attention graph clustering algorithm to distinguish the salient foreground objects from common areas in an unsupervised manner. Third, we present a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutional network, attention graph cluster, and CSD decoder in an end-to-end fashion. Afterwards, we design a salient instance segmentation network for GCAGC-ICS, and combine the outputs of GCAGC-CSD and the instance segmentation branch to obtain instance-aware co-segmentation masks. The proposed GCAGC-CSD and GCAGC-ICS are extensively evaluated on four CSD benchmark datasets (iCoseg, Cosal2015, COCO-SEG and CoSOD3k) and five ICS benchmark datasets (CoSOD3k, COCO-NONVOC, COCO-VOC, VOC12 and SOC), and achieve superior performance over state-of-the-arts on both tasks.

Co-saliency detection aims to discover the common and salient foregrounds from a group of relevant images. For this task, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC). Three major contributions have been made, and are experimentally shown to have substantial practical merits. First, we propose a graph convolutional network design to extract information cues to characterize the intra- and inter-image correspondence. Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion. Third, we present a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutional network, attention graph cluster, and co-saliency detection decoder in an end-to-end manner. We evaluate our proposed GCAGC method on three co-saliency detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method obtains significant improvements over the state-of-the-arts on most of them.

Pig counting is a crucial task for large-scale pig farming. Pigs are usually visually counted by human. But this process is very time-consuming and error-prone. Few studies in literature developed automated pig counting method. The existing works only focused on pig counting using single image, and its level of accuracy faced challenges due to pig movements, occlusion and overlapping. Especially, the field of view of a single image is very limited, and could not meet the needs of pig counting for large pig grouping houses. Towards addressing these challenges, we presented a real-time automated pig counting system in crowds using only one monocular fisheye camera with an inspection robot. Our system showed that it achieved performance superior to human. Our pipeline began with a novel bottom-up pig detection algorithm to avoid false negatives due to overlapping, occlusion and deformable pig shapes. This detection included a deep convolution neural network (CNN) for pig body part keypoints detection and the keypoints association method to identify individual pigs. It then employed an efficient on-line tracking method to associate pigs across image frames. Finally, pig counts were estimated by a novel spatial-aware temporal response filtering (STRF) method to suppress false positives caused by pig or camera movements or tracking failures. The whole pipeline has been deployed in an edge computing device, and demonstrated the effectiveness.

Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head and a grading network head. Compared with a single-task model, our multi-task model can provide complementary contextual information, which contributes to better performance. Our model is achieved a state-of-the-art performance in epithelial cells detection and Gleason grading tasks simultaneously. Using fivefold cross-validation, our model is achieved an epithelial cells detection accuracy of 99.07% with an average area under the curve of 0.998. As for Gleason grading, our model is obtained a mean intersection over union of 79.56% and an overall pixel accuracy of 89.40%.