Alibaba Group

About: Alibaba Group is a company organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Terminal (electronics). The organization has 6810 authors who have published 7389 publications receiving 55653 citations. The organization is also known as: Alibaba Group Holding Limited & Alibaba Group (Cayman Islands).

Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification

Abstract: Typical person re-identification (ReID) methods usually describe each pedestrian with a single feature vector and match them in a task-specific metric space. However, the methods based on a single feature vector are not sufficient enough to overcome visual ambiguity, which frequently occurs in real scenario. In this paper, we propose a novel end-to-end trainable framework, called Dual ATtention Matching network (DuATM), to learn context-aware feature sequences and perform attentive sequence comparison simultaneously. The core component of our DuATM framework is a dual attention mechanism, in which both intrasequence and inter-sequence attention strategies are used for feature refinement and feature-pair alignment, respectively. Thus, detailed visual cues contained in the intermediate feature sequences can be automatically exploited and properly compared. We train the proposed DuATM network as a siamese network via a triplet loss assisted with a decorrelation loss and a cross-entropy loss. We conduct extensive experiments on both image and video based ReID benchmark datasets. Experimental results demonstrate the significant advantages of our approach compared to the state-of-the-art methods.

Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba

Abstract: Recommender systems (RSs) have been the most important technology for increasing the business in Taobao, the largest online consumer-to-consumer (C2C) platform in China. There are three major challenges facing RS in Taobao: scalability, sparsity and cold start. In this paper, we present our technical solutions to address these three challenges. The methods are based on a well-known graph embedding framework. We first construct an item graph from users' behavior history, and learn the embeddings of all items in the graph. The item embeddings are employed to compute pairwise similarities between all items, which are then used in the recommendation process. To alleviate the sparsity and cold start problems, side information is incorporated into the graph embedding framework. We propose two aggregation methods to integrate the embeddings of items and the corresponding side information. Experimental results from offline experiments show that methods incorporating side information are superior to those that do not. Further, we describe the platform upon which the embedding methods are deployed and the workflow to process the billion-scale data in Taobao. Using A/B test, we show that the online Click-Through-Rates (CTRs) are improved comparing to the previous collaborative filtering based methods widely used in Taobao, further demonstrating the effectiveness and feasibility of our proposed methods in Taobao's live production environment.

Regionlets for Generic Object Detection

Abstract: Generic object detection is confronted by dealing with different degrees of variations, caused by viewpoints or deformations in distinct object classes, with tractable computations. This demands for descriptive and flexible object representations which can be efficiently evaluated in many locations. We propose to model an object class with a cascaded boosting classifier which integrates various types of features from competing local regions, each of which may consist of a group of subregions, named as regionlets . A regionlet is a base feature extraction region defined proportionally to a detection window at an arbitrary resolution (i.e., size and aspect ratio). These regionlets are organized in small groups with stable relative positions to be descriptive to delineate fine-grained spatial layouts inside objects. Their features are aggregated into a one-dimensional feature within one group so as to be flexible to tolerate deformations. The most discriminative regionlets for each object class are selected through a boosting learning procedure. Our regionlet approach achieves very competitive performance on popular multi-class detection benchmark datasets with a single method, without any context. It achieves a detection mean average precision of 41.7 percent on the PASCAL VOC 2007 dataset, and 39.7 percent on the VOC 2010 for 20 object categories. We further develop support pixel integral images to efficiently augment regionlet features with the responses learned by deep convolutional neural networks. Our regionlet based method won second place in the ImageNet Large Scale Visual Object Recognition Challenge (ILSVRC 2013).

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

Abstract: Graph representation learning has emerged as a powerful technique for addressing real-world problems Various downstream graph learning tasks have benefited from its recent developments, such as node classification, similarity search, and graph classification However, prior arts on graph representation learning focus on domain specific problems and train a dedicated model for each graph dataset, which is usually non-transferable to out-of-domain data Inspired by the recent advances in pre-training from natural language processing and computer vision, we design Graph Contrastive Coding (GCC) -- a self-supervised graph neural network pre-training framework -- to capture the universal network topological properties across multiple networks We design GCC's pre-training task as subgraph instance discrimination in and across networks and leverage contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations We conduct extensive experiments on three graph learning tasks and ten graph datasets The results show that GCC pre-trained on a collection of diverse datasets can achieve competitive or better performance to its task-specific and trained-from-scratch counterparts This suggests that the pre-training and fine-tuning paradigm presents great potential for graph representation learning

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

Abstract: Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent developments, such as node classification, similarity search, and graph classification. However, prior arts on graph representation learning focus on domain specific problems and train a dedicated model for each graph dataset, which is usually non-transferable to out-of-domain data. Inspired by the recent advances in pre-training from natural language processing and computer vision, we design Graph Contrastive Coding (GCC) --- a self-supervised graph neural network pre-training framework --- to capture the universal network topological properties across multiple networks. We design GCC's pre-training task as subgraph instance discrimination in and across networks and leverage contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations. We conduct extensive experiments on three graph learning tasks and ten graph datasets. The results show that GCC pre-trained on a collection of diverse datasets can achieve competitive or better performance to its task-specific and trained-from-scratch counterparts. This suggests that the pre-training and fine-tuning paradigm presents great potential for graph representation learning.