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).

Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

Abstract: We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.

FashionAI: A Hierarchical Dataset for Fashion Understanding

Abstract: Fine-grained attribute recognition is critical for fashion understanding, yet is missing in existing professional and comprehensive fashion datasets. In this paper, we present a large scale attribute dataset with manual annotation in high quality. To this end, complex fashion knowledge is disassembled into mutually exclusive concepts and form a hierarchical structure to describe the cognitive process. Such well-structured knowledge is reflected by dataset in terms of its clear definition and precise annotation. The problems which are common in the process of annotation, including structured noise, occlusion, uncertain problems, and attribute inconsistency, are well addressed instead of merely discarding those bad data. Further, we propose an iterative process of building a dataset with practical usefulness. With 24 key points, 245 labels that cover 6 categories of women's clothing, and a total of 41 subcategories, the creation of our dataset drew upon a large amount of crowd staff engagement. Extensive experiments quantitatively and qualitatively demonstrate its effectiveness.

Distance-based sampling of software configuration spaces

Abstract: Configurable software systems provide a multitude of configuration options to adjust and optimize their functional and non-functional properties. For instance, to find the fastest configuration for a given setting, a brute-force strategy measures the performance of all configurations, which is typically intractable. Addressing this challenge, state-of-the-art strategies rely on machine learning, analyzing only a few configurations (i.e., a sample set) to predict the performance of other configurations. However, to obtain accurate performance predictions, a representative sample set of configurations is required. Addressing this task, different sampling strategies have been proposed, which come with different advantages (e.g., covering the configuration space systematically) and disadvantages (e.g., the need to enumerate all configurations). In our experiments, we found that most sampling strategies do not achieve a good coverage of the configuration space with respect to covering relevant performance values. That is, they miss important configurations with distinct performance behavior. Based on this observation, we devise a new sampling strategy, called distance-based sampling, that is based on a distance metric and a probability distribution to spread the configurations of the sample set according to a given probability distribution across the configuration space. This way, we cover different kinds of interactions among configuration options in the sample set. To demonstrate the merits of distance-based sampling, we compare it to state-of-the-art sampling strategies, such as t-wise sampling, on $10$ real-world configurable software systems. Our results show that distance-based sampling leads to more accurate performance models for medium to large sample sets.

Local Convolutional Neural Networks for Person Re-Identification

Abstract: Recent works have shown that person re-identification can be substantially improved by introducing attention mechanisms, which allow learning both global and local representations. However, all these works learn global and local features in separate branches. As a consequence, the interaction/boosting of global and local information are not allowed, except in the final feature embedding layer. In this paper, we propose local operations as a generic family of building blocks for synthesizing global and local information in any layer. This building block can be inserted into any convolutional networks with only a small amount of prior knowledge about the approximate locations of local parts. For the task of person re-identification, even with only one local block inserted, our local convolutional neural networks (Local CNN) can outperform state-of-the-art methods consistently on three large-scale benchmarks, including Market-1501, CUHK03, and DukeMTMC-ReID.

End-to-End Learning Local Multi-View Descriptors for 3D Point Clouds

Abstract: In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds. To adopt a similar multi-view representation, existing studies use hand-crafted viewpoints for rendering in a preprocessing stage, which is detached from the subsequent descriptor learning stage. In our framework, we integrate the multi-view rendering into neural networks by using a differentiable renderer, which allows the viewpoints to be optimizable parameters for capturing more informative local context of interest points. To obtain discriminative descriptors, we also design a soft-view pooling module to attentively fuse convolutional features across views. Extensive experiments on existing 3D registration benchmarks show that our method outperforms existing local descriptors both quantitatively and qualitatively.