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).

SPARK: Spatial-Aware Online Incremental Attack Against Visual Tracking

Abstract: Adversarial attacks of deep neural networks have been intensively studied on image, audio, and natural language classification tasks. Nevertheless, as a typical while important real-world application, the adversarial attacks of online video tracking that traces an object’s moving trajectory instead of its category are rarely explored. In this paper, we identify a new task for the adversarial attack to visual tracking: online generating imperceptible perturbations that mislead trackers along with an incorrect (Untargeted Attack, UA) or specified trajectory (Targeted Attack, TA). To this end, we first propose a spatial-aware basic attack by adapting existing attack methods, i.e., FGSM, BIM, and C&W, and comprehensively analyze the attacking performance. We identify that online object tracking poses two new challenges: 1) it is difficult to generate imperceptible perturbations that can transfer across frames, and 2) real-time trackers require the attack to satisfy a certain level of efficiency. To address these challenges, we further propose the spatial-aware online inc remental attac k (a.k.a. SPARK) that performs spatial-temporal sparse incremental perturbations online and makes the adversarial attack less perceptible. In addition, as an optimization-based method, SPARK quickly converges to very small losses within several iterations by considering historical incremental perturbations, making it much more efficient than basic attacks. The in-depth evaluation of the state-of-the-art trackers (i.e., SiamRPN++ with AlexNet, MobileNetv2, and ResNet-50, and SiamDW) on OTB100, VOT2018, UAV123, and LaSOT demonstrates the effectiveness and transferability of SPARK in misleading the trackers under both UA and TA with minor perturbations.

Recursive segmentation and recognition templates for image parsing

Abstract: In this paper, we propose a Hierarchical Image Model (HIM) which parses images to perform segmentation and object recognition. The HIM represents the image recursively by segmentation and recognition templates at multiple levels of the hierarchy. This has advantages for representation, inference, and learning. First, the HIM has a coarse-to-fine representation which is capable of capturing long-range dependency and exploiting different levels of contextual information (similar to how natural language models represent sentence structure in terms of hierarchical representations such as verb and noun phrases). Second, the structure of the HIM allows us to design a rapid inference algorithm, based on dynamic programming, which yields the first polynomial time algorithm for image labeling. Third, we learn the HIM efficiently using machine learning methods from a labeled data set. We demonstrate that the HIM is comparable with the state-of-the-art methods by evaluation on the challenging public MSRC and PASCAL VOC 2007 image data sets.

Hermes: an efficient federated learning framework for heterogeneous mobile clients

Abstract: Federated learning (FL) has been a popular method to achieve distributed machine learning among numerous devices without sharing their data to a cloud server. FL aims to learn a shared global model with the participation of massive devices under the orchestration of a central server. However, mobile devices usually have limited communication bandwidth to transfer local updates to the central server. In addition, the data residing across devices is intrinsically statistically heterogeneous (i.e., non-IID data distribution). Learning a single global model may not work well for all devices participating in the FL under data heterogeneity. Such communication cost and data heterogeneity are two critical bottlenecks that hinder from applying FL in practice. Moreover, mobile devices usually have limited computational resources. Improving the inference efficiency of the learned model is critical to deploy deep learning applications on mobile devices. In this paper, we present Hermes - a communication and inference-efficient FL framework under data heterogeneity. To this end, each device finds a small subnetwork by applying the structured pruning; only the updates of these subnetworks will be communicated between the server and the devices. Instead of taking the average over all parameters of all devices as conventional FL frameworks, the server performs the average on only overlapped parameters across each subnetwork. By applying Hermes, each device can learn a personalized and structured sparse deep neural network, which can run efficiently on devices. Experiment results show the remarkable advantages of Hermes over the status quo approaches. Hermes achieves as high as 32.17% increase in inference accuracy, 3.48× reduction on the communication cost, 1.83× speedup in inference efficiency, and 1.8× savings on energy consumption.

A deep-learning approach to facial expression recognition with candid images

Abstract: To recognize facial expression from candid, non-posed images, we propose a deep-learning based approach using convolutional neural networks (CNNs) In order to evaluate the performance in real-time candid facial expression recognition, we have created a candid image facial expression (CIFE) dataset, with seven types of expression in more than 10,000 images gathered from the Web As baselines, two feature-based approaches (LBP+SVM, SIFT+SVM) are tested on the dataset The structure of our proposed CNN-based approach is described, and a data augmentation technique is provided in order to generate sufficient number of training samples The performance using the feature-based approaches is close to the state of the art when tested with standard datasets, but fails to function well when dealing with candid images Our experiments show that the CNN-based approach is very effective in candid image expression recognition, significantly outperforming the baseline approaches, by a 20% margin