Despite the evident superiority of event cameras in practical vision applications (e.g., action recognition), owing to their distinctive sensing mechanism, existing event-based action recognition methods rely heavily on large-scale training data. However, the expensive cost of camera deployment and the requirement of data privacy protection make it challenging to collect substantial data in real-world scenarios. To address this limitation, we explore a novel yet practical task, Few-Shot Event-Based Action Recognition (FSEAR), which aims at leveraging a minimal number of intractable event action data for model training and accurately classifying unlabeled data into a specific category. Accordingly, we design a new framework for FSEAR, including a Noise-Aware Event Encoder (NAE) and a Distilled Prototypical Distance Fusion (DPDF). The former efficiently filters noise within the spatiotemporal domain while retaining vital information related to action timing. The latter conducts multi-scale measurements across geometric, directional, and distributional dimensions. These two modules benefit mutually and thus effectively exploit the potential characteristics of event data. Extensive experiments on four distinct event action recognition datasets have demonstrated the significant advantages of our model over other few-shot learning methods. Our code and models will be publicly released.

Real-time collaborative programming supports a team of programmers to concurrently view and edit the same set of source code at the same time, which is beneficial in meeting particular collaboration needs. However, during the collaboration process, programmers are not able to compile and debug the source code with syntactic errors as it is being continuously edited by other collaborators. To address this challenge, we propose a novel approach named Reversion of Error-free Code with Workspace Isolation (RECON) and contribute supporting techniques with prototype implementation. In this approach, the system continuously monitors the files being collaboratively edited, detects source code without syntactic error, and maintains additional Error-free source code copies. Whenever a programmer attempts to compile and debug the code, the system creates a temporary isolated workspace and replaces the source code files with the latest Error-free copies. The proposed approach and solution have been implemented in a prototype system named CoIDEA, which has indicated the feasibility of the scheme and techniques.

With the rapid growth of the internet and multimedia technology, there is an exponential growth of copy video, which causes some certain impact on video retrieval and copyright protection. Therefore, it becomes increasingly important to find copy videos and locate subsequent clips in a large-scale video database. In this paper, an efficient method is proposed to solve the current problem of video copy detection when the test video contains both copy video clips and non-copy video clips. This paper presents a method of judging the video copy sub-sequence based on the distance between the test video keyframe and the reference video keyframe. First, AlexNet is used to extract the features of the keyframe. Second, it judges whether each test video keyframe is a copy frame according to the distance. Then, the location of the copy sub-sequence is determined by finding the continuous copy frame, and then the video location of the copy sub-sequence is determined. Experimental results show that the proposed method can achieve 86.29% in recall and 95.71% in precision.

Effectively measuring, understanding, and improving mobile app performance is of paramount importance for mobile app developers. Across the mobile Internet landscape, companies run online controlled experiments (A/B tests) with thousands of performance metrics in order to understand how app performance causally impacts user retention and to guard against service or app regressions that degrade user experiences. To capture certain characteristics particular to performance metrics, such as enormous observation volume and high skewness in distribution, an industry-standard practice is to construct a performance metric as a quantile over all performance events in control or treatment buckets in A/B tests. In our experience with thousands of A/B tests provided by Snap, we have discovered some pitfalls in this industry-standard way of calculating performance metrics that can lead to unexplained movements in performance metrics and unexpected misalignment with user engagement metrics. In this paper, we discuss two major pitfalls in this industry-standard practice of measuring performance for mobile apps. One arises from strong heterogeneity in both mobile devices and user engagement, and the other arises from self-selection bias caused by post-treatment user engagement changes. To remedy these two pitfalls, we introduce several scalable methods including user-level performance metric calculation and imputation and matching for missing metric values. We have extensively evaluated these methods on both simulation data and real A/B tests, and have deployed them into Snap's in-house experimentation platform.

The distant supervised (DS) method has improved the performance of relation classification (RC) by means of extending the dataset. However, DS also brings the problem of wrong labeling. Contrary to DS, the few-shot method relies on few supervised data to predict the unseen classes. In this paper, we use word embedding and position embedding to construct multi-channel vector representation and use the multi-channel convolutional method to extract features of sentences. Moreover, in order to alleviate few-shot learning to be sensitive to overfitting, we introduce adversarial learning for training a robust model. Experiments on the FewRel dataset show that our model achieves significant and consistent improvements on few-shot RC as compared with baselines.

Human activity recognition has been widely used in healthcare applications such as elderly monitoring, exercise supervision, and rehabilitation monitoring. Compared with other approaches, sensor-based wearable human activity recognition is less affected by environmental noise and therefore is promising in providing higher recognition accuracy. However, one of the major issues of existing wearable human activity recognition methods is that although the average recognition accuracy is acceptable, the recognition accuracy for some activities (e.g., ascending stairs and descending stairs) is low, mainly due to relatively less training data and complex behavior pattern for these activities. Another issue is that the recognition accuracy is low when the training data from the test subject are limited, which is a common case in real practice. In addition, the use of neural network leads to large computational complexity and thus high power consumption. To address these issues, we proposed a new human activity recognition method with two-stage end-to-end convolutional neural network and a data augmentation method. Compared with the state-of-the-art methods (including neural network based methods and other methods), the proposed methods achieve significantly improved recognition accuracy and reduced computational complexity.

This paper presents a high throughput and energy-efficient retina inspired tone mapping processor. Several hardware design techniques have been proposed to achieve high throughput and high energy efficiency, including data partition based parallel processing with S-shape sliding, adjacent frame feature sharing, multi-layer convolution pipelining and convolution filter compression with zero skipping convolution. The proposed processor has been implemented on a Xilinx's Virtex7 FPGA for demonstration. It is able to achieve a throughput of 189 frames per second for 1024*768 RGB images with 819 mW. Compared with several state-of-the-art tone mapping processors, the proposed processor achieves higher throughput and energy efficiency. It is suitable for high-speed and energy-constrained video enhancement applications such as autonomous vehicle and drone monitoring.

Online controlled experiments (a.k.a. A/B testing) have been used as the mantra for data-driven decision making on feature changing and product shipping in many Internet companies. However, it is still a great challenge to systematically measure how every code or feature change impacts millions of users with great heterogeneity (e.g. countries, ages, devices). The most commonly used A/B testing framework in many companies is based on Average Treatment Effect (ATE), which cannot detect the heterogeneity of treatment effect on users with different characteristics. In this paper, we propose statistical methods that can systematically and accurately identify Heterogeneous Treatment Effect (HTE) of any user cohort of interest (e.g. mobile device type, country), and determine which factors (e.g. age, gender) of users contribute to the heterogeneity of the treatment effect in an A/B test. By applying these methods on both simulation data and real-world experimentation data, we show how they work robustly with controlled low False Discover Rate (FDR), and at the same time, provides us with useful insights about the heterogeneity of identified user groups. We have deployed a toolkit based on these methods, and have used it to measure the Heterogeneous Treatment Effect of many A/B tests at Snap.