Ruyi Ji

Libo Zhang, Lutao Jiang, Ruyi Ji et al.

Automatic security inspection relying on computer vision technology is a challenging task in real-world scenarios due to many factors, such as intra-class variance, class imbalance, and occlusion. Most previous methods rarely touch the cases where the prohibited items are deliberately hidden in messy objects because of the scarcity of large-scale datasets, hindering their applications. To address this issue and facilitate related research, we present a large-scale dataset, named PIDray, which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. In specific, PIDray collects 124,486 X-ray images for $12$ categories of prohibited items, and each image is manually annotated with careful inspection, which makes it, to our best knowledge, to largest prohibited items detection dataset to date. Meanwhile, we propose a general divide-and-conquer pipeline to develop baseline algorithms on PIDray. Specifically, we adopt the tree-like structure to suppress the influence of the long-tailed issue in the PIDray dataset, where the first course-grained node is tasked with the binary classification to alleviate the influence of head category, while the subsequent fine-grained node is dedicated to the specific tasks of the tail categories. Based on this simple yet effective scheme, we offer strong task-specific baselines across object detection, instance segmentation, and multi-label classification tasks and verify the generalization ability on common datasets (e.g., COCO and PASCAL VOC). Extensive experiments on PIDray demonstrate that the proposed method performs favorably against current state-of-the-art methods, especially for deliberately hidden items. Our benchmark and codes will be released at https://github.com/lutao2021/PIDray.

Lutao Jiang, Ruyi Ji, Libo Zhang

In this paper, we develop a new method, termed SDF-3DGAN, for 3D object generation and 3D-Aware image synthesis tasks, which introduce implicit Signed Distance Function (SDF) as the 3D object representation method in the generative field. We apply SDF for higher quality representation of 3D object in space and design a new SDF neural renderer, which has higher efficiency and higher accuracy. To train only on 2D images, we first generate the objects, which are represented by SDF, from Gaussian distribution. Then we render them to 2D images and use them to apply GAN training method together with 2D images in the dataset. In the new rendering method, we relieve all the potential of SDF mathematical property to alleviate computation pressure in the previous SDF neural renderer. In specific, our new SDF neural renderer can solve the problem of sampling ambiguity when the number of sampling point is not enough, \ie use the less points to finish higher quality sampling task in the rendering pipeline. And in this rendering pipeline, we can locate the surface easily. Therefore, we apply normal loss on it to control the smoothness of generated object surface, which can make our method enjoy the much higher generation quality. Quantitative and qualitative experiments conducted on public benchmarks demonstrate favorable performance against the state-of-the-art methods in 3D object generation task and 3D-Aware image synthesis task. Our codes will be released at https://github.com/lutao2021/SDF-3DGAN.

Jingjing Liang, Ruyi Ji, Jiajun Jiang et al.

It is widely recognized that program repair tools need to have a high precision to be useful, i.e., the generated patches need to have a high probability to be correct. However, it is fundamentally difficult to ensure the correctness of the patches, and many tools compromise other aspects of repair performance such as recall for an acceptable precision. In this paper we ask a question: can a repair tool with a low precision be still useful? To explore this question, we propose an interactive filtering approach to patch review, which filters out incorrect patches by asking questions to the developers. Our intuition is that incorrect patches can still help understand the bug. With proper tool support, the benefit outweighs the cost even if there are many incorrect patches. We implemented the approach as an Eclipse plugin tool, InPaFer, and evaluated it with a simulated experiment and a user study with 30 developers. The results show that our approach improve the repair performance of developers, with 62.5% more successfully repaired bugs and 25.3% less debugging time in average. In particular, even if the generated patches are all incorrect, the performance of the developers would not be significantly reduced, and could be improved when some patches provide useful information for repairing, such as the faulty location and a partial fix.