12 accepted papers
The research in AI-based formal mathematical reasoning has shown an unstoppable growth trend. These studies have excelled in mathematical competitions like IMO and have made significant progress. However, these studies intertwined multiple skills simultaneously—problem-solving, reasoning, and writin…
This paper aims to predict accurate unoriented normals from unorganized 3D point clouds, possibly containing noise and varying densities. Previous methods for unoriented normal estimation either fit an explicit surface from a local patch to infer normals or directly regress normals using neural netw…
Neural Radiance Field (NeRF) stands out by demonstrating photo-realistic renderings, while it suffers from quality degradation when given only a few shot inputs. This paper aims to improve the rendering quality of NeRF from a few shot inputs. Original NeRF tends to overfit input views rapidly at the…
Reference line detection is a challenging problem due to localization uncertainty and severe occlusion. To deal with the two issues, we propose a general framework for reference line detection with two modules: Gaussian line detection and connection graph inference. The first module outputs a set of…
This paper proposes BPNet, a novel end-to-end deep learning framework to learn Bézier primitive segmentation on 3D point clouds. The existing works treat different primitive types separately, thus limiting them to finite shape categories. To address this issue, we seek a generalized primitive segmen…
Point cloud sampling is crucial for efficient large-scale point cloud analysis, where learning-to-sample methods have recently received increasing attention from the community for jointly training with downstream tasks. However, the above-mentioned task-specific sampling methods usually fail to expl…
This paper describes our best system and methodology for ADD 2022: The First Audio Deep Synthesis Detection Challenge[1]. The very same system was used for both two rounds of evaluation in Track 3.2 with similar training methodology. The first round of Track 3.2 data is generated from Text-to-Speech…
In this paper, we describe our speech generation system for the first Audio Deep Synthesis Detection Challenge (ADD 2022). Firstly, we build an any-to-many voice conversion (VC) system to convert source speech with arbitrary language content into target speaker’s fake speech. Then the converted spee…
Invariance to diverse types of image corruption, such as noise, blurring, or colour shifts, is essential to establish robust models in computer vision. Data augmentation has been the major approach in improving the robustness against common corruptions. However, the samples produced by popular augme…
This paper introduces a new open-sourced Mandarin speech corpus, called DiDiSpeech. It consists of about 800 hours of speech data at 48kHz sampling rate from 6000 speakers and the corresponding texts. All speech data in the corpus is recorded in quiet environment and is suitable for various speech p…
This paper introduces an open source speech dataset, KeSpeech, which involves 1,542 hours of speech signals recorded by 27,237 speakers in 34 cities in China, and the pronunciation includes standard Mandarin and its 8 subdialects. The new dataset possesses several properties. Firstly, the dataset pr…
Generative models for 3D point clouds are extremely important for scene/object reconstruction applications in autonomous driving and robotics. Despite recent success of deep learning-based representation learning, it remains a great challenge for deep neural networks to synthesize or reconstruct hig…