Inspired by the recent local descriptor based few-shot learning (FSL), our general UDA model is fully built upon local descriptors (LDs) for image classification and domain adaptation.
In this paper, we study stochastic optimization of areas under precision-recall curves (AUPRC), which is widely used for combating imbalanced classification tasks.
Inspired by the back-tracing strategy in the conventional Hough voting methods, in this work, we introduce a new 3D object detection method, named as Back-tracing Representative Points Network (BRNet), which generatively back-traces the representative points from the vote centers and also revisits complementary seed points around these generated points, so as to better capture the fine local structural features surrounding the potential objects from the raw point clouds.
Ranked #2 on 3D Object Detection on ScanNetV2
Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking.
Ranked #1 on Multi-Object Tracking on MOT15
no code implementations • 11 Mar 2021 • Xingyu Jiang, Mingyang Qin, Xinjian Wei, Zhongpei Feng, Jiezun Ke, Haipeng Zhu, Fucong Chen, Liping Zhang, Li Xu, Xu Zhang, Ruozhou Zhang, Zhongxu Wei, Peiyu Xiong, Qimei Liang, Chuanying Xi, Zhaosheng Wang, Jie Yuan, Beiyi Zhu, Kun Jiang, Ming Yang, Junfeng Wang, Jiangping Hu, Tao Xiang, Brigitte Leridon, Rong Yu, Qihong Chen, Kui Jin, Zhongxian Zhao
Iron selenide (FeSe) - the structurally simplest iron-based superconductor, has attracted tremendous interest in the past years.
no code implementations • 21 Jan 2021 • Ming Yang, Alceste Z. Bonanos, Biwei Jiang, Man I Lam, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Shu Wang, Xiao-Dian Chen, Frank Tramper, Yi Ren, Zoi T. Spetsieri
Further separating RSG candidates from the rest of the LSG candidates is done by using semi-empirical criteria on NIR CMDs and resulted in 323 RSG candidates.
Solar and Stellar Astrophysics Astrophysics of Galaxies
To address the above issues, in this paper, we propose a novel deep generative model, called Self-Consistent Generative Network (SCGN), which synthesizes novel views from the given input views without explicitly exploiting the geometric information.
Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space.
Experimental results on the KITTI dataset demonstrate significant improvement in filtering false positive over the approach using only point cloud data.
To address this issue, we propose a novel approach called Conditional ADversarial Image Translation (CADIT) to explicitly align the class distributions given samples between the two domains.
As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios.
The 2D and 3D dimensions of pedestrians are determined from the camera captures and further utilized through two feedforward links connected to the orientation estimator.
Moreover, incorporating with the learned affinity pyramid, a novel cascaded graph partition module is presented to sequentially generate instances from coarse to fine.
Learning structural information is critical for producing an ideal result in retinal image segmentation.
To instantiate this structure, the paper proposes a residual fusion block (RFB) to formulate the interdependences of the encoders.
RIFE adopts two feature extraction streams weighted by a dual-attention block to learn features for low and high resolution images, respectively.
For reentry or near space communication, owing to the influence of the time-varying plasma sheath channel environment, the received IQ baseband signals are severely rotated on the constellation.
Exploiting multi-scale representations is critical to improve edge detection for objects at different scales.
The period-luminosity (P-L) relation is analyzed for the RSGs in the fundamental mode.
Solar and Stellar Astrophysics Astrophysics of Galaxies
As a result, our framework can output both the semantic prediction and the instance prediction.
Dose calculation accuracy using sCT images has been improved over the original CBCT images, with the average Gamma Index passing rate increased from 95. 4% to 97. 4% for 1 mm/1% criteria.
Visual tracking is confronted by the dilemma to locate a target both}accurately and efficiently, and make decisions online whether and how to adapt the appearance model or even restart tracking.
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass.
Ranked #3 on Human Part Segmentation on CIHP
Temporal action proposal generation is an important yet challenging problem, since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content.
Ranked #1 on Temporal Action Proposal Generation on THUMOS' 14
In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images.
In recent years, deep neural nets have triumphed over many computer vision problems, including semantic segmentation, which is a critical task in emerging autonomous driving and medical image diagnostics applications.
Finally, an RDC based semantic segmentation model is built; the model is trained for real-world surround view images through a multi-task learning architecture by combining real-world images with transformed images.
In view of contemporary panoramic camera-laser scanner system, the traditional calibration method is not suitable for panoramic cameras whose imaging model is extremely nonlinear.
Therefore, a novel multi-model combination (MMC) approach for short-term probabilistic wind generation forecasting is proposed in this paper to exploit the advantages of different forecasting models.
Consequently, we first review the representative methods and theories of multi-view representation learning based on the perspective of alignment, such as correlation-based alignment.
To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix.
In this paper, we tackle this model storage issue by investigating information theoretical vector quantization methods for compressing the parameters of CNNs.
In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify.
Ranked #1 on 3D FACE MODELING on LFW
Scaling machine learning methods to very large datasets has attracted considerable attention in recent years, thanks to easy access to ubiquitous sensing and data from the web.