584 citations · 1.3k across the 51 of their papers we have counts for
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Center Focusing Network for Real-Time LiDAR Panoptic Segmentation
Xiaoyan Li, Gang Zhang, Boyue Wang +2
LiDAR panoptic segmentation facilitates an autonomous vehicle to comprehensively understand the surrounding objects and scenes and is required to run in real time. The recent propo…
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks
Yang Wang, Bo Dong, Ke Xu +4
Deep Neural Networks (DNNs) are widely used for computer vision tasks. However, it has been shown that deep models are vulnerable to adversarial attacks, i.e., their performances d…
Referring Image Segmentation Using Text Supervision
Fang Liu, Yuhao Liu, Yuqiu Kong +5
Existing Referring Image Segmentation (RIS) methods typically require expensive pixel-level or box-level annotations for supervision. In this paper, we observe that the referring t…
Fast Adversarial Training with Smooth Convergence
Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1
Fast adversarial training (FAT) is beneficial for improving the adversarial robustness of neural networks. However, previous FAT work has encountered a significant issue known as c…
A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
Jiapu Wang, Boyue Wang, Meikang Qiu +8
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and i…
Frame-Event Alignment and Fusion Network for High Frame Rate Tracking
Jiqing Zhang, Yuanchen Wang, Wenxi Liu +4
Most existing RGB-based trackers target low frame rate benchmarks of around 30 frames per second. This setting restricts the tracker's functionality in the real world, especially f…
ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis
Hongchen Tan, Baocai Yin, Kun Wei +2
We propose a novel Text-to-Image Generation Network, Adaptive Layout Refinement Generative Adversarial Network (ALR-GAN), to adaptively refine the layout of synthesized images with…