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20212023
most citedNAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction

113 citations · 292 across the 14 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CV2023

All-pairs Consistency Learning for Weakly Supervised Semantic Segmentation

Weixuan Sun, Yanhao Zhang, Zhen Qin +5

In this work, we propose a new transformer-based regularization to better localize objects for Weakly supervised semantic segmentation (WSSS). In image-level WSSS, Class Activation…

cs.CV2023

View Consistent Purification for Accurate Cross-View Localization

Shan Wang, Yanhao Zhang, Akhil Perincherry +2

This paper proposes a fine-grained self-localization method for outdoor robotics that utilizes a flexible number of onboard cameras and readily accessible satellite images. The pro…

cs.CV2023★ 3 cited

TALL: Thumbnail Layout for Deepfake Video Detection

Yuting Xu, Jian Liang, Gengyun Jia +3

The growing threats of deepfakes to society and cybersecurity have raised enormous public concerns, and increasing efforts have been devoted to this critical topic of deepfake vide…

cs.CV2023★ 16 cited

An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems

Weixuan Sun, Zheyuan Liu, Yanhao Zhang +2

The Segment Anything Model (SAM) has demonstrated exceptional performance and versatility, making it a promising tool for various related tasks. In this report, we explore the appl…

cs.CV2023★ 2 cited

Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning

Weixuan Sun, Jiayi Zhang, Jianyuan Wang +6

Self-supervised audio-visual source localization aims to locate sound-source objects in video frames without extra annotations. Recent methods often approach this goal with the hel…

cs.CV2023★ 1 cited

GAM : Gradient Attention Module of Optimization for Point Clouds Analysis

Haotian Hu, Fanyi Wang, Jingwen Su +5

In point cloud analysis tasks, the existing local feature aggregation descriptors (LFAD) are unable to fully utilize information in the neighborhood of central points. Previous met…