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20232026
most citedRediscovering BCE Loss for Uniform Classification

14 citations · 20 across the 7 of their papers we have counts for

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cs.CV2026

More than the Sum: Panorama-Language Models for Adverse Omni-Scenes

Weijia Fan, Ruiping Liu, Jiale Wei +7

Existing vision-language models (VLMs) are tailored for pinhole imagery, stitching multiple narrow field-of-view inputs to piece together a complete omni-scene understanding. Yet,…

cs.CV2025

DAP-MAE: Domain-Adaptive Point Cloud Masked Autoencoder for Effective Cross-Domain Learning

Ziqi Gao, Qiufu Li, Linlin Shen

Compared to 2D data, the scale of point cloud data in different domains available for training, is quite limited. Researchers have been trying to combine these data of different do…

cs.CV2024

WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration

Xinxing Cheng, Xi Jia, Wenqi Lu +4

Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense d…

cs.CV2024

EPL: Empirical Prototype Learning for Deep Face Recognition

Weijia Fan, Jiajun Wen, Xi Jia +3

Prototype learning is widely used in face recognition, which takes the row vectors of coefficient matrix in the last linear layer of the feature extraction model as the prototypes…

cs.CV202414 cited

Rediscovering BCE Loss for Uniform Classification

Qiufu Li, Xi Jia, Jiancan Zhou +2

This paper introduces the concept of uniform classification, which employs a unified threshold to classify all samples rather than adaptive threshold classifying each individual sa…

cs.CV20241 cited

Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

Songhe Deng, Wei Zhuo, Jinheng Xie +1

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only imag…