14 citations · 21 across the 8 of their papers we have counts for
8 papers
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,…
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…
BCE vs. CE in Deep Feature Learning
Qiufu Li, Huibin Xiao, Linlin Shen
When training classification models, it expects that the learned features are compact within classes, and can well separate different classes. As the dominant loss function for tra…
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…
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…
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…
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…
UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition
Qiufu Li, Xi Jia, Jiancan Zhou +2
Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require…