Zobrazeno 1 - 10
of 318
pro vyhledávání: '"Deng, Weihong"'
The study of oracle characters plays an important role in Chinese archaeology and philology. However, the difficulty of collecting and annotating real-world scanned oracle characters hinders the development of oracle character recognition. In this pa
Externí odkaz:
http://arxiv.org/abs/2409.15893
Table-based Question Answering (TQA) involves answering questions based on tabular data. The complexity of table structures and question logic makes this task difficult even for Large Language Models (LLMs). This paper improves TQA performance by lev
Externí odkaz:
http://arxiv.org/abs/2409.05286
SOTA facial expression recognition (FER) methods fail on test sets that have domain gaps with the train set. Recent domain adaptation FER methods need to acquire labeled or unlabeled samples of target domains to fine-tune the FER model, which might b
Externí odkaz:
http://arxiv.org/abs/2408.10614
Autor:
Liu, Xuannan, Li, Zekun, Li, Peipei, Xia, Shuhan, Cui, Xing, Huang, Linzhi, Huang, Huaibo, Deng, Weihong, He, Zhaofeng
Current multimodal misinformation detection (MMD) methods often assume a single source and type of forgery for each sample, which is insufficient for real-world scenarios where multiple forgery sources coexist. The lack of a benchmark for mixed-sourc
Externí odkaz:
http://arxiv.org/abs/2406.08772
Autor:
Qin, Lixiong, Wang, Mei, Liu, Xuannan, Zhang, Yuhang, Deng, Wei, Song, Xiaoshuai, Xu, Weiran, Deng, Weihong
With the comprehensive research conducted on various face analysis tasks, there is a growing interest among researchers to develop a unified approach to face perception. Existing methods mainly discuss unified representation and training, which lack
Externí odkaz:
http://arxiv.org/abs/2403.09500
Autor:
Chen, Zijian, Wang, Mei, Deng, Weihong, Shi, Hongzhi, Wen, Dongchao, Zhang, Yingjie, Cui, Xingchen, Zhao, Jian
2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose. Recent studies focus on RGB-D face recognition to improve robustness by incorporating depth information. However, collecting suf
Externí odkaz:
http://arxiv.org/abs/2403.06529
Autor:
Liu, Xuannan, Li, Peipei, Huang, Huaibo, Li, Zekun, Cui, Xing, Liang, Jiahao, Qin, Lixiong, Deng, Weihong, He, Zhaofeng
The massive generation of multimodal fake news involving both text and images exhibits substantial distribution discrepancies, prompting the need for generalized detectors. However, the insulated nature of training restricts the capability of classic
Externí odkaz:
http://arxiv.org/abs/2403.01988
Facial expression recognition (FER) models are typically trained on datasets with a fixed number of seven basic classes. However, recent research works point out that there are far more expressions than the basic ones. Thus, when these models are dep
Externí odkaz:
http://arxiv.org/abs/2401.12507
Deep neural networks (DNNs) are often prone to learn the spurious correlations between target classes and bias attributes, like gender and race, inherent in a major portion of training data (bias-aligned samples), thus showing unfair behavior and ari
Externí odkaz:
http://arxiv.org/abs/2401.02150
Autor:
Liu, Xuannan, Zhong, Yaoyao, Deng, Weihong, Shi, Hongzhi, Cui, Xingchen, Yin, Yunfeng, Wen, Dongchao
The blooming of social media and face recognition (FR) systems has increased people's concern about privacy and security. A new type of adversarial privacy cloak (class-universal) can be applied to all the images of regular users, to prevent maliciou
Externí odkaz:
http://arxiv.org/abs/2401.01575