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20072026
most citedA Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges

2.7k citations

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21 papers · 1 filter

cs.CL2025★ 12 cited

Robust Multimodal Sentiment Analysis of Image-Text Pairs by Distribution-Based Feature Recovery and Fusion

Daiqing Wu, Dongbao Yang, Yu Zhou +1

As posts on social media increase rapidly, analyzing the sentiments embedded in image-text pairs has become a popular research topic in recent years. Although existing works achiev…

cs.CV2025★ 6 cited

Bridging Visual Affective Gap: Borrowing Textual Knowledge by Learning from Noisy Image-Text Pairs

Daiqing Wu, Dongbao Yang, Yu Zhou +1

Visual emotion recognition (VER) is a longstanding field that has garnered increasing attention with the advancement of deep neural networks. Although recent studies have achieved…

cs.CR2025★ 1 cited

Uncovering and Aligning Anomalous Attention Heads to Defend Against NLP Backdoor Attacks

Haotian Jin, Yang Li, Haihui Fan +3

Backdoor attacks pose a serious threat to the security of large language models (LLMs), causing them to exhibit anomalous behavior under specific trigger conditions. The design of…

cs.CR2025★ 1 cited

Dynamic Vulnerability Patching for Heterogeneous Embedded Systems Using Stack Frame Reconstruction

Ming Zhou, Xupu Hu, Zhihao Wang +4

Existing dynamic vulnerability patching techniques are not well-suited for embedded devices, especially mission-critical ones such as medical equipment, as they have limited comput…

cs.MM2025★ 2 cited

REMOTE: A Unified Multimodal Relation Extraction Framework with Multilevel Optimal Transport and Mixture-of-Experts

Xinkui Lin, Yongxiu Xu, Minghao Tang +4

Multimodal relation extraction (MRE) is a crucial task in the fields of Knowledge Graph and Multimedia, playing a pivotal role in multimodal knowledge graph construction. However,…

cs.CR2025

CIF: A Constrained Inversion Framework for Reliable Message Extraction in Diffusion-Based Generative Steganography

Yuqi Qian, Yun Cao, Meiyang Lv +1

Generative image steganography aims to conceal secret information in generated images without arousing suspicion. However, in practical scenarios involving high-capacity embedding…