17 accepted papers
Evidential Deep Learning (EDL) enables trustworthy multi-view classification, yet suffers from a critical vulnerability: the Scale Mismatch Problem. We theoretically demonstrate that existing evidential fusion rules erroneously equate logit magnitude with semantic confidence, rendering them suscepti…
Concept Embedding Models (CEMs) advance interpretable AI by extending Concept Bottleneck Models (CBMs) through semantic concept embeddings, providing an important solution in high-stakes domains such as medical diagnosis where accuracy and interpretability are critical. However, a fundamental limita…
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by incorporating external knowledge bases, significantly improving their factual accuracy and contextual relevance. However, this integration also introduces new privacy vulnerabilities. Existing privacy attacks on RAG…
Retrieval-augmented generation (RAG) systems suffer from a fundamental functional misalignment where retrievers optimize for semantic relevance, often recalling documents with high background utility but factually erroneous answer spans that generators blindly adopt as cognitive shortcuts. To resolv…
Large Language Models (LLMs) show strong reasoning abilities, yet their reliability is hindered by hallucinations, where fluent reasoning becomes factually or logically incorrect. Most existing uncertainty-based detectors rely on sequence-level averaging, which ignores the step-wise dynamics of reas…
Uncertainty Quantification (UQ) is critical for detecting hallucinations in black-box Large Vision-Language Models (LVLMs). However, prevailing methods like Discrete Semantic Entropy (DSE) are unreliable, as their scores are primarily dominated by the number of semantic clusters. This renders them i
Evidential Deep Learning (EDL) has emerged as a promising framework for uncertainty estimation in classification tasks by modeling predictive uncertainty with a Dirichlet prior. Despite its empirical success, prior work has primarily focused on the probabilistic properties of the Dirichlet distribut…
Multi-view classification based on evidence theory aims to enhance result reliability by effectively quantifying prediction uncertainty at the evidence level, particularly when dealing with low-quality views. However, these methods face limitations in real-world applications due to the sensitivity o…
Trusted multi-view classification (TMVC) addresses variations in data quality by evaluating the reliability of each view based on prediction uncertainty at the evidence level, reducing the impact of low-quality views commonly encountered in real-world scenarios. However, existing TMVC methods often…
Autonomous exploration of unknown environments is a critical task in robotic search and rescue operations. Recently, hierarchical planning frameworks have gained significant attention for their potential to enhance exploration efficiency. However, most existing approaches struggle with efficient exp
Uncertainty estimation is crucial for ensuring the reliability of machine learning models in safety-critical applications. Evidential Deep Learning (EDL) offers a principled framework by modeling predictive uncertainty through Dirichlet distributions over class probabilities. However, existing EDL m…
Evidential Deep Learning (EDL), grounded in Evidence Theory and Subjective Logic (SL), provides a robust framework to estimate uncertainty for out-of-distribution (OOD) detection alongside traditional classification probabilities.However, the EDL framework is constrained by its focus on evidence tha…
Multi-view deep classification expects to obtain better classification performance than using a single view. However, due to the uncertainty and inconsistency of data sources, adding data views does not necessarily lead to the performance improvements in multi-view classification. How to avoid worse…
Real-world classification tasks often show an extremely imbalanced problem. The extreme imbalance will cause a strong bias that the decision boundary of the classifier is completely dominated by the categories with abundant samples, which are also called the head categories. Current methods have all…
As in-the-wild data are increasingly involved in the training stage, machine learning applications become more susceptible to data poisoning attacks. Such attacks typically lead to test-time accuracy degradation or controlled misprediction. In this paper, we investigate the third type of exploitatio…
Multi-view deep learning is performed based on the deep fusion of data from multiple sources, i.e. data with multiple views. However, due to the property differences and inconsistency of data sources, the deep learning results based on the fusion of multi-view data may be uncertain and unreliable. I…
Point set registration (PSR) is a fundamental problem in computer vision and pattern recognition, and it has been successfully applied to many applications. Although widely used, existing PSR methods cannot align point sets robustly under degradations, such as deformation, noise, occlusion, outlier,…