15 accepted papers
Multi-view learning has been widely applied for sleep stage classification using multi-modal data. However, existing methods typically assume that different modalities are well-aligned, which is often unattainable in real-world scenarios, thereby compromising the reliability of the staging results. …
Recent advances in multimodal language models (MLLMs) have made thinking with images a dominant paradigm for multimodal reasoning. However, existing methods still fail to ensure evidence–answer consistency, where correct answers must be supported by correct visual evidence. To address this issue, we…
Existing solutions on differentially private deep learning (DPDL) either require the assumption of a trusted data server (centralized DPDL) or suffer from poor utility (local DPDL); and hence their adoptions are hampered in real-world scenarios.We present CRYPTDP, a crypto-assisted differentially pr
Face anti-spoofing (FAS) is crucial for protecting facial recognition systems from presentation attacks. Previous methods approached this task as a classification problem, lacking interpretability and reasoning behind the predicted results. Recently, multimodal large language models (MLLMs) have sho
Truth inference is a critical technique for aggregating noisy and biased multi-class classification annotations. State-of-the-art approaches model each annotator using an individual confusion matrix. While well-grounded, they suffer from two fundamental bottlenecks: 1) confusion matrices are underfi…
Large-scale pre-trained vision-language models (VLMs) like CLIP show exceptional performance and zero-shot generalization. However, their reliability may be severely undermined by a critical vulnerability to subtle adversarial perturbations. Our work reveals a critical cross-modal vulnerability: vis
Safety alignment is critical in pre-trained large language models (LLMs) to generate responses aligned with human values and refuse harmful queries. Unlike LLM, the current safety alignment of VLMs is often achieved with post-hoc safety fine-tuning. However, these methods are less effective to white
Backdoor attacks in federated learning (FL) face challenges such as lower attack success rates and compromised main task accuracy (MA) compared to local training. Existing methods like distributed backdoor attack (DBA) mitigate these issues by modifying malicious clients’ updates and partitioning gl…
Aspect-level sentiment classification (ASC) is a significant problem in fine-grained sentiment analysis, which automatically predicts the sentiment polarity of a given aspect in a sentence. Dependency tree-based graph convolutional networks have been widely studied for their ability to effectively c…
Inquiry conversation is a common form of conversation that aims to complete the investigation (e.g., court hearing, medical consultation and police interrogation) during which a series of focus shifts occurs. While many models have been proposed to generate a smooth response to a given conversation…
As a hot research topic in the field of computer vision, blind image quality assessment (BIQA) can provide high-quality images for end-users and promote the development of other fields of computer vision. Although the existing BIQA based on convolution neural networks has made significant progress i…
Deep-learning based methods that aim to extract effective high-level features have steadily improved the performance on the speech emotion recognition. However, low-level features that contain important emotion-related information have not gained much attention. In this paper, we propose a novel low…
Multi-view Speech Emotion Recognition (SER) based on the pre-trained model has achieved success in speaker-independent scenarios. However, the existing SER methods rely on excessive feature views and have complicated feature fusion strategies. In this paper, we propose a novel method to learn effect…
Speech emotion recognition (SER) based on multi-view learning has made some progress on speaker-independent scenarios. How-ever, the existing SER methods always rely on excessive feature views and ignore the importance of heterogeneous feature learning. In this paper, we propose a novel multi-level…
Legal judgment prediction (LJP) is a fundamental task in legal AI, which aims to assist the judge to hear the case and determine the judgment. The legal judgment usually consists of the law article, charge, and term of penalty. In the real trial scenario, the judge usually makes the decision step-by…