8 accepted papers
Existing fair multi-view clustering methods typically suffer from a severe trade-off between clustering utility and fairness, while incurring prohibitive quadratic complexity on large-scale datasets. To address these challenges, we propose Causal Disentangled Anchor Learning (CDAL), a novel framewor…
Vehicle trajectory prediction is critical for safe and efficient autonomous driving. However, its generalization and scalability are hindered by heavy reliance on real-time, online priors. To break this bottleneck, we introduce RAG-TP, a framework reframing the problem from relying on uncertain onli
Recently, substantial progress has been achieved in leveraging deep learning models for tabular data learning. However, despite significant advancements, the predominant focus of these endeavors has been on augmenting the performance of contemporary deep learning models. Consequently, the interpreta…
Multi-layer networks (MLN) effectively model interactions across layers, and the network disintegration (ND) problem yields significant importance in the analysis of MLN. Unfortunately, previous advances in ND for single-layer networks exhibits inefficiency and lack of scalability when extended to M…
Infrared-visible image fusion (IVF) and infrared-to-visible image translation (I2V) are two closely related tasks in multimodal image processing, both aimed at combining or transforming infrared and visible modalities to enhance image information content. Existing methods typically focus on either f…
Standing out as one of the most widely used tools in Cross-Domain Few-Shot Learning (CDFSL), data augmentation forms the bedrock of numerous recent advancements. However, the current augmentations in CDFSL are limited in their ability to modify high-level semantic attributes, resulting in a lack of…
Visual Question Answering (VQA) models often prioritize language cues over visual knowledge, leading to the "language prior" phenomenon. To address this, researchers have proposed methods to balance language and image information during training and inference. However, these approaches often struggl…
In Electronic Support Measure (ESM) systems, the recognition of radar emitters stands as a pivotal yet intricate task. The complex electromagnetic environments, however, often hinders the collection of clean radar signal data, and results in data with different noise levels. Consequently, formulatin…