23 accepted papers
Diffusion models have advanced fine-grained garment generation, yet balancing controllability, efficiency, and texture fidelity remains challenging. Adapter-based methods often yield incoherent details, while full fine-tuning is computationally expensive and prone to overwriting pretrained priors. T
Meta-learning for Bayesian optimization accelerates optimization by leveraging knowledge from previous tasks, but existing methods optimize for average performance and fail on challenging outlier tasks critical in practice. These limitations become particularly severe when target tasks exhibit distr
Occluded visible-infrared person re-identification (Occluded VI-ReID) remains difficult due to modality heterogeneity and occlusions, both of which break structural consistency and weaken cross-modality feature alignment. Existing methods rely mainly on spatial-domain cues (such as local body parts
In structured light systems, the accuracy of measurement notably diminishes when assessing complex texture objects, especially encountering boundaries between various colors. To address this challenge, this paper meticulously analyzes and establishes an error model, elaborating the correlation betwe…
Few-shot named entity recognition is to identify named entities in scenarios where labeled data is scarce. Existing prototype building methods ignore the use of class semantic and it is difficult to obtain accurate prototype representations only by relying on few support samples. In this paper, we p…
With the development of embodied intelligence, many studies have made progress by incorporating scene graphs and GNN into task planning. However, most methods still face challenges in fully capturing the sequential relationships between agent actions and the environment, making it difficult to handl
In structured light systems, measurement accuracy tends to decline significantly when evaluating complex textured surfaces, particularly at boundaries between different colors. To address this issue, this paper conducts a detailed analysis to develop an error model that illustrates the relationship…
Neural processes (NPs) are a promising paradigm to enable skill transfer learning across tasks with the aid of the distribution of functions. The previous NPs employ the empirical risk minimization principle in optimization. However, the fast adaption ability to different tasks can vary widely, and…
Learning-based methods have made promising advances in low-light RAW image enhancement, while their capability to extremely dark scenes where the environmental illuminance drops as low as 0.0001 lux remains to be explored due to the lack of corresponding datasets. To this end, we propose a paired-to…
Knowledge graphs often face the issue of missing links. Addressing the problem of reasoning about and completing these missing entities or relations has become a key research focus. However, existing graph attention networks rely on connections within the graph for information propagation and aggreg…
Sequential recommender systems aim to learn user preferences through historical interaction sequences. User interactions are driven both by popular trends and personal interests, introducing two types of noise: popular choices triggered by conformist behavior and irrelevant terms that do not reflect…
Recent progress in large language models (LLMs) has opened new possibilities for mental health support, yet current approaches lack realism in simulating specialized psychotherapy and fail to capture therapeutic progression over time. Narrative therapy, which helps individuals transform problematic
Children with Autism Spectrum Disorder (ASD) often misunderstand social situations and struggle to participate in daily routines. Social Stories™ are traditionally crafted by psychology experts under strict constraints to address these challenges but are costly and limited in diversity. As Large Lan…
Recommender systems based on Matrix Factorization are widely used. However, they can easily suffer from the problem of overrecommendation of popular items, i.e., popularity bias. To mitigate popularity bias, current methods often uniformly model interactions' popularity bias degree considering user…
Recent advancements in noisy multi-label text classification have primarily relied on the class-conditional noise (CCN) assumption, which treats each label independently undergoing label flipping to generate noisy labels. However, in real-world scenarios, noisy labels often exhibit dependencies with…
In noisy label learning, instance selection based on small-loss criteria has been proven to be highly effective. However, in the case of noisy multi-label text classification (NMLTC), the presence of noise is not limited to the instance-level but extends to the (instance-label) pair-level.This gives…
In extreme multi-label text classification (XMC), labels usually follow a long-tailed distribution, where most labels only contain a small number of documents and limit the performance of XMC. Data augmentation (DA) is a simple but effective strategy to solve such low-resource problems. In this pape…
ImageNet pre-training has enabled state-of-the-art results on many tasks. In spite of its recognized contribution to generalization, we observed in this study that ImageNet pre-training also transfers adversarial non-robustness from pre-trained model into fine-tuned model in the downstream classific…
Multi-label text classification (MLTC) involves tagging a document with its most relevant subset of labels from a label set. In real applications, labels usually follow a long-tailed distribution, where most labels (called as tail-label) only contain a small number of documents and limit the perform…
Unlike traditional statistical models depending on hand-specified priors, neural processes (NPs) have recently emerged as a class of powerful neural statistical models that combine the strengths of neural networks and stochastic processes. NPs can define a flexible class of stochastic processes well…
Synthesizing pseudo samples is currently the most effective way to solve the Generalized Zero Shot Learning (GZSL) problem. Most models achieve competitive performance but still suffer from two problems: (1) Feature confounding, the overall representations confound task-correlated and task-independe…
Soft robotics has advanced the field of biomedical engineering by creating safer technologies for interfacing with the human body. One of the challenges in this field is the realization of modular soft basic constituents and accessible assembly methods to increase the versatility of soft robots. We…
Multi-label problems arise in various domains including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do not adequately address two key challenges: exploiting correlations between labels…