13 accepted papers
Multimodal large language models (MLLMs) frequently suffer from object hallucinations, yet the visual perceptual mechanism underlying this failure remains poorly understood. In this work, we reveal that hallucinations are strongly associated with a human-like attention distraction phenomenon, where …
Auto-Regressive (AR) models with Monte Carlo Tree Search (MCTS) are a dominant paradigm for achieving “System 2” reasoning. However, this approach suffers from significant latency due to the serial, token-by-token generation mechanism of AR models. In contrast, Diffusion Large Language Models (dLLMs…
Feature dynamics have emerged as a critical topic about open-environment learning due to the instability of feature availability. While traditional feature evolution targets single-label tasks, multi-label learning is essential to accommodate the exploding annotation spaces. However, multi-label cl
Partial Label Learning (PLL) aims to train multi-class classifiers from examples where each instance is associated with a set of candidate labels, among which the ground-truth label is assumed to be included. While most existing studies assume that partial labels are both instance-independent and re
Continual learning aims to learn multiple tasks sequentially while preserving prior knowledge, but faces the challenge of catastrophic forgetting when adapting to new tasks. Recently, approaches leveraging pre-trained models have gained increasing popularity in mitigating this issue, due to the stro…
Second-order Online Kernel Learning (OKL) has attracted considerable research interest due to its promising predictive performance in streaming environments. However, existing second-order OKL approaches suffer from at least quadratic time complexity with respect to the pre-set budget, rendering the
In open-environment applications, data are often collected from heterogeneous modalities with distinct encodings, resulting in feature space heterogeneity. This heterogeneity inherently induces label shift, making cross-modal knowledge transfer particularly challenging when the source and target dat…
Label shift, which investigates the adaptation of label distributions between the fixed source and target domains, has attracted significant research interests and broad applications in offline settings. In real-world scenarios, however, data often arrives as a continuous stream. Addressing label sh…
In many real applications, the data attributes are incremental and the samples are stored with accumulated feature spaces gradually. Although there are several elegant approaches to tackling this problem, the theoretical analysis is still limited. There exist at least two challenges and fundamental
Label shift is a prevalent phenomenon encountered in open environments, characterized by a notable discrepancy in the label distributions between the source (training) and target (test) domains, whereas the conditional distributions given the labels remain invariant. Existing label shift methods ado
Multi-view multi-label learning has become a research focus for describing objects with rich expressions and annotations. However, real-world data often contains numerous unlabeled instances, due to the high cost and technical limitations of manual labeling. This crucial problem involves three main…
Multi-view multi-label learning typically suffers from dual data incompleteness due to limitations in feature storage and annotation costs. The interplay of hetero geneous features, numerous labels, and missing information significantly degrades model performance. To tackle the complex yet highly…
Learning Partial Differential Equations (LPDEs) from training data for particular tasks has been successfully applied to many image processing problems. In this paper, we aim to learn compact Partial Differential Equations (LCPDEs) for color image tasks by proposing a more effective algorithm. The L…