9 accepted papers
Multimodal learning, while contributing to numerous success stories across various fields, faces the challenge of prohibitively expensive manual annotation. To address the scarcity of annotated data, a popular solution is unsupervised domain adaptation, which has been extensively studied in unimodal
Multimodal learning has celebrated remarkable success across diverse areas, yet faces the challenge of prohibitively expensive data collection and annotation when adapting models to new environments. In this context, domain adaptation has gained growing popularity as a technique for knowledge transf…
Although transformer-based methods excel in stereo image super-resolution, the full potential of the distinctive, complementary information inherent in stereo images has not been fully utilized. We propose a Disparity-Guided Cross-View Transformer (DCT) to extract features across dimensions and view…
Meta-learning methods typically follow a two-loop framework, where each loop potentially suffers from notorious overfitting, hindering rapid adaptation and generalization to new tasks. Existing methods address this by enhancing the mutual-exclusivity or diversity of training samples, but these data
Facial expression recognition (FER) faces unique challenges from expression ambiguity and noisy labels, degrading performance in real-world applications. While leveraging attention, existing methods frequently neglect attention dynamic mechanism of dispersion followed by focus and the spatially stru…
Large language models (LLMs) demonstrate advanced reasoning abilities, enabling robots to understand natural language instructions and generate high-level plans with appropriate grounding. However, LLM hallucinations present a significant challenge, often leading to overconfident yet potentially mis…
This paper investigates unsupervised multimodal domain adaptation for multimodal emotion recognition, which is a solution for data scarcity yet remains under studied. Due to the varying distribution discrepancies of different modalities between source and target domains, the primary challenge lies i…
Multimodal learning is susceptible to modality missing, which poses a major obstacle for its practical applications and, thus, invigorates increasing research interest. In this paper, we investigate two challenging problems: 1) when modality missing exists in the training data, how to exploit the in…
"Open-Vocabulary Video Instance Segmentation (VIS) is attracting increasing attention due to its ability to segment and track arbitrary objects. However, the recent Open-Vocabulary VIS attempts obtained unsatisfactory results, especially in terms of generalization ability of novel categories. We dis…