8 accepted papers
This paper focuses on the basic structure of Chinese characters: semantic-phonetic compound characters. This paper takes advantage of this feature of Chinese characters and innovatively proposes a Chinese speech-processing method based on character shape. We use the association between the character…
Existing multimodal models typically assume the availability of all modalities, leading to significant performance degradation when certain modalities are missing. Recent methods have introduced prompt learning to adapt pretrained models to incomplete data, achieving remarkable performance when the
Audio-visual speech recognition (AVSR) aims to enhance the robustness of an automatic speech recognition (ASR) systems by incorporating visual information from lip movements, especially in challenging noisy environments. Nevertheless, most current approaches either involve training from scratch or f…
The utilization of multimodal cues enhances the effectiveness of specific cognitive tasks in audio-visual learning. However, on the one hand, designing a unified model for multimodal learning poses challenges due to the presence of information redundancy and modality noise. On the other hand, existi…
Dataset condensation aims to synthesize compact yet informative datasets that retain the training efficacy of full-scale data, offering substantial gains in efficiency. Recent studies reveal that the condensation process can be vulnerable to backdoor attacks, where malicious triggers are injected in…
Object recognition in open environments, e.g., video surveillance, poses significant challenges due to the inclusion of unknown and multi-granularity tasks (MGT). However, recent methods exhibit limitations as they struggle to capture subtle differences between different parts within an object and a…
Conventional Federated Domain Adaptation (FDA) approaches usually demand an abundance of assumptions, which makes them significantly less feasible for real-world situations and introduces security hazards. This paper relaxes the assumptions from previous FDAs and studies a more practical scenario na…
In this paper, we propose KGenre, a knowledge-embedded music representation learning framework for improved genre classification. We construct the knowledge graph from the metadata in the open-source FMA-medium and OpenMIC-2018 datasets, with no extra information/effort required. KGenre then mines t…