8 accepted papers
Training a unified model integrating video-to-audio (V2A), text-to-audio (T2A), and joint video-text-to-audio (VT2A) generation offers significant application flexibility, yet faces two unexplored foundational challenges: (1) the scarcity of high-quality audio captions with tight V-A-T alignment, le
This paper introduces Swap Forward (SaFa), a modality-agnostic and efficient method to generate seamless and coherent long spectrum and panorama using a latent swap joint diffusion process across multi-views. We first investigate spectrum aliasing problem in spectrum-based audio generation caused by…
User-defined keyword spotting (KWS) enhances the user experience by allowing individuals to customize keywords. However, in open-vocabulary scenarios, most existing methods commonly suffer from high false alarm rates with confusable words and are limited to either audio-only or text-only enrollment.…
This paper tackles the emerging challenge of multi-view virtual try-on, utilizing both front- and back-view clothing images as inputs. Extending frontal try-on methods to a multi-view context is not straightforward. Simply concatenating the two input views or encoding their features for a generative…
Advanced Audio-Visual Speech Recognition (AVSR) systems have been observed to be sensitive to missing video frames performing even worse than single-modality models. While applying the common dropout techniques to the video modality enhances robustness to missing frames it simultaneously results in…
In this study, we aim to improve our recent hierarchical information fusion system for multi-modal emotion recognition challenge (MER 2023) in both efficiency and performance. Specifically, we extract robust acoustic and visual representations from pre-trained models and fuse them together in differ…
Previous Multimodal Information based Speech Processing (MISP) challenges mainly focused on audio-visual speech recognition (AVSR) with commendable success. However, the most advanced back-end recognition systems often hit performance limits due to the complex acoustic environments. This has prompte…
The Multimodal Information based Speech Processing (MISP) 2022 challenge aimed to enhance speech processing performance in harsh acoustic environments by leveraging additional modalities such as video or text. The challenge included two tracks: audio-visual speaker diarization (AVSD) and audio-visua…