Audio-visual learning, aimed at exploiting the relationship between audio and visual
modalities, has drawn considerable attention since deep learning started to be used …

Sight and hearing are two senses that play a vital role in human communication and scene
understanding. To mimic human perception ability, audio-visual learning, aimed at …

In this paper, we introduce a novel problem of audio-visual event localization in
unconstrained videos. We define an audio-visual event as an event that is both visible and …

The objectives of this work are cross-modal text-audio and audio-text retrieval, in which the
goal is to retrieve the audio content from a pool of candidates that best matches a given …

In this work, we present the Textless Vision-Language Transformer (TVLT), where
homogeneous transformer blocks take raw visual and audio inputs for vision-and-language …

We address the problem of cross-modal fine-grained action retrieval between text and video.
Cross-modal retrieval is commonly achieved through learning a shared embedding space …

The recent success of the generative model shows that leveraging the multi-modal
embedding space can manipulate an image using text information. However, manipulating …

Despite the success of multimodal learning in cross-modal retrieval task, the remarkable
progress relies on the correct correspondence among multimedia data. However, collecting …

In this paper, we present MovieFactory, a powerful framework to generate cinematic-picture
(3072x1280), film-style (multi-scene), and multi-modality (sounding) movies on the demand …

We present an approach for recommending a music track for a given video, and vice versa,
based on both their temporal alignment and their correspondence at an artistic level. We …