6 accepted papers
Unifying image clustering across different clustering scenarios remains challenging due to fundamental gaps among tasks. We introduce a Guideline-Driven Image Clustering Agent, the first universal framework that bridges these gaps through textual guidelines. To incorporate complex guidelines without
Emotion recognition is inherently a multimodal problem. Humans use both audible and visual cues to determine a person’s emotions. There has been extensive improvement in the methods we use to fuse audio and visual representations between two unimodal deep-learning models. However, there is a lack of…
Audio-Visual Speech-to-Speech Translation (AVS2S) typically prioritizes improving translation quality and naturalness. However, an equally critical aspect in audio-visual content is lip-synchrony—ensuring that the movements of the lips match the spoken content—essential for maintaining realism in du…
"Recent advancements in audio-visual generative modeling have been propelled by progress in deep learning and the availability of data-rich benchmarks. However, the growth is not attributed solely to models and benchmarks. Universally accepted evaluation metrics also play an important role in advanc…
Representation learning is a challenging, but essential task in audiovisual learning. A key challenge is to generate strong cross-modal representations while still capturing discriminative information contained in unimodal features. Properly capturing this information is important to increase accura…
A challenging task in audiovisual emotion recognition is to implement neural network architectures that can leverage and fuse multimodal information while temporally aligning modalities, handling missing modalities, and capturing information from all modalities without losing information during trai…