14 citations · 20 across the 6 of their papers we have counts for
10 papers · 1 filter
Audio-visual Generalized Zero-shot Learning the Easy Way
Shentong Mo, Pedro Morgado
Audio-visual generalized zero-shot learning is a rapidly advancing domain that seeks to understand the intricate relations between audio and visual cues within videos. The overarch…
Unveiling the Power of Audio-Visual Early Fusion Transformers with Dense Interactions through Masked Modeling
Shentong Mo, Pedro Morgado
Humans possess a remarkable ability to integrate auditory and visual information, enabling a deeper understanding of the surrounding environment. This early fusion of audio and vis…
Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?
Cheng-En Wu, Yu Tian, Haichao Yu +4
Vision-language models such as CLIP learn a generic text-image embedding from large-scale training data. A vision-language model can be adapted to a new classification task through…
Learning State-Aware Visual Representations from Audible Interactions
Himangi Mittal, Pedro Morgado, Unnat Jain +1
We propose a self-supervised algorithm to learn representations from egocentric video data. Recently, significant efforts have been made to capture humans interacting with their ow…
Localizing Visual Sounds the Easy Way
Shentong Mo, Pedro Morgado
Unsupervised audio-visual source localization aims at localizing visible sound sources in a video without relying on ground-truth localization for training. Previous works often se…
The Challenges of Continuous Self-Supervised Learning
Senthil Purushwalkam, Pedro Morgado, Abhinav Gupta
Self-supervised learning (SSL) aims to eliminate one of the major bottlenecks in representation learning - the need for human annotations. As a result, SSL holds the promise to lea…