5 papers
CEZSAR: A Contrastive Embedding Method for Zero-Shot Action Recognition
Valter Estevam, Rayson Laroca, Helio Pedrini +1
This paper proposes a novel Zero-Shot Action Recognition~(ZSAR) method based on contrastive learning. In ZSAR, we aim to classify examples from classes that were missing during tra…
Self-Organizing Visual Prototypes for Non-Parametric Representation Learning
Thalles Silva, Helio Pedrini, Adín Ramírez Rivera
We present Self-Organizing Visual Prototypes (SOP), a new training technique for unsupervised visual feature learning. Unlike existing prototypical self-supervised learning (SSL) m…
Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features
Thalles Silva, Helio Pedrini, Adín Ramírez Rivera
This paper introduces a novel approach to improving the training stability of self-supervised learning (SSL) methods by leveraging a non-parametric memory of seen concepts. The pro…
Self-supervised Learning of Contextualized Local Visual Embeddings
Thalles Santos Silva, Helio Pedrini, Adín Ramírez Rivera
We present Contextualized Local Visual Embeddings (CLoVE), a self-supervised convolutional-based method that learns representations suited for dense prediction tasks. CLoVE deviate…
SelfGraphVQA: A Self-Supervised Graph Neural Network for Scene-based Question Answering
Bruno Souza, Marius Aasan, Helio Pedrini +1
The intersection of vision and language is of major interest due to the increased focus on seamless integration between recognition and reasoning. Scene graphs (SGs) have emerged a…