27 citations · 53 across the 9 of their papers we have counts for
19 papers
ViewNeRF: Unsupervised Viewpoint Estimation Using Category-Level Neural Radiance Fields
Octave Mariotti, Oisin Mac Aodha, Hakan Bilen
We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimation method that learns to predict category-level viewpoints directly from images during training. While NeRF i…
ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation
Octave Mariotti, Oisin Mac Aodha, Hakan Bilen
Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are…
An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition
Kiyoon Kim, Davide Moltisanti, Oisin Mac Aodha +1
Precisely naming the action depicted in a video can be a challenging and oftentimes ambiguous task. In contrast to object instances represented as nouns (e.g. dog, cat, chair, etc.…
SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models
Omiros Pantazis, Gabriel Brostow, Kate Jones +1
Vision-language models such as CLIP are pretrained on large volumes of internet sourced image and text pairs, and have been shown to sometimes exhibit impressive zero- and low-shot…
Focus on the Positives: Self-Supervised Learning for Biodiversity Monitoring
Omiros Pantazis, Gabriel Brostow, Kate Jones +1
We address the problem of learning self-supervised representations from unlabeled image collections. Unlike existing approaches that attempt to learn useful features by maximizing…
Multi-Label Learning from Single Positive Labels
Elijah Cole, Oisin Mac Aodha, Titouan Lorieul +3
Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is co…