Didac Suris — accepted AI-conference papers

2026

The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification

Dante Wasmuht, Otto Brookes, Maximilian Schall, Pablo Palencia, Christopher Beirne, Tilo Burghardt +18

CVPR 2026

Automated video analysis is critical for wildlife conservation. A foundational task in this domain is multi-animal tracking (MAT), which underpins applications such as individual re-identification and behavior recognition. However, existing datasets are limited in scale, constrained to a few species

Cited by 0 Source ↗Scholar ↗

2021

Learning the Predictability of the Future

Didac Suris, Ruoshi Liu, Carl Vondrick

CVPR 2021poster

We introduce a framework for learning from unlabeled video what is predictable in the future. Instead of committing up front to features to predict, our approach learns from data which features are predictable. Based on the observation that hyperbolic geometry naturally and compactly encodes hierarc…

Cited by 85 PDF ↗code ↗Scholar ↗

2019

Learning Words by Drawing Images

Didac Suris, Adria Recasens, David Bau, David Harwath, James Glass, Antonio Torralba

CVPR 2019poster

We propose a framework for learning through drawing. Our goal is to learn the correspondence between spoken words and abstract visual attributes, from a dataset of spoken descriptions of images. Building upon recent findings that GAN representations can be manipulated to edit semantic concepts in th…

Cited by 13 PDF ↗Scholar ↗

2018

Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input

David Harwath, Adria Recasens, Didac Suris, Galen Chuang, Antonio Torralba, James Glass

ECCV 2018poster

In this paper, we explore neural network models that learn to associate segments of spoken audio captions with the semantically relevant portions of natural images that they refer to. We demonstrate that these audio-visual associative localizations emerge from network-internal representations learne…

Cited by 254 Source ↗PDF ↗Scholar ↗

2018

Overcoming Catastrophic Forgetting with Hard Attention to the Task

Joan Serra, Didac Suris, Marius Miron, Alexandros Karatzoglou

ICML 2018oral

Catastrophic forgetting occurs when a neural network loses the information learned in a previous task after training on subsequent tasks. This problem remains a hurdle for artificial intelligence systems with sequential learning capabilities. In this paper, we propose a task-based hard attention mec…

Cited by 1374 Source ↗PDF ↗code ↗Scholar ↗