We introduce a structured-noise approach to masked modeling that generalizes across video, audio, and other modalities, improving self-supervised representation learning. This work was selected for oral presentation at ECCV 2026.
We propose CURE, a curriculum-guided multi-task training framework for reliable, anatomy-grounded radiology report generation. Developed with collaborators at KAUST and Pontificia Universidad Católica de Chile, CURE was selected for oral presentation …
UnMix-NeRF brings spectral unmixing into Neural Radiance Fields, enabling spectral-aware 3D scene reconstruction. See the [project page](https://www.factral.co/UnMix-NeRF/) for more details.
We explore data-independent masking strategies for Masked AutoEncoders (MAE), showing that carefully designed structured noise masks can match or improve upon learned/data-dependent masking, while being simpler and more efficient. See our [Project …
The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition. In this paper, we propose an optimizing framework to provide robust visual privacy protection …
Image captioning is a challenging task that connects two major artificial intelligence fields: computer vision and natural language processing. Image captioning models use traditional images to generate a natural language description of the scene. …
The widespread use of always-connected digital cameras in our everyday life has led to increasing concerns about the users privacy and security. How to develop privacy-preserving computer vision systems? In particular, we want to prevent the camera …
In this project, we design the camera lens to perform human pose estimation while preserving users’ privacy.