Understanding the world from multiple perspectives is essential for intelligent systems operating together, where segmenting common objects across different views remains an open problem. We introduce a new approach that re-defines cross-image segmentation by treating it as a mask matching task. Our method consists of: (1) A Mask-Context Encoder that pools dense DINOv2 semantic features to obtain discriminative object-level representations from FastSAM mask candidates, (2) a Ego↔Exo Cross-Attention that fuses multi-perspective observations, (3) a Mask Matching contrastive loss that aligns cross-view features in a shared latent space and, (4) a Hard Negative Adjacent Mining strategy to encourage the model to better differentiate between nearby objects. O-MaMa achieves the state of the art in the Ego-Exo4D Correspondences benchmark.

Action recognition is an essential task in egocentric vision due to its wide range of applications across many fields. While deep learning methods have been proposed to address this task, most rely on a single modality, typically video. However, including additional modalities may improve the robustness of the approaches to common issues in egocentric videos, such as blurriness and occlusions. Recent efforts in multimodal egocentric action recognition often assume the availability of all modalities, leading to failures or performance drops when any modality is missing. To address this, we introduce an efficient multimodal knowledge distillation approach for egocentric action recognition that is robust to missing modalities (KARMMA) while still benefiting when multiple modalities are available. Our method focuses on resource-efficient development by leveraging pre-trained models as unimodal feature extractors in our teacher model, which distills knowledge into a much smaller and faster student model. Experiments on the Epic-Kitchens and Something-Something datasets demonstrate that our student model effectively handles missing modalities while reducing its accuracy drop in this scenario.

One of the main challenges of visual prostheses is to augment the perceived information to improve the experience of its wearers. Given the limited access to implanted patients, in order to facilitate the experimentation of new techniques, this is often evaluated via Simulated Prosthetic Vision (SPV) with sighted people. In this work, we introduce a novel SPV framework and implementation that presents major advantages with respect to previous approaches. First, it is integrated into a robotics framework, which allows us to benefit from a wide range of methods and algorithms from the field (e.g. object recognition, obstacle avoidance, autonomous navigation, deep learning). Second, we go beyond traditional image processing with 3D point clouds processing using an RGB-D camera, allowing us to robustly detect the floor, obstacles and the structure of the scene. Third, it works either with a real camera or in a virtual environment, which gives us endless possibilities for immersive experimentation through a head-mounted display. Fourth, we incorporate a validated temporal phosphene model that replicates time effects into the generation of visual stimuli. Finally, we have proposed, developed and tested several applications within this framework, such as avoiding moving obstacles, providing a general understanding of the scene, staircase detection, helping the subject to navigate an unfamiliar space, and object and person detection. We provide experimental results in real and virtual environments.

Convolution kernels are the basic structural component of convolutional neural networks (CNNs). In the last years there has been a growing interest in fisheye cameras for many applications. However, the radially symmetric projection model of these cameras produces high distortions that affect the performance of CNNs, especially when the field of view is very large. In this work, we tackle this problem by proposing a method that leverages the calibration of cameras to deform the convolution kernel accordingly and adapt to the distortion. That way, the receptive field of the convolution is similar to standard convolutions in perspective images, allowing us to take advantage of pre-trained networks in large perspective datasets. We show how, with just a brief fine-tuning stage in a small dataset, we improve the performance of the network for the calibrated fisheye with respect to standard convolutions in depth estimation and semantic segmentation.