arXiv subjects

Maria Santos-Villafranca

Publications and source records attributed to Maria Santos-Villafranca.

4 recordsLinked to original sources

arXiv2026

Ego-METAS: Egocentric online Multimodal Energy-efficient Temporal Action Segmentation benchmark

To operate in the physical world, embodied agents must perceive their environment in an "always-on" fashion, selectively accessing the most informative sensors to balance energy constraints and task accuracy. Despite its importance for resource-constrained devices, energy-aware perception remains under-explored, with most prior work assuming unlimited compute. To address this, we introduce Ego-METAS: the first Egocentric online Multimodal Energy-efficient Temporal Action Segmentation benchmark. Ego-METAS provides a unified testbed of more than 100 hours of untrimmed egocentric video from EgoExo4D, CMU-MMAC, and CaptainCook4D, spanning 5 modalities (RGB, audio, gaze, IMU, and monochrome camera). We formulate an online temporal action segmentation task where models must dynamically select which sensors to activate at each timestep while strictly adhering to hardware-representative energy budgets. Alongside the benchmark, we release unified splits, cleaned annotations, pre-extracted features, and a diverse suite of baseline routing policies. Our evaluations show that optimal routing is highly scenario-dependent, and that existing policy-learning methods, designed primarily for trimmed clips, struggle to adapt to continuous, untrimmed environments. However, even simple dynamic fusion of complementary modalities (e.g., via random routing) proves critical for balancing predictive accuracy against strict energy budgets. Ultimately, Ego-METAS provides a standardized foundation to develop robust, cost-aware policies for autonomous, always-on embodied AI.

cs.CV↗

arXiv2026

Multimodal Knowledge Distillation for Egocentric Action Recognition Robust to Missing Modalities

Egocentric action recognition enables robots to facilitate human-robot interactions and monitor task progress. Existing methods often rely solely on RGB videos, although additional modalities, such as audio, can improve accuracy under challenging conditions. However, most multimodal approaches assume that all modalities are available at inference time, leading to significant accuracy drops, or even failure, when inputs are missing. To address this limitation, we introduce KARMMA, a multimodal Knowledge distillation framework for egocentric Action Recognition robust to Missing ModAlities that does not require modality alignment across all samples during training or inference. KARMMA distills knowledge from a multimodal teacher into a multimodal student that leverages all available modalities while remaining robust to missing ones, enabling deployment across diverse sensor configurations without retraining. Our student uses approximately 50% fewer computational resources than the teacher, resulting in a lightweight and fast model that is well suited for on-robot deployment. Experiments on Epic-Kitchens and Something-Something demonstrate that our student achieves competitive accuracy while significantly reducing performance degradation under missing modality conditions.

cs.CV↗

arXiv2025

O-MaMa: Learning Object Mask Matching between Egocentric and Exocentric Views

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) an Ego$\leftrightarrow$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, obtaining relative gains of +22% and +76% in the Ego2Exo and Exo2Ego IoU against the official challenge baselines, and a +13% and +6% compared with the SOTA with 1% of the training parameters.

cs.CV↗

arXiv2024

Convolution kernel adaptation to calibrated fisheye

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.

cs.CV↗