Search Results for author:
Found 16 papers, 9 papers with code
EventSleep: Sleep Activity Recognition with Event Cameras
Our novel pipeline is able to achieve high accuracy under these challenging conditions and incorporates a Bayesian approach (Laplace ensembles) to increase the robustness in the predictions, which is fundamental for medical applications.
Event Transformer+. A multi-purpose solution for efficient event data processing
Event cameras record sparse illumination changes with high temporal resolution and high dynamic range.
Event Transformer. A sparse-aware solution for efficient event data processing
Event cameras are sensors of great interest for many applications that run in low-resource and challenging environments.
Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank
In an end-to-end training, the features from both labeled and unlabeled data are optimized to be similar to same-class samples from the memory bank.
Domain and View-point Agnostic Hand Action Recognition
And, more importantly, when performing hand action recognition for action domains and camera perspectives which our approach has not been trained for (cross-domain action classification), our proposed framework achieves comparable performance to intra-domain state-of-the-art methods.
One-shot action recognition in challenging therapy scenarios
We also develop a set of complementary steps that boost the action recognition performance in the most challenging scenarios.
Ranked #4 on
One-Shot 3D Action Recognition
on NTU RGB+D 120
Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions
Besides, we propose a learning-based approach that aligns the distribution of the semantic classes of the target domain to the source domain.
Robust and Efficient Post-Processing for Video Object Detection (REPP)
Object recognition in video is an important task for plenty of applications, including autonomous driving perception, surveillance tasks, wearable devices or IoT networks.
Ranked #17 on
Video Object Detection
on ImageNet VID
Robust and efficient post-processing for video object detection
Object recognition in video is an important task for plenty of applications, including autonomous driving perception, surveillance tasks, wearable devices or IoT networks.
3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation
Fast and efficient semantic segmentation methods are needed to match the strong computational and temporal restrictions of many of these real-world applications.
Ranked #1 on
Real-Time 3D Semantic Segmentation
on SemanticKITTI
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth
Single-view depth estimation suffers from the problem that a network trained on images from one camera does not generalize to images taken with a different camera model.
Condition-Invariant Multi-View Place Recognition
Visual place recognition is particularly challenging when places suffer changes in its appearance.
Language Bootstrapping: Learning Word Meanings From Perception-Action Association
The model is based on an affordance network, i. e., a mapping between robot actions, robot perceptions, and the perceived effects of these actions upon objects.
Single-View and Multi-View Depth Fusion
The single and multi-view fusion we propose is challenging in several aspects.
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction
In this survey we present different approaches that allow an intelligent agent to explore autonomous its environment to gather information and learn multiple tasks.
