Search Results for author:
Found 10 papers, 6 papers with code
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.
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.
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.
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 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.
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.
SpectralWaste Dataset: Multimodal Data for Waste Sorting Automation
The increase in non-biodegradable waste is a worldwide concern.
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.
