Search Results for author:
Found 11 papers, 7 papers with code
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 #3 on
One-Shot 3D Action Recognition
on NTU RGB+D 120
Distributed Multi-Target Tracking in Camera Networks
On the other hand, data association and information fusion are more challenging than in a centralized setup, mostly due to the lack of global and complete information.
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 #5 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.
Performance of object recognition in wearable videos
This work studies the problem of object detection and localization on videos captured by this type of camera.
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
EV-SegNet: Semantic Segmentation for Event-based Cameras
Since there is no existing labeled dataset for this task, we propose how to automatically generate approximated semantic segmentation labels for some sequences of the DDD17 dataset, which we publish together with the model, and demonstrate they are valid to train a model for DVS data only.
