Search Results for author:
Found 9 papers, 4 papers with code
From Open-Vocabulary to Vocabulary-Free Semantic Segmentation
Open-vocabulary semantic segmentation enables models to identify novel object categories beyond their training data.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
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Learning from the Web: Language Drives Weakly-Supervised Incremental Learning for Semantic Segmentation
Current weakly-supervised incremental learning for semantic segmentation (WILSS) approaches only consider replacing pixel-level annotations with image-level labels, while the training images are still from well-designed datasets.
When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather
In Federated Learning (FL), multiple clients collaboratively train a global model without sharing private data.
HouseCat6D - A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
Estimating 6D object poses is a major challenge in 3D computer vision.
RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation
Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies.
SynDrone -- Multi-modal UAV Dataset for Urban Scenarios
The development of computer vision algorithms for Unmanned Aerial Vehicles (UAVs) imagery heavily relies on the availability of annotated high-resolution aerial data.
Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers
With the increasing availability of depth sensors, multimodal frameworks that combine color information with depth data are gaining interest.
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
Estimating 6D object poses is a major challenge in 3D computer vision.
DepthFormer: Multimodal Positional Encodings and Cross-Input Attention for Transformer-Based Segmentation Networks
Most approaches for semantic segmentation use only information from color cameras to parse the scenes, yet recent advancements show that using depth data allows to further improve performances.
