Statements (120)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Model
|
| gptkbp:bfsLayer |
4
|
| gptkbp:bfsParent |
gptkb:Deep_Lab
|
| gptkbp:adapted_into |
video segmentation
|
| gptkbp:applies_to |
gptkb:Photographer
image processing augmented reality computer vision smart city applications object detection tasks medical image analysis |
| gptkbp:architectural_style |
gptkb:television_channel
|
| gptkbp:based_on |
gptkb:Res_Net
Convolutional Neural Networks (CN Ns) |
| gptkbp:can_be |
objects in images
|
| gptkbp:competes_with |
gptkb:FCN_(Fully_Convolutional_Networks)
|
| gptkbp:developed_by |
gptkb:Google_Research
|
| gptkbp:enhances |
feature extraction
|
| gptkbp:game_components |
visual recognition systems
|
| gptkbp:has_achievements |
state-of-the-art results
state-of-the-art performance |
| https://www.w3.org/2000/01/rdf-schema#label |
Deep Lab v3
|
| gptkbp:improves |
gptkb:Deep_Lab_v2
semantic segmentation semantic segmentation accuracy |
| gptkbp:includes |
Deep Lab v3+ model
|
| gptkbp:introduced |
gptkb:2017
|
| gptkbp:is_a_framework_for |
developing AI solutions
|
| gptkbp:is_a_solution_for |
image processing challenges
|
| gptkbp:is_a_tool_for |
data analysis
image understanding |
| gptkbp:is_associated_with |
semantic segmentation benchmarks
|
| gptkbp:is_available_in |
open-source format
|
| gptkbp:is_capable_of |
fine-grained segmentation
|
| gptkbp:is_compared_to |
other segmentation models
Mask R-CNN model Seg Net model U-Net model |
| gptkbp:is_compatible_with |
GPU acceleration
transfer learning |
| gptkbp:is_designed_for |
pixel-wise classification
|
| gptkbp:is_designed_to |
handle occlusions
|
| gptkbp:is_effective_against |
urban scene segmentation
|
| gptkbp:is_enhanced_by |
contextual information
post-processing techniques |
| gptkbp:is_evaluated_by |
gptkb:COCO_dataset
gptkb:Cityscapes_dataset AD E20 K dataset MSCOCO dataset PASCALVOC dataset |
| gptkbp:is_featured_in |
gptkb:publishing_company
|
| gptkbp:is_implemented_in |
gptkb:Graphics_Processing_Unit
deep learning frameworks Py Torch framework |
| gptkbp:is_influenced_by |
gptkb:Res_Net_architecture
Fully Convolutional Networks |
| gptkbp:is_integrated_with |
other AI technologies
|
| gptkbp:is_known_for |
high accuracy
flexibility in applications reduce artifacts effective feature aggregation effective handling of object boundaries high performance on benchmark datasets high-resolution image processing real-time inference capabilities scalability in model training |
| gptkbp:is_part_of |
gptkb:Deep_Lab_family
AI research initiatives AI research community deep learning applications computer vision research AI-driven applications AI toolkits |
| gptkbp:is_recognized_by |
industry leaders
a leading model |
| gptkbp:is_recognized_for |
robustness
flexibility in architecture design high accuracy in segmentation tasks high performance in segmentation tasks improved boundary delineation robust performance across domains robustness to occlusions |
| gptkbp:is_referenced_in |
new segmentation algorithms
|
| gptkbp:is_supported_by |
community contributions
|
| gptkbp:is_used_for |
image analysis
image segmentation in robotics |
| gptkbp:is_used_in |
autonomous driving
environmental monitoring agriculture monitoring image recognition systems image recognition tasks urban scene segmentation |
| gptkbp:is_used_to |
improve image quality
|
| gptkbp:is_utilized_in |
scene understanding
image classification tasks facial recognition systems |
| gptkbp:key_people |
AI advancements
|
| gptkbp:performance |
semantic segmentation tasks
|
| gptkbp:provides |
pixel-wise classification
|
| gptkbp:related_model |
deep learning frameworks
learns from data transforms image data |
| gptkbp:release_year |
gptkb:2017
|
| gptkbp:subject |
research studies
|
| gptkbp:successor |
gptkb:Deep_Lab_v3+
|
| gptkbp:suitable_for |
real-time applications
|
| gptkbp:supports |
real-time applications
multi-scale context |
| gptkbp:technique |
dilated convolutions
|
| gptkbp:training |
deep learning techniques
large-scale datasets multi-GPU setups data augmentation techniques backpropagation algorithm PASCALVOC dataset GPU clusters |
| gptkbp:uses |
atrous convolution
Atrous convolution |
| gptkbp:utilizes |
gptkb:Deep_Lab_architecture
multi-scale context |