pascal-part-116
OV-PARTS: Towards Open-Vocabulary Part Segmentation (Supplementary Material)
The number of part queries is set to 50. SGD optimizer with the initial learning rate of 2e-2 and weight decay of 5e-4 is used. We sample 128 training samples for each object part class. The initial value of the learnable fusion weight is 0.5 . The total batch size is 8, and the training iterations amount to 40k.
OV-PARTS: Towards Open-Vocabulary Part Segmentation
OV -P ARTS includes refined versions of two publicly available datasets: Pascal-Part-116 and ADE20K-Part-234.
- Asia > China > Shanghai > Shanghai (0.04)
- North America > United States > Texas (0.04)
- Asia > Middle East > Israel (0.04)
- (2 more...)
Understanding Multi-Granularity for Open-Vocabulary Part Segmentation
Through extensive experiments, our model demonstrated a significant improvement over the state-of-the-art models on the Pascal-Part-116, ADE20K-Part-234, and PartImageNet datasets.
- Information Technology > Artificial Intelligence > Vision (1.00)
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.94)
Understanding Multi-Granularity for Open-Vocabulary Part Segmentation
Through extensive experiments, our model demonstrated a significant improvement over the state-of-the-art models on the Pascal-Part-116, ADE20K-Part-234, and PartImageNet datasets.
- Information Technology > Artificial Intelligence > Vision (1.00)
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.94)
OV-PARTS: Towards Open-V ocabulary Part Segmentation (Supplementary Material)
The number of part queries is set to 50. SGD optimizer with the initial learning rate of 2e-2 and weight decay of 5e-4 is used. We sample 128 training samples for each object part class. The initial value of the learnable fusion weight is 0.5 . The total batch size is 8, and the training iterations amount to 40k.
- Asia > China > Shanghai > Shanghai (0.04)
- North America > United States > Texas (0.04)
- Asia > Middle East > Israel (0.04)
- (2 more...)