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Found 5 papers, 0 papers with code
CLIP-S$^4$: Language-Guided Self-Supervised Semantic Segmentation
To further improve the pixel embeddings and enable language-driven semantic segmentation, we design two types of consistency guided by vision-language models: 1) embedding consistency, aligning our pixel embeddings to the joint feature space of a pre-trained vision-language model, CLIP; and 2) semantic consistency, forcing our model to make the same predictions as CLIP over a set of carefully designed target classes with both known and unknown prototypes.
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation
To further improve the pixel embeddings and enable language-driven semantic segmentation, we design two types of consistency guided by vision-language models: 1) embedding consistency, aligning our pixel embeddings to the joint feature space of a pre-trained vision-language model, CLIP; and 2) semantic consistency, forcing our model to make the same predictions as CLIP over a set of carefully designed target classes with both known and unknown prototypes.
Geo-Context Aware Study of Vision-Based Autonomous Driving Models and Spatial Video Data
The visual study is seamlessly integrated with the geographical environment by combining DL model performance with geospatial visualization techniques.
Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images
Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images.
Visual Understanding of Multiple Attributes Learning Model of X-Ray Scattering Images
This extended abstract presents a visualization system, which is designed for domain scientists to visually understand their deep learning model of extracting multiple attributes in x-ray scattering images.
