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Evaluating Text-to-Image Synthesis: Survey and Taxonomy of Image Quality Metrics
Recent advances in text-to-image synthesis enabled through a combination of language and vision foundation models have led to a proliferation of the tools available and an increased attention to the field.
TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models
Given access to paired image-pointcloud (2D-3D) data, we first optimize a 3D segmentation backbone for the main task of semantic segmentation using the pointclouds and the task of 2D $\to$ 3D KD by using an off-the-shelf 2D pre-trained foundation model.
Attention-Guided Masked Autoencoders For Learning Image Representations
Masked autoencoders (MAEs) have established themselves as a powerful method for unsupervised pre-training for computer vision tasks.
Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling
We achieve this by (1) learning depth-feature correlation by spatially correlate the feature maps with the depth maps to induce knowledge about the structure of the scene and (2) implementing farthest-point sampling to more effectively select relevant features by utilizing 3D sampling techniques on depth information of the scene.
Leveraging Self-Supervised Vision Transformers for Neural Transfer Function Design
In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity.
