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Found 6 papers, 1 papers with code
Annotation Free Semantic Segmentation with Vision Foundation Models
In this work, we build a lightweight module on top of a self-supervised pretrained vision encoder to align patch features with a pre-trained text encoder.
OOD Aware Supervised Contrastive Learning
Our solution is simple and efficient and acts as a natural extension of the closed-set supervised contrastive representation learning.
Glimpse-Attend-and-Explore: Self-Attention for Active Visual Exploration
In this paper, we propose the Glimpse-Attend-and-Explore model which: (a) employs self-attention to guide the visual exploration instead of task-specific uncertainty maps; (b) can be used for both dense and sparse prediction tasks; and (c) uses a contrastive stream to further improve the representations learned.
Attend and Segment: Attention Guided Active Semantic Segmentation
The main idea is to refine an agent's understanding of the environment by attending the areas it is most uncertain about.
How to improve CNN-based 6-DoF camera pose estimation
Convolutional neural networks (CNNs) and transfer learning have recently been used for 6 degrees of freedom (6-DoF) camera pose estimation.
Where to Look Next: Unsupervised Active Visual Exploration on 360° Input
We address the problem of active visual exploration of large 360{\deg} inputs.
