Search Results for author:
Found 5 papers, 3 papers with code
Non-Visible Light Data Synthesis and Application: A Case Study for Synthetic Aperture Radar Imagery
If large-scale models pre-trained with regular images can be adapted to generating novel SAR images, the problem is solved.
Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Models
In this paper, we focus on the WSSS with image-level labels, which is the most challenging form of WSSS.
Extracting Class Activation Maps from Non-Discriminative Features as well
Extracting class activation maps (CAM) from a classification model often results in poor coverage on foreground objects, i. e., only the discriminative region (e. g., the "head" of "sheep") is recognized and the rest (e. g., the "leg" of "sheep") mistakenly as background.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation
Specifically, due to the sum-over-class pooling nature of BCE, each pixel in CAM may be responsive to multiple classes co-occurring in the same receptive field.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Meta-Transfer Learning through Hard Tasks
In this paper, we propose a novel approach called meta-transfer learning (MTL) which learns to transfer the weights of a deep NN for few-shot learning tasks.
