FSSAM reuses SAM 2's video memory matching for few-shot segmentation by matching query features against pseudo query memories instead of support features, and reports state-of-the-art mIoU on PASCAL-5i and COCO-20i.
Attention-based Joint Detection of Object and Semantic Part
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abstract
In this paper, we address the problem of joint detection of objects like dog and its semantic parts like face, leg, etc. Our model is created on top of two Faster-RCNN models that share their features to perform a novel Attention-based feature fusion of related Object and Part features to get enhanced representations of both. These representations are used for final classification and bounding box regression separately for both models. Our experiments on the PASCAL-Part 2010 dataset show that joint detection can simultaneously improve both object detection and part detection in terms of mean Average Precision (mAP) at IoU=0.5.
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Unlocking the Power of SAM 2 for Few-Shot Segmentation
FSSAM reuses SAM 2's video memory matching for few-shot segmentation by matching query features against pseudo query memories instead of support features, and reports state-of-the-art mIoU on PASCAL-5i and COCO-20i.