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Found 4 papers, 1 papers with code
A Simple-but-effective Baseline for Training-free Class-Agnostic Counting
Class-Agnostic Counting (CAC) seeks to accurately count objects in a given image with only a few reference examples.
Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation
In this paper, we revisit the idea of using image reconstruction as the auxiliary task and incorporate it with a modern semi-supervised semantic segmentation framework.
DETRDistill: A Universal Knowledge Distillation Framework for DETR-families
Next, we propose a target-aware feature distillation to help the student model learn from the object-centric features of the teacher model.
Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism
In low data regimes, a network often struggles to choose the correct regions for recognition and tends to overfit spurious correlated patterns from the training data.
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