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Found 3 papers, 0 papers with code
Exploiting Category Names for Few-Shot Classification with Vision-Language Models
Vision-language foundation models pretrained on large-scale data provide a powerful tool for many visual understanding tasks.
RC-DARTS: Resource Constrained Differentiable Architecture Search
In this paper, we propose the resource constrained differentiable architecture search (RC-DARTS) method to learn architectures that are significantly smaller and faster while achieving comparable accuracy.
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection
Comparing to standard Faster RCNN, it contains three highlights: an ensemble of two classification heads and a distillation head to avoid overfitting on noisy labels and improve the mining precision, masking the negative sample loss in box predictor to avoid the harm of false negative labels, and training box regression head only on seed annotations to eliminate the harm from inaccurate boundaries of mined bounding boxes.
