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Found 7 papers, 1 papers with code
Coreset Selection for Object Detection
Coreset selection is a method for selecting a small, representative subset of an entire dataset.
HourglassNeRF: Casting an Hourglass as a Bundle of Rays for Few-shot Neural Rendering
Addressing this, we propose HourglassNeRF, an effective regularization-based approach with a novel hourglass casting strategy.
Tradeoff of generalization error in unsupervised learning
In this study, we propose that unsupervised learning generally exhibits a two-component tradeoff of the GE, namely the model error and the data error -- using a more complex model reduces the model error at the cost of the data error, with the data error playing a more significant role for a smaller training dataset.
End-to-End Multi-Object Detection with a Regularized Mixture Model
Recent end-to-end multi-object detectors simplify the inference pipeline by removing hand-crafted processes such as non-maximum suppression (NMS).
Few-Shot Object Detection by Attending to Per-Sample-Prototype
Second, each support sample is used as a class code to leverage the information by comparing similarities between each support feature and query features.
Ranked #12 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Training Multi-Object Detector by Estimating Bounding Box Distribution for Input Image
Instead of assigning each ground truth to specific locations of network's output, we train a network by estimating the probability density of bounding boxes in an input image using a mixture model.
Unpriortized Autoencoder For Image Generation
In this paper, we treat the image generation task using an autoencoder, a representative latent model.
