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Found 9 papers, 2 papers with code
Coreset Selection for Object Detection
Coreset selection is a method for selecting a small, representative subset of an entire dataset.
MDPose: Real-Time Multi-Person Pose Estimation via Mixture Density Model
In this work, we propose a novel framework of single-stage instance-aware pose estimation by modeling the joint distribution of human keypoints with a mixture density model, termed as MDPose.
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).
MatteFormer: Transformer-Based Image Matting via Prior-Tokens
In this paper, we propose a transformer-based image matting model called MatteFormer, which takes full advantage of trimap information in the transformer block.
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Image Matting
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KL-Divergence-Based Region Proposal Network for Object Detection
The learning of the region proposal in object detection using the deep neural networks (DNN) is divided into two tasks: binary classification and bounding box regression task.
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.
Vehicle Image Generation Going Well with The Surroundings
Using a subnetwork based on a precedent work of image completion, our model makes the shape of an object.
Image Restoration by Estimating Frequency Distribution of Local Patches
In this paper, we propose a method to solve the image restoration problem, which tries to restore the details of a corrupted image, especially due to the loss caused by JPEG compression.
