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Found 12 papers, 2 papers with code
Feature Fusion for Online Mutual Knowledge Distillation
We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated from parallel neural networks.
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.
Feature-map-level Online Adversarial Knowledge Distillation
By training a network to fool the corresponding discriminator, it can learn the other network's feature map distribution.
Maximizing Cosine Similarity Between Spatial Features for Unsupervised Domain Adaptation in Semantic Segmentation
We propose a novel method that tackles the problem of unsupervised domain adaptation for semantic segmentation by maximizing the cosine similarity between the source and the target domain at the feature level.
Exploiting Inter-pixel Correlations in Unsupervised Domain Adaptation for Semantic Segmentation
It creates a set of pseudo labels for the target domain to give explicit supervision.
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).
Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting
We also verify our method on a standard benchmark, miniImageNet, and D-ProtoNets shows the state-of-the-art open-set detection rate in FSOSR.
Personalized Keyword Spotting through Multi-task Learning
We design two personalized KWS tasks; (1) Target user Biased KWS (TB-KWS) and (2) Target user Only KWS (TO-KWS).
Unsupervised Domain Adaptation for One-stage Object Detector using Offsets to Bounding Box
Most existing domain adaptive object detection methods exploit adversarial feature alignment to adapt the model to a new domain.
Open Domain Generalization with a Single Network by Regularization Exploiting Pre-trained Features
To handle this task, the model has to learn a generalizable representation that can be applied to unseen domains while also identify unknown classes that were not present during training.
What, How, and When Should Object Detectors Update in Continually Changing Test Domains?
It is a well-known fact that the performance of deep learning models deteriorates when they encounter a distribution shift at test time.
Mitigating the Bias in the Model for Continual Test-Time Adaptation
Continual Test-Time Adaptation (CTA) is a challenging task that aims to adapt a source pre-trained model to continually changing target domains.
