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Found 8 papers, 3 papers with code
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.
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.
ProxyDet: Synthesizing Proxy Novel Classes via Classwise Mixup for Open-Vocabulary Object Detection
Open-vocabulary object detection (OVOD) aims to recognize novel objects whose categories are not included in the training set.
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.
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.
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.
Dynamic Collective Intelligence Learning: Finding Efficient Sparse Model via Refined Gradients for Pruned Weights
To alleviate this problem, dynamic pruning methods have emerged, which try to find diverse sparsity patterns during training by utilizing Straight-Through-Estimator (STE) to approximate gradients of pruned weights.
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation
Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the current frame.
