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Found 7 papers, 2 papers with code
LiDAR-UDA: Self-ensembling Through Time for Unsupervised LiDAR Domain Adaptation
Existing self-training methods use a model trained on labeled source data to generate pseudo labels for target data and refine the predictions via fine-tuning the network on the pseudo labels.
Improving Evaluation of Debiasing in Image Classification
Based on such issues, this paper 1) proposes an evaluation metric `Align-Conflict (AC) score' for the tuning criterion, 2) includes experimental settings with low bias severity and shows that they are yet to be explored, and 3) unifies the standardized experimental settings to promote fair comparisons between debiasing methods.
CAFA: Class-Aware Feature Alignment for Test-Time Adaptation
That is, a model does not have a chance to learn test data in a class-discriminative manner, which was feasible in other adaptation tasks (\textit{e. g.,} unsupervised domain adaptation) via supervised losses on the source data.
3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations
While NeRF-based 3D-aware image generation methods enable viewpoint control, limitations still remain to be adopted to various 3D applications.
CG-NeRF: Conditional Generative Neural Radiance Fields
While recent NeRF-based generative models achieve the generation of diverse 3D-aware images, these approaches have limitations when generating images that contain user-specified characteristics.
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation
However, the distribution of max logits of each predicted class is significantly different from each other, which degrades the performance of identifying unexpected objects in urban-scene segmentation.
Ranked #4 on
Anomaly Detection
on Lost and Found
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening
Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving.
Ranked #5 on
Robust Object Detection
on DWD
