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Cross-domain adaptive teacher for object detection
We address the task of domain adaptation in object detection, where there is a domain gap
between a domain with annotations (source) and a domain of interest without annotations …
between a domain with annotations (source) and a domain of interest without annotations …
Unsupervised domain adaptation of object detectors: A survey
Recent advances in deep learning have led to the development of accurate and efficient
models for various computer vision applications such as classification, segmentation, and …
models for various computer vision applications such as classification, segmentation, and …
Unbiased mean teacher for cross-domain object detection
Cross-domain object detection is challenging, because object detection model is often
vulnerable to data variance, especially to the considerable domain shift between two …
vulnerable to data variance, especially to the considerable domain shift between two …
3d semantic segmentation in the wild: Learning generalized models for adverse-condition point clouds
Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in
autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) …
autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) …
Cross-domain object detection for autonomous driving: A stepwise domain adaptative YOLO approach
Supervised object detection models based on deep learning technologies cannot perform
well in domain shift scenarios where annotated data for training is always insufficient. To this …
well in domain shift scenarios where annotated data for training is always insufficient. To this …
Multi-granularity alignment domain adaptation for object detection
Abstract Domain adaptive object detection is challenging due to distinctive data distribution
between source domain and target domain. In this paper, we propose a unified multi …
between source domain and target domain. In this paper, we propose a unified multi …
Cross domain object detection by target-perceived dual branch distillation
Cross domain object detection is a realistic and challenging task in the wild. It suffers from
performance degradation due to large shift of data distributions and lack of instance-level …
performance degradation due to large shift of data distributions and lack of instance-level …
Vector-decomposed disentanglement for domain-invariant object detection
To improve the generalization of detectors, for domain adaptive object detection (DAOD),
recent advances mainly explore aligning feature-level distributions between the source and …
recent advances mainly explore aligning feature-level distributions between the source and …
Instance relation graph guided source-free domain adaptive object detection
Abstract Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue
of domain shift. Specifically, UDA methods try to align the source and target representations …
of domain shift. Specifically, UDA methods try to align the source and target representations …
Confmix: Unsupervised domain adaptation for object detection via confidence-based mixing
Abstract Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model
trained on a source domain to detect instances from a new target domain for which …
trained on a source domain to detect instances from a new target domain for which …