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Found 11 papers, 7 papers with code
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning
We then propose an effective and realistic attack method that better produces poisoned samples without access to benign samples, and derive an effective in-distribution attack objective.
PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images
In brief, we use the prompts of overlapping masks as corresponding negative signals, resulting in refined masks.
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model Selection
In this work, we are motivated by a pitfall of TTA, i. e. sensitivity to hyper-parameters, and propose to approach HILTTA by synergizing active learning and model selection.
CLIP-Guided Source-Free Object Detection in Aerial Images
Domain adaptation is crucial in aerial imagery, as the visual representation of these images can significantly vary based on factors such as geographic location, time, and weather conditions.
Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation
The success of large language models has inspired the computer vision community to explore image segmentation foundation model that is able to zero/few-shot generalize through prompt engineering.
Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization
Test-Time Adaptation aims to adapt source domain model to testing data at inference stage with success demonstrated in adapting to unseen corruptions.
On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion
Existing approaches often focus on improving test-time training performance under well-curated target domain data.
STFAR: Improving Object Detection Robustness at Test-Time by Self-Training with Feature Alignment Regularization
This motivates us to explore adapting an object detection model at test-time, a. k. a.
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training
Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required.
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering
Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available and instant inference on target domain is required.
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning
Addressing the annotation challenge in 3D Point Cloud segmentation has inspired research into weakly supervised learning.
