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Found 6 papers, 3 papers with code
TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models
Given access to paired image-pointcloud (2D-3D) data, we first optimize a 3D segmentation backbone for the main task of semantic segmentation using the pointclouds and the task of 2D $\to$ 3D KD by using an off-the-shelf 2D pre-trained foundation model.
Are Vision Language Models Texture or Shape Biased and Can We Steer Them?
If text does indeed influence visual biases, this suggests that we may be able to steer visual biases not just through visual input but also through language: a hypothesis that we confirm through extensive experiments.
Video Test-Time Adaptation for Action Recognition
Our proposed method demonstrates a substantial performance gain over existing test-time adaptation approaches in both evaluations of a single distribution shift and the challenging case of random distribution shifts.
ActMAD: Activation Matching to Align Distributions for Test-Time-Training
Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time.
Test-time adversarial detection and robustness for localizing humans using ultra wide band channel impulse responses
Keyless entry systems in cars are adopting neural networks for localizing its operators.
Robustness of Object Detectors in Degrading Weather Conditions
State-of-the-art object detection systems for autonomous driving achieve promising results in clear weather conditions.
