For efficient and safe autonomous driving, it is essential that autonomous vehicles can predict the motion of other traffic agents. While highly accurate, current motion prediction models often impose significant chal...
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Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computervision, with applications in areas such as augmented reality (AR) and medical imaging. This work explo...
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Stereo estimation has made many advancements in recent years with the introduction of deep-learning. However the traditional supervised approach to deep-learning requires the creation of accurate and plentiful ground-...
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State-of-the-art Multiple Object Tracking (MOT) approaches have shown remarkable performance when trained and evaluated on current benchmarks. However, these benchmarks primarily consist of clear weather scenarios, ov...
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We present a novel inverse rendering-based framework to estimate the 3D shape (per-pixel surface normals and depth) of objects and scenes from single-view polarization images, the problem popularly known as Shape from...
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The total generalized variation extends the total variation by incorporating higher-order smoothness. Thus, it can also suffer from similar discretization issues related to isotropy. Inspired by the success of novel d...
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Estimation of importance for considered features is an important issue for any knowledge exploration process and it can be executed by a variety of approaches. In the research reported in this study, the primary aim w...
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Out-of-distribution (OOD) detection is essential to ensure the reliability and robustness of machine learning models in real-world applications. Post-hoc OOD detection methods have gained significant attention due to ...
Out-of-distribution (OOD) detection is essential to ensure the reliability and robustness of machine learning models in real-world applications. Post-hoc OOD detection methods have gained significant attention due to the fact that they offer the advantage of not requiring additional re-training, which could degrade model performance and increase training time. However, most existing post-hoc methods rely only on the encoder output (features), logits, or the softmax probability, meaning they have no access to information that might be lost in the feature extraction process. In this work, we address this limitation by introducing Adaptive Temperature Scaling (ATS), a novel approach that dynamically calculates a temperature value based on activations of the intermediate layers. Fusing this sample-specific adjustment with class-dependent logits, our ATS captures additional statistical information before they are lost in the feature extraction process, leading to a more robust and powerful OOD detection method. We conduct extensive experiments to demonstrate the efficacy of our approach. Notably, our method can be seamlessly combined with SOTA post-hoc OOD detection methods that rely on the logits, thereby enhancing their performance and improving their robustness.
Keyless entry systems in cars are adopting neural networks for localizing its operators. Using test-time adversarial defences equip such systems with the ability to defend against adversarial attacks without prior tra...
Keyless entry systems in cars are adopting neural networks for localizing its operators. Using test-time adversarial defences equip such systems with the ability to defend against adversarial attacks without prior training on adversarial samples. We propose a test-time adversarial example detector which detects the input adversarial example through quantifying the localized intermediate responses of a pretrained neural network and confidence scores of an auxiliary softmax layer. Furthermore, in order to make the network robust, we extenuate the non-relevant features by non-iterative input sample clipping. Using our approach, mean performance over 15 levels of adversarial perturbations is increased by 53.3% for the fast gradient sign method and 60.9% for both the basic iterative method and the projected gradient method when compared to adversarial training.
Diffusion models have been successfully applied to many inverse problems, including MRI and CT reconstruction. Researchers typically re-purpose models originally designed for unconditional sampling without modificatio...
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