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Found 11 papers, 2 papers with code
Appearance Debiased Gaze Estimation via Stochastic Subject-Wise Adversarial Learning
In this paper, we address these challenges and propose a novel framework: Stochastic subject-wise Adversarial gaZE learning (SAZE), which trains a network to generalize the appearance of subjects.
Towards Better Visualizing the Decision Basis of Networks via Unfold and Conquer Attribution Guidance
Revealing the transparency of Deep Neural Networks (DNNs) has been widely studied to describe the decision mechanisms of network inner structures.
Multi-Contextual Predictions with Vision Transformer for Video Anomaly Detection
Video Anomaly Detection(VAD) has been traditionally tackled in two main methodologies: the reconstruction-based approach and the prediction-based one.
Gradient Hedging for Intensively Exploring Salient Interpretation beyond Neuron Activation
Hedging is a strategy for reducing the potential risks in various types of investments by adopting an opposite position in a related asset.
Few-Shot Object Detection with Proposal Balance Refinement
Few-shot object detection has gained significant attention in recent years as it has the potential to greatly reduce the reliance on large amounts of manually annotated bounding boxes.
Style-Guided Domain Adaptation for Face Presentation Attack Detection
Domain adaptation (DA) or domain generalization (DG) for face presentation attack detection (PAD) has attracted attention recently with its robustness against unseen attack scenarios.
Improving Interpretability of Deep Neural Networks in Medical Diagnosis by Investigating the Individual Units
As an intuitive assessment metric for explanations, we report the performance of intersection of Union between visual explanation and bounding box of lesions.
Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations
As a result, it is possible to assign the bi-polar relevance scores of the target (positive) and hostile (negative) attributions while maintaining each attribution aligned with the importance.
Rotation Invariant Aerial Image Retrieval with Group Convolutional Metric Learning
In this work, we introduce a novel method for retrieving aerial images by merging group convolution with attention mechanism and metric learning, resulting in robustness to rotational variations.
A Two-Stream Symmetric Network with Bidirectional Ensemble for Aerial Image Matching
As a result, the training process of the deep network is regularized and the network becomes robust for the variance of aerial images.
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks
As Deep Neural Networks (DNNs) have demonstrated superhuman performance in a variety of fields, there is an increasing interest in understanding the complex internal mechanisms of DNNs.
