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Found 14 papers, 4 papers with code
A Survey on Visual Mamba
This encompasses general visual tasks, Medical visual tasks (e. g., 2D / 3D segmentation, classification, and image registration, etc.
CA-Stream: Attention-based pooling for interpretable image recognition
Explanations obtained from transformer-based architectures in the form of raw attention, can be seen as a class-agnostic saliency map.
DP-Net: Learning Discriminative Parts for image recognition
This paper presents Discriminative Part Network (DP-Net), a deep architecture with strong interpretation capabilities, which exploits a pretrained Convolutional Neural Network (CNN) combined with a part-based recognition module.
A Learning Paradigm for Interpretable Gradients
This paper studies interpretability of convolutional networks by means of saliency maps.
P-Mamba: Marrying Perona Malik Diffusion with Mamba for Efficient Pediatric Echocardiographic Left Ventricular Segmentation
In pediatric cardiology, the accurate and immediate assessment of cardiac function through echocardiography is important since it can determine whether urgent intervention is required in many emergencies.
Revisiting Transferable Adversarial Image Examples: Attack Categorization, Evaluation Guidelines, and New Insights
Transferable adversarial examples raise critical security concerns in real-world, black-box attack scenarios.
A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty
Aggregating distributed energy resources in power systems significantly increases uncertainties, in particular caused by the fluctuation of renewable energy generation.
Opti-CAM: Optimizing saliency maps for interpretability
Methods based on class activation maps (CAM) provide a simple mechanism to interpret predictions of convolutional neural networks by using linear combinations of feature maps as saliency maps.
Towards Good Practices in Evaluating Transfer Adversarial Attacks
In this work, we design good practices to address these limitations, and we present the first comprehensive evaluation of transfer attacks, covering 23 representative attacks against 9 defenses on ImageNet.
MOTSLAM: MOT-assisted monocular dynamic SLAM using single-view depth estimation
In this paper, we present MOTSLAM, a dynamic visual SLAM system with the monocular configuration that tracks both poses and bounding boxes of dynamic objects.
KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution
KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind power dataset, in which the participants are required to predict the future generation given the historical context factors.
Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness
In this paper, we propose a framework of filter-based ensemble of deep neuralnetworks (DNNs) to defend against adversarial attacks.
Walking on the Edge: Fast, Low-Distortion Adversarial Examples
Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input.
