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Found 16 papers, 6 papers with code
A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability
In this setting, considering that self-supervised model could be trained by completely different self-supervised paradigms, e. g., masked image modeling and contrastive learning, with complex training details, we propose a unified membership inference method called PartCrop.
GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving
With the representation of the ISG, the driving agents aggregate essential information from the most influential elements, including the road agents with potential collisions and the map elements to follow.
No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML
These solutions, referred to as TEE-Shielded DNN Partition (TSDP), partition a DNN model into two parts, offloading the privacy-insensitive part to the GPU while shielding the privacy-sensitive part within the TEE.
Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition
Besides, to further exploit the potential of positive pairs and increase the robustness of self-supervised representation learning, we propose a Positive Feature Transformation (PFT) strategy which adopts feature-level manipulation to increase the variance of positive pairs.
Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation
Our CGTP framework is an end to end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction.
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization
And the other introduces a new metric based on mutual information between adjacent action proposals and evaluates the informativeness of video samples, named Temporal Context Inconsistency (TCI).
Attentive pooling for Group Activity Recognition
However, the existing methods simply employed max/average pooling in this framework, which ignored the distinct contributions of different individuals to the group activity recognition.
Diagnostic Communication and Visual System based on Vehicle UDS Protocol
Unified Diagnostic Services (UDS) is a diagnostic communication protocol used in electronic control units (ECUs) within automotive electronics, which is specified in the ISO 14229-1.
Multi-scale 2D Representation Learning for weakly-supervised moment retrieval
Specifically, we first construct a two-dimensional map for each temporal scale to capture the temporal dependencies between candidates.
DistFL: Distribution-aware Federated Learning for Mobile Scenarios
Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models.
Learning Heatmap-Style Jigsaw Puzzles Provides Good Pretraining for 2D Human Pose Estimation
We note that 2D pose estimation task is highly dependent on the contextual relationship between image patches, thus we introduce a self-supervised method for pretraining 2D pose estimation networks.
SIGL: Securing Software Installations Through Deep Graph Learning
We introduce SIGL, a new tool for detecting malicious behavior during software installation.
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media.
Heterogeneous Graph Matching Networks
Information systems have widely been the target of malware attacks.
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification
The key idea is to leverage the representation learning of the heterogeneous program behavior graph to guide the reidentification process.
SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection
To facilitate the task of expressing anomalies based on expert knowledge, our system provides a domain-specific query language, SAQL, which allows analysts to express models for (1) rule-based anomalies, (2) time-series anomalies, (3) invariant-based anomalies, and (4) outlier-based anomalies.
Cryptography and Security Databases
