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Found 9 papers, 5 papers with code
Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling
To address these issues, we propose a novel federated learning framework called FedCMD, a model decoupling tailored to the Cloud-edge supported federated learning that separates deep neural networks into a body for capturing shared representations in Cloud and a personalized head for migrating data heterogeneity.
Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers
This paper addresses the problem of cross-modal object tracking from RGB videos and event data.
Ranked #1 on
Object Tracking
on COESOT
Networking of Internet of UAVs: Challenges and Intelligent Approaches
Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and transmission tasks quickly, robustly, and cost-efficiently via effective cooperation among UAVs.
Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy
In this paper, we propose a single-agent Monte Carlo based reinforced feature selection (MCRFS) method, as well as two efficiency improvement strategies, i. e., early stopping (ES) strategy and reward-level interactive (RI) strategy.
Online Binary Models are Promising for Distinguishing Temporally Consistent Computer Usage Profiles
This paper investigates whether computer usage profiles comprised of process-, network-, mouse-, and keystroke-related events are unique and consistent over time in a naturalistic setting, discussing challenges and opportunities of using such profiles in applications of continuous authentication.
V3H: View Variation and View Heredity for Incomplete Multi-view Clustering
Inspired by the variation and the heredity in genetics, V3H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively.
ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering
In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods.
Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat
However, different views often have distinct incompleteness, i. e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views).
Adaptive Adversarial Attack on Scene Text Recognition
We observe two critical obstacles in adversarial examples: (i) Strong adversarial attacks (e. g., C&W attack) require manually tuning hyper-parameters and take a long time to construct an adversarial example, making it impractical to attack real-time systems; (ii) Most of the studies focus on non-sequential tasks, such as image classification, yet only a few consider sequential tasks.
