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Found 17 papers, 5 papers with code
EvSign: Sign Language Recognition and Translation with Streaming Events
Sign language is one of the most effective communication tools for people with hearing difficulties.
Deciphering public attention to geoengineering and climate issues using machine learning and dynamic analysis
As the conversation around using geoengineering to combat climate change intensifies, it is imperative to engage the public and deeply understand their perspectives on geoengineering research, development, and potential deployment.
When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices
Comprehensive experimental results obtained from simulation- and real test-bed-based platforms show that our federated foresight-pruning method not only preserves the ability of the dense model with a memory reduction up to 9x but also boosts the performance of the vanilla BP-Free method with dramatically fewer FLOPs.
Personalized Federated Instruction Tuning via Neural Architecture Search
Federated Instruction Tuning (FIT) has shown the ability to achieve collaborative model instruction tuning among massive data owners without sharing private data.
QACP: An Annotated Question Answering Dataset for Assisting Chinese Python Programming Learners
In online learning platforms, particularly in rapidly growing computer programming courses, addressing the thousands of students' learning queries requires considerable human cost.
Exact Fusion via Feature Distribution Matching for Few-shot Image Generation
In this paper we propose an exact Fusion via Feature Distribution matching Generative Adversarial Network (F2DGAN) for few-shot image generation.
Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning
To this end, in this paper, we propose a novel MARL algorithm named Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning (SCIC), which incorporates a novel Intrinsic reward mechanism based on a new cooperation criterion measured by situation-dependent causal influence among agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems
On the one hand, to achieve model training in all the diverse clients, mobile computing systems can only use small low-performance models for collaborative learning.
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning
We formally analyze the significance of data consistency between the pre-training and training stages of CyclicFL, showing the limited Lipschitzness of loss for the pre-trained models by CyclicFL.
RF-CHORD: Towards Deployable RFID Localization System for Logistics Network
RFID localization is considered the key enabler of automating the process of inventory tracking and management for high-performance logistic network.
SRRT: Exploring Search Region Regulation for Visual Object Tracking
The dominant trackers generate a fixed-size rectangular region based on the previous prediction or initial bounding box as the model input, i. e., search region.
Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline
With the popularity of multi-modal sensors, visible-thermal (RGB-T) object tracking is to achieve robust performance and wider application scenarios with the guidance of objects' temperature information.
Ranked #8 on
Rgb-T Tracking
on GTOT
Multi-modal Visual Tracking: Review and Experimental Comparison
Visual object tracking, as a fundamental task in computer vision, has drawn much attention in recent years.
Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking
In this study, we propose a novel RGB-T tracking framework by jointly modeling both appearance and motion cues.
Ranked #10 on
Rgb-T Tracking
on GTOT
Smartphone App Usage Prediction Using Points of Interest
In this paper we present the first population-level, city-scale analysis of application usage on smartphones.
Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data
By conducting experiments on two real-world datasets collected from both mobile application and cellular network, we reveal that the attack system is able to recover users' trajectories with accuracy about 73%~91% at the scale of tens of thousands to hundreds of thousands users, which indicates severe privacy leakage in such datasets.
Computers and Society Cryptography and Security
