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Found 12 papers, 7 papers with code
Computer vision-based food calorie estimation: dataset, method, and experiment
In this paper, we present a novel food image data set with volume and mass records of foods, and a deep learning method for food detection, to make a complete calorie estimation.
Deep Learning-Based Food Calorie Estimation Method in Dietary Assessment
In order to increase accuracy of detection and reduce the error of volume estimation in food calorie estimation, we present our calorie estimation method in this paper.
Slip Detection with Combined Tactile and Visual Information
Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community.
Robotics
High-Performance Long-Term Tracking with Meta-Updater
Most top-ranked long-term trackers adopt the offline-trained Siamese architectures, thus, they cannot benefit from great progress of short-term trackers with online update.
Ranked #11 on
Visual Object Tracking
on LaSOT-ext
A Token Level Multi-target Stance Detection Dataset
It leads to lacking fine-grained analysis and generalization ability of the stance detection algorithm.
Leveraging AI and Intelligent Reflecting Surface for Energy-Efficient Communication in 6G IoT
The ever-increasing data traffic, various delay-sensitive services, and the massive deployment of energy-limited Internet of Things (IoT) devices have brought huge challenges to the current communication networks, motivating academia and industry to move to the sixth-generation (6G) network.
Video Annotation for Visual Tracking via Selection and Refinement
Deep learning based visual trackers entail offline pre-training on large volumes of video datasets with accurate bounding box annotations that are labor-expensive to achieve.
Privacy-preserving Few-shot Traffic Detection against Advanced Persistent Threats via Federated Meta Learning
Extensive experiments based on multiple benchmark datasets like CICIDS2017 and DAPT 2020 prove the superiority of proposed PFTD.
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Graph-based semi-supervised learning (GSSL) has long been a hot research topic.
Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning
Researchers have struggled to design fair FL systems that ensure fairness of results.
Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices
Federated neuromorphic learning (FedNL) leverages event-driven spiking neural networks and federated learning frameworks to effectively execute intelligent analysis tasks over amounts of distributed low-power devices but also perform vulnerability to poisoning attacks.
Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks
In this paper, we focus on the edge optimization of AIGC task execution and propose GMEL, a generative model-driven industrial AIGC collaborative edge learning framework.
