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Claim this profileWorks277 from public data
- Compressive data gathering for large-scale wireless sensor networks628
This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for large-scale wireless sensor networks and shows the efficiency and robustness of the proposed scheme.
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- An Iterative BP-CNN Architecture for Channel Decoding294
A novel iterative belief propagation – convolutional neural network (BP-CNN) architecture for channel decoding under correlated noise that concatenates a trained CNN with a standard BP decoder and the introduction of the normality test to the CNN training shapes the residual noise distribution.
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- Towards Optimal Power Control via Ensembling Deep Neural Networks275
Simulation results show that for the standard symmetric $K$ -user Gaussian interference channel, the proposed methods can outperform state-of-the-art power control solutions under a variety of system configurations.
- VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows271
This work proposes a large-scale Visible-Event benchmark (termed VisEvent) and builds a simple but effective tracking algorithm by proposing a cross-modality transformer, to achieve more effective feature fusion between visible and event data.
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- Disentangle Your Dense Object Detector234
This paper investigates three important conjunctions of dense detectors and proposes Disentangled Dense Object Detector (DDOD), in which simple and effective disentanglement mechanisms are designed and integrated into the current state-of-the-art dense object detectors.
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- Dual-camera design for coded aperture snapshot spectral imaging214
A beam splitter is placed in front of the objective lens of CASSI, which allows the same scene to be simultaneously captured by a grayscale camera, which greatly eases the reconstruction problem and yields high-quality 3D spectral data.
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- Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs166
TACT is inspired by the observation that the semantic correlation between two relations is highly correlated to their topological structure in knowledge graphs and categorizes all relation pairs into several topological patterns, and proposes a Relational Correlation Network (RCN) to learn the importance of the different patterns for inductive link prediction.
- Online planning for multi-agent systems with bounded communication135
An online algorithm for planning under uncertainty in multi-agent settings modeled as DEC-POMDPs that can solve problems that are too large for the best existing offline planning algorithms and it outperforms the best online method, producing much higher value with much less communication in most cases.
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- A Disaster Response System based on Human-Agent Collectives111
HAC-ER demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response and utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations.
- Decoding and mapping task states of the human brain via deep learning100
Without incurring the burden of handcrafting the features, the proposed deep decoding method can classify brain task states highly accurately, and is a powerful tool for fMRI researchers.
- Action Unit Memory Network for Weakly Supervised Temporal Action Localization99
This paper presents an Action Unit Memory Network (AUMN) for weakly supervised temporal action localization, which can mitigate the above two challenges by learning an action unit memory bank.
- Human–agent collaboration for disaster response99
This paper develops an algorithm, based on a multi-agent Markov decision process representation of the task allocation problem and shows that it outperforms standard baseline solutions and integrates it into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game that simulates disaster response settings in the real-world.
- Rethinking Graph Convolutional Networks in Knowledge Graph Completion96
It is suggested that existing GCNs are unnecessary for KGC, and novel GCN-based KGC models should count on more ablation studies to validate their effectiveness.
- Distributed multi-view video coding92
A practical framework of distributed multi-view video coding, in which inter-camera communication is avoided and the large computing complexity is moved from encoder to decoder, and the traditional inter frame is replaced by Wyner-Ziv frame.
- A Two-Stream Mutual Attention Network for Semi-Supervised Biomedical Segmentation with Noisy Labels87
A Two-Stream Mutual Attention Network (TSMAN) that weakens the influence of back-propagated gradients caused by incorrect labels, thereby rendering the network robust to unclean data.
- Unambiguous 3D measurement from speckle-embedded fringe85
A novel phase-shifting method for fast, accurate, and unambiguous 3D shape measurement by embedding a speckle-like signal in three sinusoidal fringe patterns to eliminate the phase ambiguity, but without reducing the fringe amplitude or frequency.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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