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Found 12 papers, 5 papers with code
Distributed Stackelberg Equilibrium Seeking for Networked Multi-Leader Multi-Follower Games with A Clustered Information Structure
In this case, the leaders cannot fully anticipate the collective rational response of all followers with its local information.
Integrated Distributed Semantic Communication and Over-the-air Computation for Cooperative Spectrum Sensing
Extensive simulations verify the superiority of ICC-CSS compared with various conventional CSS schemes in terms of detection performance, robustness to SNR variations in both sensing and reporting channels, as well as scalability with respect to the number of samples and sensors.
Deep Learning-Empowered Semantic Communication Systems with a Shared Knowledge Base
With the aid of the shared knowledge base, the proposed system integrates the message and corresponding knowledge from the shared knowledge base to obtain the residual information, which enables the system to transmit fewer symbols without semantic performance degradation.
CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility
In this paper, we present CValues, the first Chinese human values evaluation benchmark to measure the alignment ability of LLMs in terms of both safety and responsibility criteria.
No-regret learning for repeated non-cooperative games with lossy bandits
This paper considers no-regret learning for repeated continuous-kernel games with lossy bandit feedback.
EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer
To address this issue, we propose a novel 1-D EEG signal denoising network with 2-D transformer, namely EEGDnet.
Omniscient Video Super-Resolution
Most recent video super-resolution (SR) methods either adopt an iterative manner to deal with low-resolution (LR) frames from a temporally sliding window, or leverage the previously estimated SR output to help reconstruct the current frame recurrently.
Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement
Different from existing methods tending to accomplish the relighting task directly by ignoring the fidelity and naturalness recovery, we investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps.
Online Convex Optimization Over Erdos-Renyi Random Networks
The regret bounds scaling with respect to $T$ match those obtained by state-of-the-art algorithms and fundamental limits in the corresponding centralized online optimization problems, e. g., $\mathcal{O}(\sqrt{T}) $ and $\mathcal{O}(\ln(T)) $ regrets are established for convex and strongly convex losses with full gradient feedback and two-points information, respectively.
Multi-Scale Progressive Fusion Network for Single Image Deraining
In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network (MSPFN) for single image rain streak removal.
Ranked #6 on
Single Image Deraining
on Test2800
Masked Face Recognition Dataset and Application
These datasets are freely available to industry and academia, based on which various applications on masked faces can be developed.
Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations
Most previous fusion strategies either fail to fully utilize temporal information or cost too much time, and how to effectively fuse temporal information from consecutive frames plays an important role in video super-resolution (SR).
