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Found 14 papers, 2 papers with code
PATFinger: Prompt-Adapted Transferable Fingerprinting against Unauthorized Multimodal Dataset Usage
The multimodal datasets can be leveraged to pre-train large-scale vision-language models by providing cross-modal semantics.
AB-Cache: Training-Free Acceleration of Diffusion Models via Adams-Bashforth Cached Feature Reuse
Diffusion models have demonstrated remarkable success in generative tasks, yet their iterative denoising process results in slow inference, limiting their practicality.
A Survey of Foundation Models for Music Understanding
Music is essential in daily life, fulfilling emotional and entertainment needs, and connecting us personally, socially, and culturally.
Rethinking the Key Factors for the Generalization of Remote Sensing Stereo Matching Networks
Stereo matching, a critical step of 3D reconstruction, has fully shifted towards deep learning due to its strong feature representation of remote sensing images.
A Multi-timescale and Chance-Constrained Energy Dispatching Strategy of Integrated Heat-Power Community with Shared Hybrid Energy Storage
In the integrated heat-power system with coupling heat-power generators and demands, the key challenges lie in the interaction between heat and power, the inherent uncertainty of renewable energy and consumers' demands, and the multi-timescale scheduling of heat and power.
A Constrained BA Algorithm for Rate-Distortion and Distortion-Rate Functions
The Blahut-Arimoto (BA) algorithm has played a fundamental role in the numerical computation of rate-distortion (RD) functions.
Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation
Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement.
An Indirect Rate-Distortion Characterization for Semantic Sources: General Model and the Case of Gaussian Observation
A new source model, which consists of an intrinsic state part and an extrinsic observation part, is proposed and its information-theoretic characterization, namely its rate-distortion function, is defined and analyzed.
Decentralized Federated Learning: Balancing Communication and Computing Costs
The performance of decentralized SGD is jointly influenced by inter-node communications and local updates.
Blind Diagnosis for Millimeter-wave Large-scale Antenna Systems
Millimeter-wave (mmWave) communication systems rely on large-scale antenna arrays to combat large path-loss at mmWave band.
Diagnosis of Intelligent Reflecting Surface in Millimeter-wave Communication Systems
In the second case where only partial CSI is available, we jointly exploit the sparsity of the millimeter-wave channel and the failure, and adopt compressed sparse and low-rank matrix recovery algorithm to decouple channel and failure.
Deep-Learned Broadband Encoding Stochastic Filters for Computational Spectroscopic Instruments
Computational spectroscopic instruments with Broadband Encoding Stochastic (BEST) filters allow the reconstruction of the spectrum at high precision with only a few filters.
Instrumentation and Detectors
Accelerating Federated Learning via Momentum Gradient Descent
The proposed momentum federated learning (MFL) uses momentum gradient descent (MGD) in the local update step of FL system.
A Regression Approach to Certain Information Transmission Problems
A general information transmission model, under independent and identically distributed Gaussian codebook and nearest neighbor decoding rule with processed channel output, is investigated using the performance metric of generalized mutual information.
