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Found 12 papers, 1 papers with code
Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication
The third framework is designed to accommodate the non-coherent scheme involving a small number of data bits, which simultaneously performs AD and DD.
Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization
In this paper, we propose a novel USS framework called Expand-and-Quantize Unsupervised Semantic Segmentation (EQUSS), which combines the benefits of high-dimensional spaces for better clustering and product quantization for effective information compression.
Ranked #3 on
Unsupervised Semantic Segmentation
on Potsdam-3
Depth-Relative Self Attention for Monocular Depth Estimation
Monocular depth estimation is very challenging because clues to the exact depth are incomplete in a single RGB image.
Semantic-Preserving Augmentation for Robust Image-Text Retrieval
Image text retrieval is a task to search for the proper textual descriptions of the visual world and vice versa.
Beamforming Design with Partial Channel Estimation and Feedback for FDD RIS-Assisted Systems
To acquire the dominating path gain information (DPGI, also regarded as the path gains of selected dominant paths) at the base station (BS), we propose a DPGI estimation and feedback scheme by jointly beamforming design at BS and RIS.
Vision Transformer-based Feature Extraction for Generalized Zero-Shot Learning
In AAM, the correlation between each patch feature and the synthetic image attribute is used as the importance weight for each patch.
Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming
Beamforming technique realized by the multiple-input-multiple-output (MIMO) antenna arrays has been widely used to compensate for the severe path loss in the millimeter wave (mmWave) bands.
Towards Deep Learning-aided Wireless Channel Estimation and Channel State Information Feedback for 6G
Deep learning (DL), a branch of artificial intelligence (AI) techniques, has shown great promise in various disciplines such as image classification and segmentation, speech recognition, language translation, among others.
Semantic Feature Extraction for Generalized Zero-shot Learning
Key idea of the proposed approach, henceforth referred to as semantic feature extraction-based GZSL (SE-GZSL), is to use the semantic feature containing only attribute-related information in learning the relationship between the image and the attribute.
TernGEMM: GEneral Matrix Multiply Library with Ternary Weights for Fast DNN Inference
We propose TernGEMM, a special GEMM library using SIMD instructions for Deep Neural Network (DNN) inference with ternary weights and activations under 8-bit.
Gradual Federated Learning with Simulated Annealing
Federated averaging (FedAvg) is a popular federated learning (FL) technique that updates the global model by averaging local models and then transmits the updated global model to devices for their local model update.
Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility
In this paper, we propose a deep learning-based beam tracking method for millimeter-wave (mmWave)communications.
