Search Results for author:
Found 13 papers, 5 papers with code
SpikeMba: Multi-Modal Spiking Saliency Mamba for Temporal Video Grounding
To address these limitations, we introduce a novel SpikeMba: multi-modal spiking saliency mamba for temporal video grounding.
QKFormer: Hierarchical Spiking Transformer using Q-K Attention
ii) We incorporate the hierarchical structure, which significantly benefits the performance of both the brain and artificial neural networks, into spiking transformers to obtain multi-scale spiking representation.
Deep Network for Image Compressed Sensing Coding Using Local Structural Sampling
In this paper, we propose a new CNN based image CS coding framework using local structural sampling (dubbed CSCNet) that includes three functional modules: local structural sampling, measurement coding and Laplacian pyramid reconstruction.
Deep Unfolding Network for Image Compressed Sensing by Content-adaptive Gradient Updating and Deformation-invariant Non-local Modeling
In this paper, inspired by the traditional Proximal Gradient Descent (PGD) algorithm, a novel DUN for image compressed sensing (dubbed DUN-CSNet) is proposed to solve the above two issues.
Spiking Semantic Communication for Feature Transmission with HARQ
In Collaborative Intelligence (CI), the Artificial Intelligence (AI) model is divided between the edge and the cloud, with intermediate features being sent from the edge to the cloud for inference.
Enhancing the Performance of Transformer-based Spiking Neural Networks by SNN-optimized Downsampling with Precise Gradient Backpropagation
In this paper, we propose ConvBN-MaxPooling-LIF (CML), an SNN-optimized downsampling with precise gradient backpropagation.
The style transformer with common knowledge optimization for image-text retrieval
The main module is the common knowledge adaptor (CKA) with both the style embedding extractor (SEE) and the common knowledge optimization (CKO) modules.
Event-based Monocular Dense Depth Estimation with Recurrent Transformers
Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e. g., motion blur and low light) in monocular depth estimation.
SNN-SC: A Spiking Semantic Communication Framework for Feature Transmission
However, most of the existing SC works only transmit analog information on the AWGN channel and cannot be directly used for digital channels.
Task-Agnostic Learning to Accomplish New Tasks
Inspired by this observation, this paper proposes a task-agnostic learning method (TAL for short) that can learn fragmented knowledge from task-agnostic data to accomplish new tasks.
Deep Joint Source-Channel Coding for Multi-Task Network
The feature generated by the mobile device is transmitted through the wireless channel to the edge server.
COLA-Net: Collaborative Attention Network for Image Restoration
Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance.
SNR-adaptive deep joint source-channel coding for wireless image transmission
Considering the problem of joint source-channel coding (JSCC) for multi-user transmission of images over noisy channels, an autoencoder-based novel deep joint source-channel coding scheme is proposed in this paper.
