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Found 20 papers, 6 papers with code
Spiking Tucker Fusion Transformer for Audio-Visual Zero-Shot Learning
The spiking neural networks (SNNs) that efficiently encode temporal sequences have shown great potential in extracting audio-visual joint feature representations.
SpikeMba: Multi-Modal Spiking Saliency Mamba for Temporal Video Grounding
To address these issues, we introduce SpikeMba: a multi-modal spiking saliency mamba for temporal video grounding.
Event-based Visible and Infrared Fusion via Multi-task Collaboration
Visible and Infrared image Fusion (VIF) offers a comprehensive scene description by combining thermal infrared images with the rich textures from visible cameras.
Boosting Spike Camera Image Reconstruction from a Perspective of Dealing with Spike Fluctuations
Different from traditional cameras each pixel in spike cameras records the arrival of photons continuously by firing binary spikes at an ultra-fine temporal granularity.
Super-Resolution Reconstruction from Bayer-Pattern Spike Streams
Experimental results demonstrate that the proposed scheme can reconstruct satisfactory color images with both high temporal and spatial resolution from low-resolution Bayer-pattern spike streams.
WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal
To this end, we propose a method called Weather-aware Multi-scale MoE (WM-MoE) based on Transformer for blind weather removal.
SpikeCV: Open a Continuous Computer Vision Era
SpikeCV focuses on encapsulation for spike data, standardization for dataset interfaces, modularization for vision tasks, and real-time applications for challenging scenes.
Progressive Content-aware Coded Hyperspectral Compressive Imaging
Hyperspectral imaging plays a pivotal role in a wide range of applications, like remote sensing, medicine, and cytology.
1000x Faster Camera and Machine Vision with Ordinary Devices
By treating vidar as spike trains in biological vision, we have further developed a spiking neural network-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1, 000x faster than human vision.
Contrastive and Selective Hidden Embeddings for Medical Image Segmentation
Medical image segmentation has been widely recognized as a pivot procedure for clinical diagnosis, analysis, and treatment planning.
Cross-Block Difference Guided Fast CU Partition for VVC Intra Coding
In this paper, we propose a new fast CU partition algorithm for VVC intra coding based on cross-block difference.
HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging
Hyperspectral imaging is an essential imaging modality for a wide range of applications, especially in remote sensing, agriculture, and medicine.
Optical Flow Estimation for Spiking Camera
Further, for training SCFlow, we synthesize two sets of optical flow data for the spiking camera, SPIkingly Flying Things and Photo-realistic High-speed Motion, denoted as SPIFT and PHM respectively, corresponding to random high-speed and well-designed scenes.
Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation
Recently, many deep learning methods have shown great success in providing promising solutions to many event-based problems, such as optical flow estimation.
Spk2ImgNet: Learning To Reconstruct Dynamic Scene From Continuous Spike Stream
Different from the conventional digital cameras that compact the photoelectric information within the exposure interval into a single snapshot, the spike camera produces a continuous spike stream to record the dynamic light intensity variation process.
Super Resolve Dynamic Scene From Continuous Spike Streams
In this paper, we properly exploit the relative motion and derive the relationship between light intensity and each spike, so as to recover the external scene with both high temporal and high spatial resolution.
Knowledge Transfer via Student-Teacher Collaboration
One way to compress these heavy models is knowledge transfer (KT), in which a light student network is trained through absorbing the knowledge from a powerful teacher network.
Frequency-Domain Dynamic Pruning for Convolutional Neural Networks
Deep convolutional neural networks have demonstrated their powerfulness in a variety of applications.
Image Denoising via Adaptive Soft-Thresholding Based on Non-Local Samples
To estimate the expectation and variance parameters for the transform bands of a particular patch, we exploit the non-local correlation of image and collect a set of similar patches as data samples to form the distribution.
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner.
