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Found 9 papers, 2 papers with code
An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT
Vision Transformers (ViTs) have achieved significant success in computer vision.
A Computationally Efficient Neural Video Compression Accelerator Based on a Sparse CNN-Transformer Hybrid Network
Video compression is widely used in digital television, surveillance systems, and virtual reality.
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution
In this paper, we propose a double-win framework for ideal and blind SR task, named S2R, including a light-weight transformer-based SR model (S2R transformer) and a novel coarse-to-fine training strategy, which can achieve excellent visual results on both ideal and random fuzzy conditions.
An Efficient FPGA-based Accelerator for Deep Forest
In this paper, an efficient hardware accelerator is proposed for deep forest models, which is also the first work to implement Deep Forest on FPGA.
Accelerate Three-Dimensional Generative Adversarial Networks Using Fast Algorithm
3D deconvolution (DeConv), as an important computation of 3D-GAN, significantly increases computational complexity compared with 2D DeConv.
An Efficient FPGA Accelerator for Point Cloud
The submanifold sparse convolutional network (SSCN) has been widely used for the point cloud due to its unique advantages in terms of visual results.
Semi-dense Stereo Matching using Dual CNNs
A robust solution for semi-dense stereo matching is presented.
Multi-scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets
To address this problem, a regularization method named Stochastic Feature Reuse is also presented.
BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis
The key feature of BSD-GAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features.
