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Found 17 papers, 3 papers with code
Towards Homogeneous Modality Learning and Multi-Granularity Information Exploration for Visible-Infrared Person Re-Identification
In order to mitigate the impact of large modality discrepancy existing in heterogeneous images, previous methods attempt to apply generative adversarial network (GAN) to generate the modality-consisitent data.
FPPN: Future Pseudo-LiDAR Frame Prediction for Autonomous Driving
LiDAR sensors are widely used in autonomous driving due to the reliable 3D spatial information.
End-to-end Neural Video Coding Using a Compound Spatiotemporal Representation
We further design a one-to-many decoder pipeline to generate multiple predictions from the CSTR, including vector-based resampling, adaptive kernel-based resampling, compensation mode selection maps and texture enhancements, and combines them adaptively to achieve more accurate inter prediction.
PDWN: Pyramid Deformable Warping Network for Video Interpolation
At the finest scale, the two warped frames are adaptively blended to generate the middle frame.
SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification
In feature-level, we improve the conventional two-stream network through balancing the number of specific and sharable convolutional blocks, which preserve the spatial structure information of features.
Neural Video Coding using Multiscale Motion Compensation and Spatiotemporal Context Model
Over the past two decades, traditional block-based video coding has made remarkable progress and spawned a series of well-known standards such as MPEG-4, H. 264/AVC and H. 265/HEVC.
Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision
Pseudo-LiDAR point cloud interpolation is a novel and challenging task in the field of autonomous driving, which aims to address the frequency mismatching problem between camera and LiDAR.
Object-Based Image Coding: A Learning-Driven Revisit
The Object-Based Image Coding (OBIC) that was extensively studied about two decades ago, promised a vast application perspective for both ultra-low bitrate communication and high-level semantical content understanding, but it had rarely been used due to the inefficient compact representation of object with arbitrary shape.
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration
Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency.
Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling
This paper proposes a novel Non-Local Attention optmization and Improved Context modeling-based image compression (NLAIC) algorithm, which is built on top of the deep nerual network (DNN)-based variational auto-encoder (VAE) structure.
A Dual Camera System for High Spatiotemporal Resolution Video Acquisition
This paper presents a dual camera system for high spatiotemporal resolution (HSTR) video acquisition, where one camera shoots a video with high spatial resolution and low frame rate (HSR-LFR) and another one captures a low spatial resolution and high frame rate (LSR-HFR) video.
Learned Point Cloud Geometry Compression
This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a. k. a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE).
PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation
In this paper, we propose a novel Pseudo-LiDAR interpolation network (PLIN) to increase the frequency of LiDAR sensors.
Non-local Attention Optimized Deep Image Compression
This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure.
Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization
We propose a MultiScale AutoEncoder(MSAE) based extreme image compression framework to offer visually pleasing reconstruction at a very low bitrate.
Gated Context Model with Embedded Priors for Deep Image Compression
Besides, a field study on perceptual quality is also given via a dedicated subjective assessment, to compare the efficiency of our proposed methods and other conventional image compression methods.
Deep Image Compression via End-to-End Learning
We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate.
