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Found 33 papers, 15 papers with code
QARV: Quantization-Aware ResNet VAE for Lossy Image Compression
We consider the problem of lossy image compression, a fundamental problem in both information theory and many real-world applications.
Efficient Visual Computing with Camera RAW Snapshots
Conventional cameras capture image irradiance on a sensor and convert it to RGB images using an image signal processor (ISP).
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks
In order to find them, we further propose a LiDAR-guided sampling strategy to leverage the statistical distribution of LiDAR to determine the heights of local slices.
BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object Detection
In this paper, we propose a unified framework named BEV-LGKD to transfer the knowledge in the teacher-student manner.
Multi-latent Space Alignments for Unsupervised Domain Adaptation in Multi-view 3D Object Detection
Vision-Centric Bird-Eye-View (BEV) perception has shown promising potential and attracted increasing attention in autonomous driving.
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression
Quantizing a floating-point neural network to its fixed-point representation is crucial for Learned Image Compression (LIC) because it assures the decoding consistency for interoperability and reduces space-time complexity for implementation.
Complementary consistency semi-supervised learning for 3D left atrial image segmentation
The inter-model perturbation is formed between the main model and the auxiliary model to form complementary consistency training.
Lossy Image Compression with Quantized Hierarchical VAEs
Recent work has shown a strong theoretical connection between variational autoencoders (VAEs) and the rate distortion theory.
Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer
Neuromorphic spike data, an upcoming modality with high temporal resolution, has shown promising potential in real-world applications due to its inherent advantage to overcome high-velocity motion blur.
Uncertainty Guided Depth Fusion for Spike Camera
In this paper, we propose a novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo depth estimation networks for spike camera.
Efficient Meta-Tuning for Content-aware Neural Video Delivery
Our method significantly reduces the computational cost and achieves even better performance, paving the way for applying neural video delivery techniques to practical applications.
Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation
Previous works on morphable models mostly focus on large-scale facial geometry but ignore facial details.
High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation
To this end, Integrated Convolution and Self-Attention (ICSA) unit is first proposed to form a content-adaptive transform to characterize and embed neighborhood information dynamically of any input.
Adaptive Patch Exiting for Scalable Single Image Super-Resolution
Once the incremental capacity is below the threshold, the patch can exit at the specific layer.
Opening the Black Box of Learned Image Coders
End-to-end learned lossy image coders (LICs), as opposed to hand-crafted image codecs, have shown increasing superiority in terms of the rate-distortion performance.
NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery
Therefore, we study how to utilize point labels to extract water bodies and propose a novel method called the neighbor feature aggregation network (NFANet).
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution
However, existing methods mostly train the DNNs on uniformly sampled LR-HR patch pairs, which makes them fail to fully exploit informative patches within the image.
Transformer-based Image Compression
A Transformer-based Image Compression (TIC) approach is developed which reuses the canonical variational autoencoder (VAE) architecture with paired main and hyper encoder-decoders.
Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation
Internet video delivery has undergone a tremendous explosion of growth over the past few years.
Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion
We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network.
Ranked #22 on
Semantic Segmentation
on NYU Depth v2
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.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
Contextual Graph Reasoning Networks
Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks.
Decoder-side Cross Resolution Synthesis for Video Compression Enhancement
This paper proposes a decoder-side Cross Resolution Synthesis (CRS) module to pursue better compression efficiency beyond the latest Versatile Video Coding (VVC), where we encode intra frames at original high resolution (HR), compress inter frames at a lower resolution (LR), and then super-resolve decoded LR inter frames with the help from preceding HR intra and neighboring LR inter frames.
LID 2020: The Learning from Imperfect Data Challenge Results
The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect data and improve the data-efficiency during training.
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.
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration
Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency.
A Closed-form Solution to Universal Style Transfer
Although plenty of methods have been proposed, a theoretical analysis of feature transform is still missing.
Learned Quality Enhancement via Multi-Frame Priors for HEVC Compliant Low-Delay Applications
Networked video applications, e. g., video conferencing, often suffer from poor visual quality due to unexpected network fluctuation and limited bandwidth.
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels
Convolutional neural networks have been proven effective in a variety of image restoration tasks.
Decoder Network Over Lightweight Reconstructed Feature for Fast Semantic Style Transfer
Our method decomposes the semantic style transfer problem into feature reconstruction part and feature decoder part.
RON: Reverse Connection with Objectness Prior Networks for Object Detection
To address (a), we design the reverse connection, which enables the network to detect objects on multi-levels of CNNs.
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes.
