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Found 39 papers, 16 papers with code
Task-Aware Encoder Control for Deep Video Compression
Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task, mandating a dedicated decoder per task.
Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization
MSE-based models aim to improve objective metrics while generative models are leveraged to improve visual quality measured by subjective metrics.
GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting
In response, we propose a groundbreaking paradigm of image representation and compression by 2D Gaussian Splatting, named GaussianImage.
Boosting Neural Representations for Videos with a Conditional Decoder
Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing, showing remarkable versatility across various video tasks.
Consistency Models Improve Diffusion Inverse Solvers
In addition, we propose a new family of DIS using pure CM.
Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling
For the first stage, we propose a diffusion-based motion field predictor, which focuses on deducing the trajectories of the reference image's pixels.
Idempotence and Perceptual Image Compression
However, we find that theoretically: 1) Conditional generative model-based perceptual codec satisfies idempotence; 2) Unconditional generative model with idempotence constraint is equivalent to conditional generative codec.
Unified learning-based lossy and lossless JPEG recompression
Most image compression algorithms only consider uncompressed original image, while ignoring a large number of already existing JPEG images.
Efficient Learned Lossless JPEG Recompression
Second, we propose pipeline parallel context model (PPCM) and compressed checkerboard context model (CCCM) for the effective conditional modeling and efficient decoding within luma and chroma components.
Conditional Perceptual Quality Preserving Image Compression
We propose conditional perceptual quality, an extension of the perceptual quality defined in \citet{blau2018perception}, by conditioning it on user defined information.
FlowFormer: A Transformer Architecture and Its Masked Cost Volume Autoencoding for Optical Flow
This paper introduces a novel transformer-based network architecture, FlowFormer, along with the Masked Cost Volume AutoEncoding (MCVA) for pretraining it to tackle the problem of optical flow estimation.
VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation
We first propose a TRi-frame Optical Flow (TROF) module that estimates bi-directional optical flows for the center frame in a three-frame manner.
KBNet: Kernel Basis Network for Image Restoration
In this paper, we propose a kernel basis attention (KBA) module, which introduces learnable kernel bases to model representative image patterns for spatial information aggregation.
Ranked #1 on
Color Image Denoising
on McMaster sigma50
FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation
FlowFormer introduces a transformer architecture into optical flow estimation and achieves state-of-the-art performance.
Spatial Moment Pooling Improves Neural Image Assessment
In recent years, there has been widespread attention drawn to convolutional neural network (CNN) based blind image quality assessment (IQA).
Correcting the Sub-optimal Bit Allocation
In this paper, we investigate the problem of bit allocation in Neural Video Compression (NVC).
Multi-Sample Training for Neural Image Compression
This paper considers the problem of lossy neural image compression (NIC).
Bit Allocation using Optimization
In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC).
Flexible Neural Image Compression via Code Editing
Neural image compression (NIC) has outperformed traditional image codecs in rate-distortion (R-D) performance.
Learning Degradation Representations for Image Deblurring
With the integration, MSDI-Net can handle various and complicated blurry patterns adaptively.
Ranked #13 on
Image Deblurring
on GoPro
Evaluating the Practicality of Learned Image Compression
On CPU, the latency of our implementation is comparable with JPEG XL.
A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift
In this study, we propose a simple yet effective framework for video restoration.
Ranked #1 on
Deblurring
on GoPro
(using extra training data)
PO-ELIC: Perception-Oriented Efficient Learned Image Coding
In the past years, learned image compression (LIC) has achieved remarkable performance.
Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency Denoising Network
As for each sub-network, we propose an efficient multi-frequency denoising network to remove noise of different frequencies.
FlowFormer: A Transformer Architecture for Optical Flow
We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural network architecture for learning optical flow.
Ranked #1 on
Optical Flow Estimation
on Sintel-final
Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain
JPEG is a popular image compression method widely used by individuals, data center, cloud storage and network filesystems.
ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding
Recently, learned image compression techniques have achieved remarkable performance, even surpassing the best manually designed lossy image coders.
Ranked #1 on
Image Compression
on kodak
Post-Training Quantization for Cross-Platform Learned Image Compression
It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications.
IDR: Self-Supervised Image Denoising via Iterative Data Refinement
To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes.
Rethinking Noise Synthesis and Modeling in Raw Denoising
However, the real raw image noise is contributed by many noise sources and varies greatly among different sensors.
Ranked #2 on
Image Denoising
on SID SonyA7S2 x100
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning
Learned image compression is making good progress in recent years.
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression
It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications.
Checkerboard Context Model for Efficient Learned Image Compression
To the best of our knowledge, this is the first exploration on parallelization-friendly spatial context model for learned image compression.
Cross-dataset Training for Class Increasing Object Detection
Given two or more already labeled datasets that target for different object classes, cross-dataset training aims to detect the union of the different classes, so that we do not have to label all the classes for all the datasets.
PRUNING WITH HINTS: AN EFFICIENT FRAMEWORK FOR MODEL ACCELERATION
Hints can improve the performance of student model by transferring knowledge from teacher model.
Quantization Mimic: Towards Very Tiny CNN for Object Detection
To the best of our knowledge, our method, called Quantization Mimic, is the first one focusing on very tiny networks.
Impression Network for Video Object Detection
Video object detection is more challenging compared to image object detection.
Joint Training of Cascaded CNN for Face Detection
Cascade has been widely used in face detection, where classifier with low computation cost can be firstly used to shrink most of the background while keeping the recall.
