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Found 28 papers, 14 papers with code
Advanced Learning-Based Inter Prediction for Future Video Coding
LLIP enhances the filtering process by leveraging a lightweight neural network model, where parameters can be exported for efficient inference.
Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation
On the other hand, JPMA is proposed to assemble multiple hypotheses generated by D3DP into a single 3D pose for practical use.
Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling
Each score is derived from machine perceptual differences between original and compressed images.
STIP: A SpatioTemporal Information-Preserving and Perception-Augmented Model for High-Resolution Video Prediction
To solve the information loss problem, the proposed model aims to preserve the spatiotemporal information for videos during the feature extraction and the state transitions, respectively.
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution
As a highly ill-posed issue, single image super-resolution (SISR) has been widely investigated in recent years.
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality Assessment
Image super-resolution (SR) has been widely investigated in recent years.
Learning Weighting Map for Bit-Depth Expansion within a Rational Range
Existing BDE methods have no unified solution for various BDE situations, and directly learn a mapping for each pixel from LBD image to the desired value in HBD image, which may change the given high-order bits and lead to a huge deviation from the ground truth.
STAU: A SpatioTemporal-Aware Unit for Video Prediction and Beyond
In this paper, we propose a SpatioTemporal-Aware Unit (STAU) for video prediction and beyond by exploring the significant spatiotemporal correlations in videos.
STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction
In this paper, we propose a Spatiotemporal Residual Predictive Model (STRPM) for high-resolution video prediction.
P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation
In Stage II, the pre-trained encoder is loaded to STMO model and fine-tuned.
Ranked #10 on
Monocular 3D Human Pose Estimation
on Human3.6M
Cross-SRN: Structure-Preserving Super-Resolution Network with Cross Convolution
It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with correct and clear details.
MAU: A Motion-Aware Unit for Video Prediction and Beyond
The attention module aims to learn an attention map based on the correlations between the current spatial state and the historical spatial states.
Ranked #19 on
Video Prediction
on Moving MNIST
Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection
For the second issue, we reconsider how to improve detection efficiency with excellent performance, and then propose our lightweight encoder-decoder architecture termed CarNet.
Improving Robustness and Accuracy via Relative Information Encoding in 3D Human Pose Estimation
To alleviate these two problems, we propose a relative information encoding method that yields positional and temporal enhanced representations.
Ranked #13 on
Monocular 3D Human Pose Estimation
on Human3.6M
Rate Distortion Characteristic Modeling for Neural Image Compression
End-to-end optimized neural image compression (NIC) has obtained superior lossy compression performance recently.
Visual Analysis Motivated Rate-Distortion Model for Image Coding
We first propose the region of interest for machine (ROIM) to evaluate the degree of importance for each coding tree unit (CTU) in visual analysis.
Implicit Subspace Prior Learning for Dual-Blind Face Restoration
Face restoration is an inherently ill-posed problem, where additional prior constraints are typically considered crucial for mitigating such pathology.
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution
Based on the observation, in this paper, we build a sequential hierarchical learning super-resolution network (SHSR) for effective image SR.
Ranked #12 on
Image Super-Resolution
on Manga109 - 3x upscaling
Region-adaptive Texture Enhancement for Detailed Person Image Synthesis
The ability to produce convincing textural details is essential for the fidelity of synthesized person images.
Ranked #4 on
Pose Transfer
on Deep-Fashion
Towards Fine-grained Human Pose Transfer with Detail Replenishing Network
Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality.
Iterative Network for Image Super-Resolution
A novel iterative super-resolution network (ISRN) is proposed on top of the iterative optimization.
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
Existing face restoration researches typically relies on either the degradation prior or explicit guidance labels for training, which often results in limited generalization ability over real-world images with heterogeneous degradations and rich background contents.
Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression
In particular, we study the feature and texture compression in a scalable coding framework, where the base layer serves as the deep learning feature and enhancement layer targets to perfectly reconstruct the texture.
Masked Non-Autoregressive Image Captioning
Existing captioning models often adopt the encoder-decoder architecture, where the decoder uses autoregressive decoding to generate captions, such that each token is generated sequentially given the preceding generated tokens.
Self-critical n-step Training for Image Captioning
Existing methods for image captioning are usually trained by cross entropy loss, which leads to exposure bias and the inconsistency between the optimizing function and evaluation metrics.
Image and Video Compression with Neural Networks: A Review
Deep convolution neural network (CNN) which makes the neural network resurge in recent years and has achieved great success in both artificial intelligent and signal processing fields, also provides a novel and promising solution for image and video compression.
Scalable Facial Image Compression with Deep Feature Reconstruction
In this paper, we propose a scalable image compression scheme, including the base layer for feature representation and enhancement layer for texture representation.
Spatial-Temporal Residue Network Based In-Loop Filter for Video Coding
Deep learning has demonstrated tremendous break through in the area of image/video processing.
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