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Found 33 papers, 16 papers with code
Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution
To synthesize more compression-lost details and refine temporal consistency, we propose a novel Spatial Degradation-Aware and Temporal Consistent (SDATC) diffusion model for compressed VSR.
CANeRV: Content Adaptive Neural Representation for Video Compression
Furthermore, to enhance the capture of dynamics between frames within a sequence, we implement a dynamic frame-level adjustment (DFA).
Predicting Satisfied User and Machine Ratio for Compressed Images: A Unified Approach
It is meaningful to predict the perceptual quality of compressed images for both humans and machines, which guides the optimization for compression.
Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution
Omnidirectional video (ODV) can provide an immersive experience and is widely utilized in the field of virtual reality and augmented reality.
Releasing the Parameter Latency of Neural Representation for High-Efficiency Video Compression
For decades, video compression technology has been a prominent research area.
KAN4TSF: Are KAN and KAN-based models Effective for Time Series Forecasting?
To this end, we introduce the Kolmogorov-Arnold Network (KAN) into time series forecasting research, which has better mathematical properties and interpretability.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
In this paper, we present an end-to-end framework Structure-CLIP, which integrates Scene Graph Knowledge (SGK) to enhance multi-modal structured representations.
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.
Scene Matters: Model-based Deep Video Compression
Video compression has always been a popular research area, where many traditional and deep video compression methods have been proposed.
Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling
Each score is derived from machine perceptual differences between original and compressed images.
Cross Modal Compression: Towards Human-comprehensible Semantic Compression
With the recent advances in cross modal translation and generation, in this paper, we propose the cross modal compression~(CMC), a semantic compression framework for visual data, to transform the high redundant visual data~(such as image, video, etc.)
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.
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 #11 on
Monocular 3D Human Pose Estimation
on Human3.6M
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
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 #14 on
Monocular 3D Human Pose Estimation
on Human3.6M
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 #15 on
Image Super-Resolution
on Manga109 - 3x upscaling
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.
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 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.
Direct Speech-to-image Translation
In this paper, we attempt to translate the speech signals into the image signals without the transcription stage.
Multimedia Sound Audio and Speech Processing
Learning to fool the speaker recognition
Due to the widespread deployment of fingerprint/face/speaker recognition systems, attacking deep learning based biometric systems has drawn more and more attention.
Audio and Speech Processing Cryptography and Security Sound
Universal Adversarial Perturbations Generative Network for Speaker Recognition
Attacking deep learning based biometric systems has drawn more and more attention with the wide deployment of fingerprint/face/speaker recognition systems, given the fact that the neural networks are vulnerable to the adversarial examples, which have been intentionally perturbed to remain almost imperceptible for human.
A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation
Explainability is a gateway between Artificial Intelligence and society as the current popular deep learning models are generally weak in explaining the reasoning process and prediction results.
Explaining the Predictions of Any Image Classifier via Decision Trees
Despite outstanding contribution to the significant progress of Artificial Intelligence (AI), deep learning models remain mostly black boxes, which are extremely weak in explainability of the reasoning process and prediction results.
PgNN: Physics-guided Neural Network for Fourier Ptychographic Microscopy
Since the optimal parameters of the PgNN can be derived by minimizing the difference between the model-generated images and real captured angle-varied images corresponding to the same scene, the proposed PgNN can get rid of the problem of massive training data as in traditional supervised methods.
Cascaded Parallel Filtering for Memory-Efficient Image-Based Localization
Image-based localization (IBL) aims to estimate the 6DOF camera pose for a given query image.
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.
Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning
In crowded scenes, detection and localization of abnormal behaviors is challenging in that high-density people make object segmentation and tracking extremely difficult.
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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