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Found 26 papers, 3 papers with code
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Eliminating Warping Shakes for Unsupervised Online Video Stitching
In this paper, we retarget video stitching to an emerging issue, named warping shake, when extending image stitching to video stitching.
SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature Learning
We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples.
Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices
Due to its promising classification performance, sparse representation based classification(SRC) algorithm has attracted great attention in the past few years.
Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency
This paper addresses the challenging problem of estimating the general visual attention of people in images.
R$^3$-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos
In this paper, we propose a novel deep network, called rotatable region-based residual network (R$^3$-Net), to detect multi-oriented vehicles in aerial images and videos.
Attention Distillation for Learning Video Representations
To this end, we make use of attention modules that learn to highlight regions in the video and aggregate features for recognition.
Ranked #39 on
Action Recognition
on UCF101
A Novel Deep Neural Network Based Approach for Sparse Code Multiple Access
Furthermore, by manipulating the mapping vectors, an autoencoder is able to generalize SCMA, thus a dense code multiple access (DCMA) scheme is proposed.
Stereo-based Multi-motion Visual Odometry for Mobile Robots
In this letter, a stereo-based multi-motion visual odometry method is proposed to acquire the poses of the robot and other moving objects.
DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation
The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects.
A Dynamic 3D Spontaneous Micro-expression Database: Establishment and Evaluation
Micro-expressions are spontaneous, unconscious facial movements that show people's true inner emotions and have great potential in related fields of psychological testing.
Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display
The impact of distorted geometry and texture attributes is further discussed in this paper.
Sphere Face Model:A 3D Morphable Model with Hypersphere Manifold Latent Space
However, the shape parameters of traditional 3DMMs satisfy the multivariate Gaussian distribution while the identity embeddings satisfy the hypersphere distribution, and this conflict makes it challenging for face reconstruction models to preserve the faithfulness and the shape consistency simultaneously.
Distortion-Aware Loop Filtering of Intra 360^o Video Coding with Equirectangular Projection
In this paper, we propose a distortion-aware loop filtering model to improve the performance of intra coding for 360$^o$ videos projected via equirectangular projection (ERP) format.
Remote blood pressure measurement via spatiotemporal mapping of a short-time facial video
Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases.
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding
To further improve the performance of intra coding in Versatile Video Coding (VVC), an intelligent intra mode derivation method is proposed in this paper, termed as Deep Learning based Intra Mode Derivation (DLIMD).
FastBVP-Net: a lightweight pulse extraction network for measuring heart rhythm via facial videos
Remote photoplethysmography (rPPG) is an attractive camera-based health monitoring method that can measure the heart rhythm from facial videos.
Content-Preserving Image Stitching with Piecewise Rectangular Boundary Constraints
By analyzing the irregular boundary, we construct a piecewise rectangular boundary.
Epigenetic factor competition reshapes the EMT landscape
In such models, epigenetic factors are usually proposed to act on the chromatin regions directly involved in the expression of relevant genes.
Enhancing Recommender Systems: A Strategy to Mitigate False Negative Impact
In implicit collaborative filtering (CF) task of recommender systems, recent works mainly focus on model structure design with promising techniques like graph neural networks (GNNs).
LSCD: A Large-Scale Screen Content Dataset for Video Compression
Multimedia compression allows us to watch videos, see pictures and hear sounds within a limited bandwidth, which helps the flourish of the internet.
Data Upcycling Knowledge Distillation for Image Super-Resolution
Knowledge distillation (KD) compresses deep neural networks by transferring task-related knowledge from cumbersome pre-trained teacher models to compact student models.
Ranked #22 on
Image Super-Resolution
on Urban100 - 4x upscaling
CBQ: Cross-Block Quantization for Large Language Models
Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs.
Graph Elimination Networks
Graph Neural Networks (GNNs) are widely applied across various domains, yet they perform poorly in deep layers.
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement
However, most AT methods suffer from robust overfitting, i. e., a significant generalization gap in adversarial robustness between the training and testing curves.
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution
Knowledge distillation (KD) is a promising yet challenging model compression technique that transfers rich learning representations from a well-performing but cumbersome teacher model to a compact student model.
