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Found 36 papers, 13 papers with code
Neural Texture Extraction and Distribution for Controllable Person Image Synthesis
Our model is trained to predict human images in arbitrary poses, which encourages it to extract disentangled and expressive neural textures representing the appearance of different semantic entities.
PCRP: Unsupervised Point Cloud Object Retrieval and Pose Estimation
An unsupervised point cloud object retrieval and pose estimation method, called PCRP, is proposed in this work.
OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression
In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution.
GPCO: An Unsupervised Green Point Cloud Odometry Method
GPCO is an unsupervised learning method that predicts object motion by matching features of consecutive point cloud scans.
Online Meta Adaptation for Variable-Rate Learned Image Compression
This work addresses two major issues of end-to-end learned image compression (LIC) based on deep neural networks: variable-rate learning where separate networks are required to generate compressed images with varying qualities, and the train-test mismatch between differentiable approximate quantization and true hard quantization.
GSIP: Green Semantic Segmentation of Large-Scale Indoor Point Clouds
It is named GSIP (Green Segmentation of Indoor Point clouds) and its performance is evaluated on a representative large-scale benchmark -- the Stanford 3D Indoor Segmentation (S3DIS) dataset.
PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering
The proposed model can generate photo-realistic portrait images with accurate movements according to intuitive modifications.
Combining Attention with Flow for Person Image Synthesis
Pose-guided person image synthesis aims to synthesize person images by transforming reference images into target poses.
Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression
Compressing Deep Neural Network (DNN) models to alleviate the storage and computation requirements is essential for practical applications, especially for resource limited devices.
Recent Standard Development Activities on Video Coding for Machines
Specifically, we will first provide an overview of the MPEG VCM group including use cases, requirements, processing pipelines, plan for potential VCM standards, followed by the evaluation framework including machine-vision tasks, dataset, evaluation metrics, and anchor generation.
Substitutional Neural Image Compression
The key idea is to replace the image to be compressed with a substitutional one that outperforms the original one in a desired way.
Tencent Video Dataset (TVD): A Video Dataset for Learning-based Visual Data Compression and Analysis
Learning-based visual data compression and analysis have attracted great interest from both academia and industry recently.
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method
Inspired by the recent PointHop classification method, an unsupervised 3D point cloud registration method, called R-PointHop, is proposed in this work.
An Optimized H.266/VVC Software Decoder On Mobile Platform
As the successor of H. 265/HEVC, the new versatile video coding standard (H. 266/VVC) can provide up to 50% bitrate saving with the same subjective quality, at the cost of increased decoding complexity.
Structure-Transformed Texture-Enhanced Network for Person Image Synthesis
Pose-guided virtual try-on task aims to modify the fashion item based on pose transfer task.
High Quality Disparity Remapping With Two-Stage Warping
In the second stage, we derive another warping model to refine warping results in less important regions by eliminating serious distortions in shape, disparity and 3D structure.
SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains
This issue makes the generator lack the incentive from the discriminator to learn high-frequency content of data, resulting in a significant spectrum discrepancy between generated images and real images.
TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis
On one hand, we propose to discriminate ground-truth waveform from synthetic one in frequency domain for offering more consistency guarantees instead of only in time domain.
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation
The UFF method exploits statistical correlations of points in a point cloud set to learn shape and point features in a one-pass feedforward manner through a cascaded encoder-decoder architecture.
Unsupervised Point Cloud Registration via Salient Points Analysis (SPA)
An unsupervised point cloud registration method, called salient points analysis (SPA), is proposed in this work.
Deep Spatial Transformation for Pose-Guided Person Image Generation and Animation
We show that our framework can spatially transform the inputs in an efficient manner.
Toward Zero-Shot Unsupervised Image-to-Image Translation
Recent studies have shown remarkable success in unsupervised image-to-image translation.
Learning Model-Blind Temporal Denoisers without Ground Truths
Denoisers trained with synthetic data often fail to cope with the diversity of unknown noises, giving way to methods that can adapt to existing noise without knowing its ground truth.
AdaDurIAN: Few-shot Adaptation for Neural Text-to-Speech with DurIAN
This paper investigates how to leverage a DurIAN-based average model to enable a new speaker to have both accurate pronunciation and fluent cross-lingual speaking with very limited monolingual data.
PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction.
RoIMix: Proposal-Fusion among Multiple Images for Underwater Object Detection
Generic object detection algorithms have proven their excellent performance in recent years.
C3DVQA: Full-Reference Video Quality Assessment with 3D Convolutional Neural Network
However, video quality exhibits different characteristics from static image quality due to the existence of temporal masking effects.
Multi-mapping Image-to-Image Translation via Learning Disentanglement
Recent advances of image-to-image translation focus on learning the one-to-many mapping from two aspects: multi-modal translation and multi-domain translation.
StructureFlow: Image Inpainting via Structure-aware Appearance Flow
Image inpainting techniques have shown significant improvements by using deep neural networks recently.
PointHop: An Explainable Machine Learning Method for Point Cloud Classification
In the attribute building stage, we address the problem of unordered point cloud data using a space partitioning procedure and developing a robust descriptor that characterizes the relationship between a point and its one-hop neighbor in a PointHop unit.
Residual-Guided In-Loop Filter Using Convolution Neural Network
The block-based coding structure in the hybrid video coding framework inevitably introduces compression artifacts such as blocking, ringing, etc.
Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds
Point cloud is a fundamental 3D representation which is widely used in real world applications such as autonomous driving.
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection
Remarkably, we obtain the frame-level AUC score of 82. 12% on UCF-Crime.
Generative Adversarial Network based Speaker Adaptation for High Fidelity WaveNet Vocoder
Firstly, due to the noisy input signal of the model, there is still a gap between the quality of generated and natural waveforms.
BLP -- Boundary Likelihood Pinpointing Networks for Accurate Temporal Action Localization
Despite tremendous progress achieved in temporal action detection, state-of-the-art methods still suffer from the sharp performance deterioration when localizing the starting and ending temporal action boundaries.
Multi-Mapping Image-to-Image Translation with Central Biasing Normalization
Recent advances in image-to-image translation have seen a rise in approaches generating diverse images through a single network.
