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Found 51 papers, 17 papers with code
Recent Progress of Face Image Synthesis
Face synthesis has been a fascinating yet challenging problem in computer vision and machine learning.
An Effective Training Method For Deep Convolutional Neural Network
In this paper, we propose the nonlinearity generation method to speed up and stabilize the training of deep convolutional neural networks.
Load Balanced GANs for Multi-view Face Image Synthesis
Multi-view face synthesis from a single image is an ill-posed problem and often suffers from serious appearance distortion.
Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization
We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks.
Geometry-Aware Face Completion and Editing
Extensive experimental results qualitatively and quantitatively demonstrate that our network is able to generate visually pleasing face completion results and edit face attributes as well.
Location-Centered House Price Prediction: A Multi-Task Learning Approach
Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently.
Cross-spectral Face Completion for NIR-VIS Heterogeneous Face Recognition
This paper models high resolution heterogeneous face synthesis as a complementary combination of two components, a texture inpainting component and pose correction component.
Biphasic Learning of GANs for High-Resolution Image-to-Image Translation
In this work, we present a novel training framework for GANs, namely biphasic learning, to achieve image-to-image translation in multiple visual domains at $1024^2$ resolution.
Accurate Human Gesture Sensing With Coarse-Grained RF Signatures
RF-based gesture sensing and recognition has increasingly attracted intense academic and industrial interest due to its various device-free applications in daily life, such as elder monitoring, mobile games.
Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes
Specifically, we address the problem of providing real-time guidance to therapists with a dialogue observer that (1) categorizes therapist and client MI behavioral codes and, (2) forecasts codes for upcoming utterances to help guide the conversation and potentially alert the therapist.
Rhetorically Controlled Encoder-Decoder for Modern Chinese Poetry Generation
Rhetoric is a vital element in modern poetry, and plays an essential role in improving its aesthetics.
PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer
In this paper, we address the makeup transfer task, which aims to transfer the makeup from a reference image to a source image.
Amazon at MRP 2019: Parsing Meaning Representations with Lexical and Phrasal Anchoring
This paper describes the system submission of our team Amazon to the shared task on Cross Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL).
Learning 3D Human Shape and Pose from Dense Body Parts
Reconstructing 3D human shape and pose from monocular images is challenging despite the promising results achieved by the most recent learning-based methods.
Ranked #76 on
3D Human Pose Estimation
on 3DPW
(MPJPE metric)
3D human pose and shape estimation
3D Human Reconstruction
+3
Informative Sample Mining Network for Multi-Domain Image-to-Image Translation
The performance of multi-domain image-to-image translation has been significantly improved by recent progress in deep generative models.
Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation
At last, we proposed a differentiable auto data augmentation method to further improve estimation accuracy.
OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer
A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data.
Reference-guided Face Component Editing
Face portrait editing has achieved great progress in recent years.
ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification
Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the sample size is small.
$P^2$ Net: Augmented Parallel-Pyramid Net for Attention Guided Pose Estimation
We propose a refinement stage for the pyramid features to further boost the accuracy of our network.
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification
Motivated by this, we propose a so-called \textit{Bi-Similarity Network} (\textit{BSNet}) that consists of a single embedding module and a bi-similarity module of two similarity measures.
ReMix: Towards Image-to-Image Translation with Limited Data
We interpolate training samples at the feature level and propose a novel content loss based on the perceptual relations among samples.
Database Workload Characterization with Query Plan Encoders
To address this workload characterization problem, we propose our query plan encoders that learn essential features and their correlations from query plans.
A Comparative Study on Schema-Guided Dialogue State Tracking
In this paper, we conduct in-depth comparative studies to understand the use of natural language description for schema in dialog state tracking.
FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains
In this work, we propose a novel two-stage framework named FaceInpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains.
Optimization of retina-like illumination patterns in ghost imaging
This proposed method is verified by simulations and experiments compared with conventional GI, retina-like GI and GI using patterns optimized by principal component analysis.
Complementary Fourier single-pixel imaging
The complementary nature of Fourier patterns based on a four-step phase-shift algorithm is combined with the complementary nature of a digital micromirror device.
Improving Subgraph Recognition with Variational Graph Information Bottleneck
Subgraph recognition aims at discovering a compressed substructure of a graph that is most informative to the graph property.
Styleverse: Towards Identity Stylization across Heterogeneous Domains
We propose a new challenging task namely IDentity Stylization (IDS) across heterogeneous domains.
Window Filtering Algorithm for Pulsed Light Coherent Combining of Low Repetition Frequency
The multi-dithering method has been well verified in phase locking of polarization coherent combination experiment.
Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution Neural Network
In recent years, with the development of computing resources and LiDAR, point cloud semantic segmentation has attracted many researchers.
Temporally and Spatially variant-resolution illumination patterns in computational ghost imaging
For the same number of measurements, the method using temporally variable-resolution illumination patterns has better imaging quality than CGI, but it is less robust to noise.
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches
We decomposed and evaluated a set of critical geometric concepts from the common adopted classification loss, and used them to design a visualization system to compare and highlight the impact of pruning on model performance and feature representation.
Modeling the Social Influence of COVID-19 via Personalized Propagation with Deep Learning
The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis.
DeepGen: Diverse Search Ad Generation and Real-Time Customization
In addition, our system creates a customized ad in real-time in response to the user's search query, therefore highlighting different aspects of the same product based on what the user is looking for.
OTSeq2Set: An Optimal Transport Enhanced Sequence-to-Set Model for Extreme Multi-label Text Classification
However, such models can't predict a relatively complete and variable-length label subset for each document, because they select positive labels relevant to the document by a fixed threshold or take top k labels in descending order of scores.
Multi Label Text Classification
Multi-Label Text Classification
+1
ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data
Then, we optimize the augmented samples by minimizing the norms of the data scores, i. e., the gradients of the log-density functions.
Vision Transformer with Super Token Sampling
STA decomposes vanilla global attention into multiplications of a sparse association map and a low-dimensional attention, leading to high efficiency in capturing global dependencies.
Rega-Net:Retina Gabor Attention for Deep Convolutional Neural Networks
Extensive research works demonstrate that the attention mechanism in convolutional neural networks (CNNs) effectively improves accuracy.
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification
Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study reveals that they in fact push for the opposite (i. e., lower inter-class variations and higher intra-class variations).
Dependency Dialogue Acts -- Annotation Scheme and Case Study
DDA combines and adapts features from existing dialogue annotation frameworks, and emphasizes the multi-relational response structure of dialogues in addition to the dialogue acts and rhetorical relations.
MODIFY: Model-driven Face Stylization without Style Images
Briefly, MODIFY first trains a generative model in the target domain and then translates a source input to the target domain via the provided style model.
AUTO: Adaptive Outlier Optimization for Online Test-Time OOD Detection
Out-of-distribution (OOD) detection is a crucial aspect of deploying machine learning models in open-world applications.
Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion
When decreasing the number of sampling steps (i. e., the number of line segments used to fit the path), the ease of fitting straight lines compared to curves allows us to generate higher quality samples from a random noise with fewer iterations.
Semi-Offline Reinforcement Learning for Optimized Text Generation
In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline.
Flexible uniform-sampling foveated Fourier single-pixel imaging
At a sampling ratio of 0. 0084 referring to HR FSI with 1024*768 pixels, experimentally, by UFFSI with 255*341 cells of 89% reduction in data redundancy, the ROI has a significantly better imaging quality to meet imaging needs.
Improved Dense Nested Attention Network Based on Transformer for Infrared Small Target Detection
To address this issue, we propose a novel method for detecting infrared small targets called improved dense nested attention network (IDNANet), which is based on the transformer architecture.
Exploring Straighter Trajectories of Flow Matching with Diffusion Guidance
First, we propose a coupling strategy to straighten trajectories, creating couplings between image and noise samples under diffusion model guidance.
Goal-Oriented Communication, Estimation, and Control over Bidirectional Wireless Links
This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control.
Band-Attention Modulated RetNet for Face Forgery Detection
The transformer networks are extensively utilized in face forgery detection due to their scalability across large datasets. Despite their success, transformers face challenges in balancing the capture of global context, which is crucial for unveiling forgery clues, with computational complexity. To mitigate this issue, we introduce Band-Attention modulated RetNet (BAR-Net), a lightweight network designed to efficiently process extensive visual contexts while avoiding catastrophic forgetting. Our approach empowers the target token to perceive global information by assigning differential attention levels to tokens at varying distances.
An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding
The lack of high-quality datasets and efficient neural geometric solvers impedes the development of automatic geometric problems solving.
