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Found 110 papers, 32 papers with code
The State of Aerial Surveillance: A Survey
The rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and covert observation capabilities.
LMR-CBT: Learning Modality-fused Representations with CB-Transformer for Multimodal Emotion Recognition from Unaligned Multimodal Sequences
Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion recognition.
Achieving an Accurate Random Process Model for PV Power using Cheap Data: Leveraging the SDE and Public Weather Reports
To fill this gap, this article finds that an accurate SDE model for PV power can be constructed by only using the cheap data from low-resolution public weather reports.
Face Presentation Attack Detection using Taskonomy Feature
The robustness and generalization ability of Presentation Attack Detection (PAD) methods is critical to ensure the security of Face Recognition Systems (FRSs).
Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising
Besides, our model is simpler, lighter and, more efficient and has achieved a 74. 76% reduction in time-consuming compared with the state-of-the-art multiple model based method.
Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks
In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the novel neural networks.
Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image
With complementary supervision from both 3D detection and reconstruction, one enables the 3D voxel features to be geometry and context preserving, benefiting both tasks. The effectiveness of our approach is demonstrated through 3D detection and reconstruction in single object and multiple object scenarios.
A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition
Firstly, we perform representation learning for audio and video modalities to obtain the semantic features of the two modalities by efficient ResNeXt and 1D CNN, respectively.
EvoGAN: An Evolutionary Computation Assisted GAN
Quantitative and Qualitative results are presented on several compound expressions, and the experimental results demonstrate the feasibility and the potential of EvoGAN.
Fingerprints of Super Resolution Networks
Compared to previously studied models, SISR networks are a uniquely challenging class of image generation model from which to extract and analyze fingerprints, as they can often generate images that closely match the corresponding ground truth and thus likely leave little flexibility to embed signatures.
Attacking Perceptual Similarity Metrics
Perceptual similarity metrics have progressively become more correlated with human judgments on perceptual similarity; however, despite recent advances, the addition of an imperceptible distortion can still compromise these metrics.
Approaching the Transient Stability Boundary of a Power System: Theory and Applications
Estimating the stability boundary is a fundamental and challenging problem in transient stability studies.
Manifold-preserved GANs
Generative Adversarial Networks (GANs) have been widely adopted in various fields.
Taming Self-Supervised Learning for Presentation Attack Detection: In-Image De-Folding and Out-of-Image De-Mixing
Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem.
Deep 3D Mask Volume for View Synthesis of Dynamic Scenes
Image view synthesis has seen great success in reconstructing photorealistic visuals, thanks to deep learning and various novel representations.
Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechanism
Softmax is widely used in neural networks for multiclass classification, gate structure and attention mechanisms.
StereoRel: Relational Triple Extraction from a Stereoscopic Perspective
Relational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest.
Local Reweighting for Adversarial Training
However, when tested on attacks different from the given attack simulated in training, the robustness may drop significantly (e. g., even worse than no reweighting).
Learning Bounds for Open-Set Learning
In this paper, we target a more challenging and realistic setting: open-set learning (OSL), where there exist test samples from the classes that are unseen during training.
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders.
Fast Monte Carlo Rendering via Multi-Resolution Sampling
Specifically, we formulate this fusion task as a super resolution problem that generates a high resolution rendering from a low resolution input (LRHS), assisted with the HRLS rendering.
Augmented Synchronization of Power Systems
Here, we formulate the empirical wisdom by the concept of augmented synchronization and aim to bridge such a theory-practice gap.
Probabilistic Margins for Instance Reweighting in Adversarial Training
Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights.
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
In realistic scenarios with very limited numbers of data samples, however, it can be challenging to identify a kernel powerful enough to distinguish complex distributions.
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
To this end, we propose a target orientated hypothesis adaptation network (TOHAN) to solve the FHA problem, where we generate highly-compatible unlabeled data (i. e., an intermediate domain) to help train a target-domain classifier.
KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation
However, in an open world, the unlabeled test images probably contain unknown categories and have different distributions from the labeled images.
Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning
Method: A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages.
SDNet: mutil-branch for single image deraining using swin
The former implements the basic rain pattern feature extraction, while the latter fuses different features to further extract and process the image features.
Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction
That is, for a 2D image of a generic object, we decompose it into latent representations of category, shape and albedo, lighting and camera projection matrix, decode the representations to segmented 3D shape and albedo respectively, and fuse these components to render an image well approximating the input image.
Deep Simultaneous Optimisation of Sampling and Reconstruction for Multi-contrast MRI
MRI images of the same subject in different contrasts contain shared information, such as the anatomical structure.
Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network
In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM acquisition.
Universal Undersampled MRI Reconstruction
Simply mixing images from multiple anatomies for training a single network does not lead to an ideal universal model due to the statistical shift among datasets of various anatomies, the need to retrain from scratch on all datasets with the addition of a new dataset, and the difficulty in dealing with imbalanced sampling when the new dataset is further of a smaller size.
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
Few-shot object detection has made substantial progressby representing novel class objects using the feature representation learned upon a set of base class objects.
Ranked #5 on
Few-Shot Object Detection
on MS-COCO (10-shot)
Group-wise Inhibition based Feature Regularization for Robust Classification
The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e. g. corrupted and adversarial samples).
Measurement of the absolute branching fractions for purely leptonic $D_s^+$ decays
Constraining our measurement to the Standard Model expectation of lepton universality ($R=9. 75$), we find the more precise results $\cal B(D_s^+\to \tau^+\nu_\tau) = (5. 22\pm0. 10\pm 0. 14)\times10^{-2}$ and $A_{\it CP}(\tau^\pm\nu_\tau) = (-0. 1\pm1. 9\pm1. 0)\%$.
High Energy Physics - Experiment
Cross section measurement of $e^+e^- \to p\bar{p}η$ and $e^+e^- \to p\bar{p}ω$ at center-of-mass energies between 3.773 GeV and 4.6 GeV
Based on $14. 7~\textrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected with the BESIII detector at the BEPCII collider at 17 different center-of-mass energies between $3. 7730~\textrm{GeV}$ and $4. 5995~\textrm{GeV}$, Born cross sections of the two processes $e^+e^- \to p\bar{p}\eta$ and $e^+e^- \to p\bar{p}\omega$ are measured for the first time.
High Energy Physics - Experiment
Demystifying Assumptions in Learning to Discover Novel Classes
In learning to discover novel classes (L2DNC), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes.
Robust Scheduling of Virtual Power Plant under Exogenous and Endogenous Uncertainties
Virtual power plant (VPP) provides a flexible solution to distributed energy resources integration by aggregating renewable generation units, conventional power plants, energy storages, and flexible demands.
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?
Experiments show that the proxy can effectively curb the increase of the combined risk when minimizing the source risk and distribution discrepancy.
Measurements of the center-of-mass energies of $e^{+}e^{-}$ collisions at BESIII
During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7. 5~fb$^{-1}$ of $e^+e^-$ collision data at center-of-mass energies ranging from 4. 13 to 4. 44 GeV.
High Energy Physics - Experiment
A State Representation Dueling Network for Deep Reinforcement Learning
In recent years there have been many successes in boosting the performance of Deep Q-Networks (DQN).
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation
Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template.
Search for the reaction $e^{+}e^{-} \rightarrow π^{+}π^{-} χ_{cJ}$ and a charmonium-like structure decaying to $χ_{cJ}π^{\pm}$ between 4.18 and 4.60 GeV
We search for the process $e^{+}e^{-}\rightarrow \pi ^{+}\pi ^{-} \chi_{cJ}$ ($J=0, 1, 2$) and for a charged charmonium-like state in the $\pi ^{\pm} \chi_{cJ}$ subsystem.
High Energy Physics - Experiment
Synchronization Instability of Inverter-Based Generation During Asymmetrical Grid Faults
In contrast, both positive- and negative-sequence synchronizations should be of concern for inverter-based generation (IBG) under asymmetrical faults.
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
The goal of this paper is to learn dense 3D shape correspondence for topology-varying objects in an unsupervised manner.
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks
However, it has been shown that the MMD test is unaware of adversarial attacks -- the MMD test failed to detect the discrepancy between natural and adversarial data.
Learned Dual-View Reflection Removal
Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users.
Learning from a Complementary-label Source Domain: Theory and Algorithms
We consider two cases of this setting, one is that the source domain only contains complementary-label data (completely complementary unsupervised domain adaptation, CC-UDA), and the other is that the source domain has plenty of complementary-label data and a small amount of true-label data (partly complementary unsupervised domain adaptation, PC-UDA).
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation
To mitigate this problem, we consider a novel problem setting where the classifier for the target domain has to be trained with complementary-label data from the source domain and unlabeled data from the target domain named budget-friendly UDA (BFUDA).
Model independent determination of the spin of the $Ω^{-}$ and its polarization alignment in $ψ(3686)\rightarrowΩ^{-}\barΩ^{+}$
We present an analysis of the process $\psi(3686) \to \Omega^- \bar{\Omega}^+$ ($\Omega^-\to K^-\Lambda$, $\bar{\Omega}^+\to K^+\bar{\Lambda}$, $\Lambda\to p\pi^-$, $\bar{\Lambda}\to \bar{p}\pi^+$) based on a data set of $448\times 10^6$ $\psi(3686)$ decays collected with the BESIII detector at the BEPCII electron-positron collider.
High Energy Physics - Experiment
Global optimization using mixed integer quadratic programming on non-convex two-way interaction truncated linear multivariate adaptive regression splines
The use of MARS maintains the flexibility of modeling within TITL-MARS-OPT while also taking advantage of the linear modeling structure of MARS to enable global optimality.
Optimization and Control
Domain Contrast for Domain Adaptive Object Detection
We present Domain Contrast (DC), a simple yet effective approach inspired by contrastive learning for training domain adaptive detectors.
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation
To achieve this aim, a previous study has proven an upper bound of the target-domain risk, and the open set difference, as an important term in the upper bound, is used to measure the risk on unknown target data.
Assessing the Impact of COVID-19 on the Objective and Analysis of Oncology Clinical Trials -- Application of the Estimand Framework
We identify key intercurrent events that may occur due to COVID-19 in oncology clinical trials with a focus on time-to-event endpoints and discuss considerations pertaining to the other estimand attributes introduced in the ICH E9 addendum.
Event Arguments Extraction via Dilate Gated Convolutional Neural Network with Enhanced Local Features
However, the F-Score of event arguments extraction is much lower than that of event trigger extraction, i. e. in the most recent work, event trigger extraction achieves 80. 7%, while event arguments extraction achieves only 58%.
Inter-sequence Enhanced Framework for Personalized Sequential Recommendation
Then, based on the inter-sequence correlation encoder, we build GRU network and attention network in the intra-sequence correlation encoder to model the item sequential correlation within each individual sequence and temporal dynamics for predicting users' preferences over candidate items.
xQSM-Quantitative Susceptibility Mapping with Octave Convolutional Neural Networks
Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications.
Image and Video Processing
Deep Homography Estimation for Dynamic Scenes
We then develop a multi-scale neural network and show that when properly trained using our new dataset, this neural network can already handle dynamic scenes to some extent.
Local Facial Makeup Transfer via Disentangled Representation
In this paper, we propose a novel unified adversarial disentangling network to further decompose face images into four independent components, i. e., personal identity, lips makeup style, eyes makeup style and face makeup style.
Softmax Splatting for Video Frame Interpolation
In contrast, how to perform forward warping has seen less attention, partly due to additional challenges such as resolving the conflict of mapping multiple pixels to the same target location in a differentiable way.
Ranked #1 on
Video Frame Interpolation
on Middlebury
Segmentation-based Method combined with Dynamic Programming for Brain Midline Delineation
The midline related pathological image features are crucial for evaluating the severity of brain compression caused by stroke or traumatic brain injury (TBI).
Learning Deep Kernels for Non-Parametric Two-Sample Tests
We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution.
Ranked #1 on
Two-sample testing
on HIGGS Data Set
A Zero-Shot based Fingerprint Presentation Attack Detection System
Therefore, we propose a novel Zero-Shot Presentation Attack Detection Model to guarantee the generalization of the PAD model.
Frosting Weights for Better Continual Training
Training a neural network model can be a lifelong learning process and is a computationally intensive one.
On the Detection of Digital Face Manipulation
Instead of simply using multi-task learning to simultaneously detect manipulated images and predict the manipulated mask (regions), we propose to utilize an attention mechanism to process and improve the feature maps for the classification task.
Wildly Unsupervised Domain Adaptation and Its Powerful and Efficient Solution
Hence, we consider a new, more realistic and more challenging problem setting, where classifiers have to be trained with noisy labeled data from SD and unlabeled data from TD---we name it wildly UDA (WUDA).
Unsupervised Domain Adaptation
Wildly Unsupervised Domain Adaptation
Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation
Our method employs two encoder networks to extract essential information for matting.
Ranked #5 on
Image Matting
on Composition-1K
3D Ken Burns Effect from a Single Image
According to this depth estimate, our framework then maps the input image to a point cloud and synthesizes the resulting video frames by rendering the point cloud from the corresponding camera positions.
Ranked #1 on
Depth Estimation
on NYU-Depth V2
On Learning Disentangled Representations for Gait Recognition
The LSTM integrates pose features over time as a dynamic gait feature while canonical features are averaged as a static gait feature.
Meta Learning with Relational Information for Short Sequences
This paper proposes a new meta-learning method -- named HARMLESS (HAwkes Relational Meta LEarning method for Short Sequences) for learning heterogeneous point process models from short event sequence data along with a relational network.
MemeFaceGenerator: Adversarial Synthesis of Chinese Meme-face from Natural Sentences
Chinese meme-face is a special kind of internet subculture widely spread in Chinese Social Community Networks.
Open Set Domain Adaptation: Theoretical Bound and Algorithm
The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain -- the basic strategy being to mitigate the effects of discrepancies between the two distributions.
Ranked #9 on
Domain Adaptation
on Office-Home
Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior
Traditional EEG/MEG Source Imaging (ESI) methods usually assume that either source activity at different time points is unrelated, or that similar spatiotemporal patterns exist across an entire study period.