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Found 100 papers, 29 papers with code
Plan-CVAE: A Planning-based Conditional Variational Autoencoder for Story Generation
Story generation is a challenging task of automatically creating natural languages to describe a sequence of events, which requires outputting text with not only a consistent topic but also novel wordings.
SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events
Firstly, we propose an attention-based interactive fusion (AIF) module that applies spatial priors of events and frames as the initial masks and learns the consensus regions to guide the inter-modal feature fusion.
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event Camera
The ability to detect objects in all lighting (i. e., normal-, over-, and under-exposed) conditions is crucial for real-world applications, such as self-driving. Traditional RGB-based detectors often fail under such varying lighting conditions. Therefore, recent works utilize novel event cameras to supplement or guide the RGB modality; however, these methods typically adopt asymmetric network structures that rely predominantly on the RGB modality, resulting in limited robustness for all-day detection.
IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency
To address this challenge and propose a solution to efficiently manage model variants in inference pipelines, we present IPA, an online deep-learning Inference Pipeline Adaptation system that efficiently leverages model variants for each deep learning task.
OmniZoomer: Learning to Move and Zoom in on Sphere at High-Resolution
The M\"obius transformation is typically employed to further provide the opportunity for movement and zoom on ODIs, but applying it to the image level often results in blurry effect and aliasing problem.
Look at the Neighbor: Distortion-aware Unsupervised Domain Adaptation for Panoramic Semantic Segmentation
The aim is to tackle the domain gaps caused by the style disparities and distortion problem from the non-uniformly distributed pixels of equirectangular projection (ERP).
E-CLIP: Towards Label-efficient Event-based Open-world Understanding by CLIP
To this end, we first introduce a novel event encoder that subtly models the temporal information from events and meanwhile generates event prompts to promote the modality bridging.
Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples
In this paper, we first establish a comprehensive, and rigorous point cloud adversarial robustness benchmark to evaluate adversarial robustness, which can provide a detailed understanding of the effects of the defense and attack methods.
Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF
The explicit neural radiance field (NeRF) has gained considerable interest for its efficient training and fast inference capabilities, making it a promising direction such as virtual reality and gaming.
A Good Student is Cooperative and Reliable: CNN-Transformer Collaborative Learning for Semantic Segmentation
In this paper, we strive to answer the question "how to collaboratively learn convolutional neural network (CNN)-based and vision transformer (ViT)-based models by selecting and exchanging the reliable knowledge between them for semantic segmentation?"
Coherent Compensation based ISAC Signal Processing for Long-range Sensing
Integrated sensing and communication (ISAC) will greatly enhance the efficiency of physical resource utilization.
Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation
we propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime RGBT semantic segmentation without access to the source (daytime) data during adaptation.
InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphs
Inspired by the philosophy of ``think-like-a-vertex", a GAS-like (Gather-Apply-Scatter) schema is proposed to describe the computation paradigm and data flow of GNN inference.
Self-supervised Learning of Event-guided Video Frame Interpolation for Rolling Shutter Frames
Although events possess high temporal resolution, beneficial for video frame interpolation (VFI), a hurdle in tackling this task is the lack of paired GS frames.
When SAM Meets Sonar Images
Notably, there is a lack of research on the application of SAM to sonar imaging.
FMapping: Factorized Efficient Neural Field Mapping for Real-Time Dense RGB SLAM
We then propose the sliding window sampler to reduce uncertainty by incorporating coherent geometric cues from observed frames during map initialization to enhance convergence.
Towards Language-guided Interactive 3D Generation: LLMs as Layout Interpreter with Generative Feedback
Generating and editing a 3D scene guided by natural language poses a challenge, primarily due to the complexity of specifying the positional relations and volumetric changes within the 3D space.
Learning INR for Event-guided Rolling Shutter Frame Correction, Deblur, and Interpolation
Images captured by rolling shutter (RS) cameras under fast camera motion often contain obvious image distortions and blur, which can be modeled as a row-wise combination of a sequence of global shutter (GS) frames within the exposure time naturally, recovering high-frame-rate GS sharp frames from an RS blur image needs to simultaneously consider RS correction, deblur, and frame interpolation Taking this task is nontrivial, and to our knowledge, no feasible solutions exist by far.
SPP-CNN: An Efficient Framework for Network Robustness Prediction
This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks.
Deep Reinforcement Learning Based Resource Allocation for Cloud Native Wireless Network
Cloud native technology has revolutionized 5G beyond and 6G communication networks, offering unprecedented levels of operational automation, flexibility, and adaptability.
360$^\circ$ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer
Our key idea is to transfer the scene structural knowledge from the readily available HR image modality and the corresponding LR depth maps to achieve the goal of HR depth estimation without extra inference cost.
Towards Open-Scenario Semi-supervised Medical Image Classification
Therefore, in this paper, we proposed a unified framework to leverage these unseen unlabeled data for open-scenario semi-supervised medical image classification.
Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation
Moreover, adversarial intra-projection training is proposed to reduce the inherent gap, between the features of the pinhole images and those of the ERP and TP images, respectively.
Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution
In addition, we collect a real-world dataset with spatially aligned events and RGB frames.
CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout
To address it, we propose a novel framework, dubbed CompoNeRF, to explicitly incorporates an editable 3D scene layout to provide effective guidance at the object (i. e., local) and scene (i. e., global) levels.
Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective
We solve this problem from a game theory's perspective with the proposed model dubbed as PMTrans, which bridges source and target domains with an intermediate domain.
Ranked #1 on
Domain Adaptation
on Office-Home
Variantional autoencoder with decremental information bottleneck for disentanglement
One major challenge of disentanglement learning with variational autoencoders is the trade-off between disentanglement and reconstruction fidelity.
HRDFuse: Monocular 360°Depth Estimation by Collaboratively Learning Holistic-with-Regional Depth Distributions
Depth estimation from a monocular 360{\deg} image is a burgeoning problem owing to its holistic sensing of a scene.
Reproduction number of SARS-CoV-2 Omicron variants, China, December 2022-January 2023
China adjusted the zero-COVID strategy in late 2022, triggering an unprecedented Omicron wave.
Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks
Being capable of capturing information in challenging visual conditions, event cameras have the potential to overcome the limitations of frame-based cameras in the computer vision and robotics community.
A Fast Bootstrap Algorithm for Causal Inference with Large Data
The bag of little bootstraps has been proposed in non-causal settings for large data but has not yet been applied to evaluate the properties of estimators of causal effects.
FedEBA+: Towards Fair and Effective Federated Learning via Entropy-Based Model
Ensuring fairness is a crucial aspect of Federated Learning (FL), which enables the model to perform consistently across all clients.
Multi-scale multi-modal micro-expression recognition algorithm based on transformer
A micro-expression is a spontaneous unconscious facial muscle movement that can reveal the true emotions people attempt to hide.
HRDFuse: Monocular 360deg Depth Estimation by Collaboratively Learning Holistic-With-Regional Depth Distributions
Depth estimation from a monocular 360 image is a burgeoning problem owing to its holistic sensing of a scene.
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection
This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset.
SEPT: Towards Scalable and Efficient Visual Pre-Training
To address these issues, we build a task-specific self-supervised pre-training framework from a data selection perspective based on a simple hypothesis that pre-training on the unlabeled samples with similar distribution to the target task can bring substantial performance gains.
Autoregressive GNN-ODE GRU Model for Network Dynamics
In this paper, we propose an Autoregressive GNN-ODE GRU Model (AGOG) to learn and capture the continuous network dynamics and realize predictions of node states at an arbitrary time in a data-driven manner.
Reconstruction of compressed spectral imaging based on global structure and spectral correlation
To take full exploration of the constraints between spectra, the coefficients corresponding to the convolution kernel are constrained by the L_(2, 1)norm to improve spectral accuracy.
Synthetic Data Supervised Salient Object Detection
In this paper, we propose a novel yet effective method for SOD, coined SODGAN, which can generate infinite high-quality image-mask pairs requiring only a few labeled data, and these synthesized pairs can replace the human-labeled DUTS-TR to train any off-the-shelf SOD model.
3D Matting: A Benchmark Study on Soft Segmentation Method for Pulmonary Nodules Applied in Computed Tomography
In this work, we introduce the image matting into the 3D scenes and use the alpha matte, i. e., a soft mask, to describe lesions in a 3D medical image.
A Comprehensive Survey on Trustworthy Recommender Systems
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites.
3D Matting: A Soft Segmentation Method Applied in Computed Tomography
It can be caused by many factors, such as the imaging properties, pathological anatomy, and the weak representation of the binary masks, which brings challenges to accurate 3D segmentation.
Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students
To this end, we propose class-aware feature consistency distillation (CFCD) that first leverages the outputs of each student as the pseudo labels and generates class-aware feature (CF) maps.
Joint Attention-Driven Domain Fusion and Noise-Tolerant Learning for Multi-Source Domain Adaptation
As a study on the efficient usage of data, Multi-source Unsupervised Domain Adaptation transfers knowledge from multiple source domains with labeled data to an unlabeled target domain.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
Efficient Video Deblurring Guided by Motion Magnitude
Video deblurring is a highly under-constrained problem due to the spatially and temporally varying blur.
Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization
Existing unsupervised domain adaptation methods based on adversarial learning have achieved good performance in several medical imaging tasks.
Priors in Deep Image Restoration and Enhancement: A Survey
Image restoration and enhancement is a process of improving the image quality by removing degradations, such as noise, blur, and resolution degradation.
DELTA: Diverse Client Sampling for Fasting Federated Learning
However, an inadequate client sampling scheme can lead to the selection of unrepresentative subsets, resulting in significant variance in model updates and slowed convergence.
Deep Learning for Omnidirectional Vision: A Survey and New Perspectives
Omnidirectional image (ODI) data is captured with a 360x180 field-of-view, which is much wider than the pinhole cameras and contains richer spatial information than the conventional planar images.
Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification
Finally, the distance between vehicle appearances is presented by the discriminative region features and multi-view features together.
Flexible Sampling for Long-tailed Skin Lesion Classification
To address this, in this paper, we propose a curriculum learning-based framework called Flexible Sampling for the long-tailed skin lesion classification task.
Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams
Combined with the designed vehicle planning and control algorithm, the unsignalized intersection management strategy consists of two parts: the heuristic priority queues based right of way allocation (HPQ) algorithm, and the vehicle planning and control algorithm.
Three-dimensional Microstructural Image Synthesis from 2D Backscattered Electron Image of Cement Paste
In experiments, the method is verified on actual 3D Micro-CT images and 2D BSE images.
Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data
Current human assessment of thyroid nodule malignancy is prone to errors and may not guarantee an accurate preoperative diagnosis.
A Learning Convolutional Neural Network Approach for Network Robustness Prediction
Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors; 2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability; 3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations; 4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures.
Controllability of Multilayer Networked Sampled-data Systems
This paper explores the state controllability of multilayer networked sampled-data systems with inter-layer couplings, where zero-order holders (ZOHs) are on the control and transmission channels.
Controllability of Networked Sampled-data Systems
The controllability of networked sampled-data systems with zero-order holders on the control and transmission channels is explored, where single- and multi-rate sampling patterns are considered, respectively.
SphereSR: 360deg Image Super-Resolution With Arbitrary Projection via Continuous Spherical Image Representation
In this paper, we propose SphereSR, a novel framework to generate a continuous spherical image representation from an LR 360deg image, with the goal of predicting the RGB values at given spherical coordinates for superresolution with an arbitrary 360deg image projection.
SphereSR: 360° Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation
We then propose a spherical local implicit image function (SLIIF) to predict RGB values at the spherical coordinates.
Event-guided Deblurring of Unknown Exposure Time Videos
To this end, we first derive a new formulation for event-guided motion deblurring by considering the exposure and readout time in the video frame acquisition process.
BIPS: Bi-modal Indoor Panorama Synthesis via Residual Depth-aided Adversarial Learning
Providing omnidirectional depth along with RGB information is important for numerous applications, eg, VR/AR.
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation
To enable KD across the unpaired modalities, we first propose a bidirectional modality reconstruction (BMR) module to bridge both modalities and simultaneously exploit them to distill knowledge via the crafted pairs, causing no extra computation in the inference.
Hierarchical Knowledge Guided Learning for Real-world Retinal Diseases Recognition
From a modeling perspective, most deep learning models trained on these datasets may lack the ability to generalize to rare diseases where only a few available samples are presented for training.
Deep Learning for HDR Imaging: State-of-the-Art and Future Trends
High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision.
SiamEvent: Event-based Object Tracking via Edge-aware Similarity Learning with Siamese Networks
Importantly, to find the part having the most similar edge structure of target, we propose to correlate the embedded events at two timestamps to compute the target edge similarity.
Investigating and Modeling the Dynamics of Long Ties
Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks.
Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation
Event cameras are novel sensors that perceive the per-pixel intensity changes and output asynchronous event streams with high dynamic range and less motion blur.
DQN Control Solution for KDD Cup 2021 City Brain Challenge
From our perspective, the major challenge of this competition is how to extend the classical DQN framework to traffic signals control in real-world complex road network and traffic flow situation.
Unsupervised Domain Adaptation for Retinal Vessel Segmentation with Adversarial Learning and Transfer Normalization
In this work, we explore unsupervised domain adaptation in retinal vessel segmentation by using entropy-based adversarial learning and transfer normalization layer to train a segmentation network, which generalizes well across domains and requires no annotation of the target domain.
Medical Matting: A New Perspective on Medical Segmentation with Uncertainty
None of them investigate the influence of the ambiguous nature of the lesion itself. Inspired by image matting, this paper introduces alpha matte as a soft mask to represent uncertain areas in medical scenes and accordingly puts forward a new uncertainty quantification method to fill the gap of uncertainty research for lesion structure.
Multi-Granularity Network with Modal Attention for Dense Affective Understanding
Video affective understanding, which aims to predict the evoked expressions by the video content, is desired for video creation and recommendation.
Deconvolutional Density Network: Modeling Free-Form Conditional Distributions
Conditional density estimation (CDE) is the task of estimating the probability of an event conditioned on some inputs.
Unsupervised Dynamic Routing Via Competition Over Network Loss
Using the self-organizing map over the competition of the loss of individual neural column, we route the input to the most appropriate modules dynamically, by doing this we separate the input functional space into different sub spaces which are represented by each individual neural column.
Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition
For example, there are estimated more than 40 different kinds of retinal diseases with variable morbidity, however with more than 30+ conditions are very rare from the global patient cohorts, which results in a typical long-tailed learning problem for deep learning-based screening models.
Surrogate-assisted cooperative signal optimization for large-scale traffic networks
The decomposition operation significantly narrows the search space of the whole traffic network, and the surrogate-assisted optimizer greatly lowers the computational burden by reducing the number of expensive traffic simulations.
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation
In this paper, we systematically discuss and define the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from wrong diagnosis record.
STS-GAN: Can We Synthesize Solid Texture with High Fidelity from Arbitrary 2D Exemplar?
Solid texture synthesis (STS), an effective way to extend a 2D exemplar to a 3D solid volume, exhibits advantages in computational photography.
DEFT: Distilling Entangled Factors by Preventing Information Diffusion
Disentanglement is a highly desirable property of representation owing to its similarity to human understanding and reasoning.
OPT-GAN: A Broad-Spectrum Global Optimizer for Black-box Problems by Learning Distribution
Black-box optimization (BBO) algorithms are concerned with finding the best solutions for problems with missing analytical details.
An embedded multichannel sound acquisition system for drone audition
Microphone array techniques can improve the acoustic sensing performance on drones, compared to the use of a single microphone.
Locally symmetric lattices for storage ring light sources
Two kinds of locally symmetric lattices are designed for a 2. 2 GeV diffraction-limited storage ring to demonstrate the lattice concept.
Accelerator Physics
CDNet: Centripetal Direction Network for Nuclear Instance Segmentation
Specifically, we define the centripetal direction feature as a class of adjacent directions pointing to the nuclear center to represent the spatial relationship between pixels within the nucleus.
Operator as a Service: Stateful Serverless Complex Event Processing
In this paper, we propose CEPLESS, a scalable data management system that decouples the specification from the runtime system by building on the principles of serverless computing.
Networking and Internet Architecture Distributed, Parallel, and Cluster Computing
Disentangling Action Sequences: Discovering Correlated Samples
Disentanglement is a highly desirable property of representation due to its similarity with human's understanding and reasoning.
Go viral or go broadcast? Characterizing the virality and growth of cascades
Quantifying the virality of cascades is an important question across disciplines such as the transmission of disease, the spread of information and the diffusion of innovations.
Physics and Society Social and Information Networks
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features
These techniques reject the features by considering term presence difference for SentiTPC and ratio of the distribution distinction for SentiTPR.
A Novel Graphic Bending Transformation on Benchmark
Classical benchmark problems utilize multiple transformation techniques to increase optimization difficulty, e. g., shift for anti centering effect and rotation for anti dimension sensitivity.
Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks
To achieve faster speeds and to handle the problems caused by the lack of data, knowledge distillation (KD) has been proposed to transfer information learned from one model to another.
EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning
While each phase is mainly for one of the three tasks, the networks in earlier phases are fine-tuned by respective loss functions in an end-to-end manner.
DSSLP: A Distributed Framework for Semi-supervised Link Prediction
Instead of training model on the whole graph, DSSLP is proposed to train on the \emph{$k$-hops neighborhood} of nodes in a mini-batch setting, which helps reduce the scale of the input graph and distribute the training procedure.
Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations
However, most previous works have focused on image classification tasks, and it has never been studied regarding adversarial perturbations on Image-to-image (Im2Im) translation tasks, showing great success in handling paired and/or unpaired mapping problems in the field of autonomous driving and robotics.
Predicting Network Controllability Robustness: A Convolutional Neural Network Approach
Under the new framework, a fairly large number of training data generated by simulations are used to train a convolutional neural network for predicting the controllability robustness according to the input network-adjacency matrices, without performing conventional attack simulations.
Improving Neural Network Classifier using Gradient-based Floating Centroid Method
In this study, a gradient-based floating centroid (GDFC) method is introduced to address the fixed centroid problem for the neural network classifiers optimized by gradient-based methods.
A Novel Neural Network-Based Symbolic Regression Method: Neuro-Encoded Expression Programming
Neuro-encoded expression programming(NEEP) that aims to offer a novel continuous representation of combinatorial encoding for genetic programming methods is proposed in this paper.
Event-based High Dynamic Range Image and Very High Frame Rate Video Generation using Conditional Generative Adversarial Networks
Event cameras have a lot of advantages over traditional cameras, such as low latency, high temporal resolution, and high dynamic range.
Adversarial Attack on Graph Structured Data
Deep learning on graph structures has shown exciting results in various applications.
Boosting Cooperative Coevolution for Large Scale Optimization with a Fine-Grained Computation Resource Allocation Strategy
Cooperative coevolution (CC) has shown great potential in solving large scale optimization problems (LSOPs).
Accelerating flux balance calculations in genome-scale metabolic models by localizing the application of loopless constraints
Background: Genome-scale metabolic network models and constraint-based modeling techniques have become important tools for analyzing cellular metabolism.
Molecular Networks
Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0
This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios.
How does Dictionary Size Influence Performance of Vietnamese Word Segmentation?
Vietnamese word segmentation (VWS) is a challenging basic issue for natural language processing.
