Lingjia Deng and Janyce Wiebe. Grounding to Multiple Modalities in Vision-and-Language Navigation, Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments, Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation, The Regretful Navigation Agent for Vision-and-Language Navigation, Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation, Multi-modal Discriminative Model for Vision-and-Language Navigation, Self-Monitoring Navigation Agent via Auxiliary Progress Estimation, From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following, Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos, Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout, Attention Based Natural Language Grounding by Navigating Virtual Environment, Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction, Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments, Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation, Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting, Multimodal Transformer for Multimodal Machine Translation, Neural Machine Translation with Universal Visual Representation, Visual Agreement Regularized Training for Multi-Modal Machine Translation, VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research, Latent Variable Model for Multi-modal Translation, Distilling Translations with Visual Awareness, Probing the Need for Visual Context in Multimodal Machine Translation, Emergent Translation in Multi-Agent Communication, Zero-Resource Neural Machine Translation with Multi-Agent Communication Game, Learning Translations via Images with a Massively Multilingual Image Dataset, A Visual Attention Grounding Neural Model for Multimodal Machine Translation, Adversarial Evaluation of Multimodal Machine Translation, Doubly-Attentive Decoder for Multi-modal Neural Machine Translation, An empirical study on the effectiveness of images in Multimodal Neural Machine Translation, Incorporating Global Visual Features into Attention-based Neural Machine Translation, Multimodal Pivots for Image Caption Translation, Multi30K: Multilingual English-German Image Descriptions. Retrieved from https://arxiv:1507.06228. : survey | Cri. Retrieved from https://arXiv:1711.07341. Machine learning and data mining techniques have been used in numerous real-world applications. Text-to-Image Coreference, CVPR 2014, Grounded Language Learning from Video Described with Sentences, ACL 2013, Grounded Compositional Semantics for Finding and Describing Images with Sentences, TACL 2013, ALFWorld: Aligning Text and Embodied Environments for Interactive Learning, ICLR 2021 [code], Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation, ICRA 2021, [code], [video], [project page], Improving Vision-and-Language Navigation with Image-Text Pairs from the Web, ECCV 2020, Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training, CVPR 2020 [code], VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering, BMVC 2019 [code], Vision-and-Dialog Navigation, arXiv 2019 [code], Hierarchical Decision Making by Generating and Following Natural Language Instructions, arXiv 2019 [code], Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation, ACL 2019, Are You Looking? 2017. Wikiqa: A challenge dataset for open-domain question answering. 1993. . Gary Marcus. In Proceedings of the 33rd International Conference on Machine Learning (ICML16). Text-to-Image Coreference, Grounded Language Learning from Video Described with Sentences, Grounded Compositional Semantics for Finding and Describing Images with Sentences, ALFWorld: Aligning Text and Embodied Environments for Interactive Learning, Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation, Improving Vision-and-Language Navigation with Image-Text Pairs from the Web, Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training, VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering, Hierarchical Decision Making by Generating and Following Natural Language Instructions, Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation, Are You Looking? Jaeyoung Kim, Sion Jang, Eunjeong Park, and Sungchul Choi. Recurrent neural network-based sentence encoder with gated attention for natural language inference. Self-supervised Learning. Typical sketch of the 1D-CNN model that can be used for epileptic seizure detection. Recurrent neural network for text classification with multi-task learning. Retrieved from https://arxiv:1409.1556. 2016. However, in some real-world machine learning RaviPrakash H., Korostenskaja M., Castillo E.M., Lee K.H., Salinas C.M., Baumgartner J., Anwar S.M., Spampinato C., Bagci U. Turner J.T., Page A., Mohsenin T., Oates T. Deep belief networks used on high resolution multichannel electroencephalography data for seizure detection. 2019. PASS: An An ImageNet replacement for Another drawback of RNN is the vanishing gradient problem [30,31,32,33]. 2011. AE networks are most commonly used for feature extraction or dimensionality reduction in the brain signal processing. Minh-Thang Luong, Hieu Pham, and Christopher D Manning. In addition, meta information about 59 seizures and information related to the position of electrodes are presented. Conceptually situated between supervised and unsupervised learning, it permits harnessing the large amounts of unlabelled data available in many use cases in combination with typically smaller sets of Others Sci. Summary of related works done using RNNs. Moreover, people with epileptic seizures sometimes suffer emotional distress due to embarrassment and lack of appropriate social status. 3.Theory and Survey () Here are some articles on transfer learning theory and survey. Yao X., Cheng Q., Zhang G.Q. In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey. Are you sure you want to create this branch? Ravi Prakash et al. 182186. will also be available for a limited time. Learn more. Jonas Mueller and Aditya Thyagarajan. [n.d.]. Glove: Global vectors for word representation. 2007. 1821 July 2018; pp. However, these data form a significant part of the information in the world, compelling the need for DL-based schemes to process these types of data. Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii. FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence ; Training data-efficient image transformers & distillation through attention ; Advanced Topics; Self-Supervised Learning. [102] practiced ten different and independently ameliorated RNN (IndRNN) architectures and achieved the best accuracy using Dense IndRNN with attention (DIndRNN) with 31 layers. A convolutional neural network for modelling sentences. These two gates decide which information is necessary to pass to the output. 2017. In one experiment, Chen et al. 1721 February 2019; pp. Retrieved from https://arxiv:1410.3916. Quora. Survey () 2022 Transfer Learning for Future Wireless Networks: A Comprehensive Survey; 2022 A Review of Deep Transfer Learning and Recent Advancements; 2022 Transferability in Deep Learning: A Survey, from Mingsheng Long in THU. However, the success rate of ECoG-FM is low as compared with electro-cortical stimulation mapping (ESM). Attention-based LSTM network for cross-lingual sentiment classification. On-demand learning for deep image restoration. The best structure should be chosen carefully based on the dataset and problem characteristics, such as the need for real-time detection or minimum acceptable accuracy or even the use of pre-trained models. Syst. Therefore, handheld applications, mobile or wearable devices, may be equipped with such models, and cloud servers will perform the computations; by taking benefit from predictive models, these devices can be used to avert patients in a timely manner. Proc. Hence, real-time diagnosis of epileptic seizures is still challenging. Roberta: A robustly optimized bert pretraining approach. Bouaziz B., Chaari L., Batatia H., Quintero-Rincn A. In the proposed method, during the preprocessing, the input signals are split into time windows and spectrogram are obtained from them. Self-Supervised Semi-Supervised Learning Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer. 2019. Single-Image-Blind-Motion-Deblurring (non-DL), Single-Image-Blind-Motion-Deblurring (DL), Defocus Deblurring and Potential Datasets, Removing camera shake from a single photograph, Single image motion deblurring using transparency, Psf estimation using sharp edge prediction, High-quality motion deblurring from a single image, Image deblurring and denoising using color priors, Efficient filter flow for space-variant multiframe blind deconvolution, Denoising vs. deblurring: HDR imaging techniques using moving cameras, Single image deblurring using motion density functions, Two-phase kernel estimation for robust motion deblurring, Space-variant single-image blind deconvolution for removing camera shake, Blind deconvolution using a normalized sparsity measure, Blur kernel estimation using the radon transform, Exploring aligned complementary image pair for blind motion deblurring, The non-parametric sub-pixel local point spread function estimation is a well posed problem, Blur-kernel estimation from spectral irregularities, Framelet-based Blind Motion deblurring from a single Image, Unnatural L0 sparse representation for natural image deblurring, Handling noise in single image deblurring using directional filters, Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty, Edge-based blur kernel estimation using patch priors, Deblurring Text Images via L0 -Regularized Intensity and Gradient Prior, Segmentation-Free Dynamic Scene Deblurring, Deblurring Low-light Images with Light Streaks, Joint depth estimation and camera shake removal from single blurry image, Hybrid Image Deblurring by Fusing Edge and Power Spectrum Information, Blind deblurring using internal patch recurrence, Burst Deblurring: Removing Camera Shake Through Fourier Burst Accumulation, Kernel Fusion for Better Image Deblurring, Blind image deblurring using dark channel prior, Robust Kernel Estimation with Outliers Handling for Image Deblurring, Blind image deconvolution by automatic gradient activation, Image deblurring via extreme channels prior, From local to global: Edge profiles to camera motion in blurred images, Project page & Results-on-benchmark-datasets, Blind Image Deblurring with Outlier Handling, Self-paced Kernel Estimation for Robust Blind Image Deblurring, Convergence Analysis of MAP based Blur Kernel Estimation, Deblurring Natural Image Using Super-Gaussian Fields, Blind Image Deblurring With Local Maximum Gradient Prior, Phase-Only Image Based Kernel Estimation for Single Image Blind Deblurring, A Variational EM Framework With Adaptive Edge Selection for Blind Motion Deblurring, Graph-Based Blind Image Deblurring From a Single Photograph, Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior, OID: Outlier Identifying and Discarding in Blind Image Deblurring, Enhanced Sparse Model for Blind Deblurring, Polyblur: Removing mild blur by polynomial reblurring, Fast blind deconvolution using a deeper sparse patch-wise maximum gradient prior, Blind Image Deblurring Using Patch-Wise Minimal Pixels Regularization, Pixel Screening Based Intermediate Correction for Blind Deblurring, Learning a convolutional neural network for non-uniform motion blur removal, Convolutional neural networks for direct text deblurring, A neural approach to blind motion deblurring, Deep multi-scale convolutional neural network for dynamic scene deblurring, From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur, Blur-Invariant Deep Learning for Blind Deblurring, Learning to Super-resolve Blurry Face and Text Images, Learning Discriminative Data Fitting Functions for Blind Image Deblurring, Semi-supervised Learning of Camera Motion from a Blurred Image, Motion blur kernel estimation via deep learning, Learning a Discriminative Prior for Blind Image Deblurring, Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks, Scale-recurrent network for deep image deblurring, Deblurgan: Blind motion deblurring using conditional adversarial networks, Gated Fusion Network for Joint Image Deblurring and Super-Resolution, Gyroscope-Aided Motion Deblurring with Deep Networks, Dynamic Scene Deblurring With Parameter Selective Sharing and Nested Skip Connections, Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring, Unsupervised Domain-Specific Deblurring via Disentangled Representations, Douglas-Rachford Networks: Learning Both the Image Prior and Data Fidelity Terms for Blind Image Deconvolution, DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better, Blind image deconvolution using deep generative priors, Tell Me Where It is Still Blurry: Adversarial Blurred Region Mining and Refining, Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement, Learning to Deblur Face Images via Sketch Synthesis, Region-Adaptive Dense Network for Efficient Motion Deblurring, Neural Blind Deconvolution Using Deep Priors, Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring, Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training, Deblurring using Analysis-Synthesis Networks Pair, Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training, Efficient and Interpretable Deep Blind Image Deblurring Via Algorithm Unrolling, Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks, Dark and bright channel prior embedded network for dynamic scene deblurring, Dynamic Scene Deblurring by Depth Guided Model, Scale-Iterative Upscaling Network for Image Deblurring, Human Motion Deblurring using Localized Body Prior, Physics-Based Generative Adversarial Models for Image Restoration and Beyond, Blind Image Deconvolution using Deep Generative Priors, Blur Invariant Kernel-Adaptive Network for Single Image Blind deblurring, Exposure Trajectory Recovery from Motion Blur, BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring, Multi-Stage Progressive Image Restoration, DeFMO: Deblurring and Shape Recovery of Fast Moving Objects, Test-Time Fast Adaptation for Dynamic Scene Deblurring via Meta-Auxiliary Learning, Explore Image Deblurring via Encoded Blur Kernel Space, Multi-stage progressive image restoration, Hinet: Half instance normalization network for image restoration, Spatially-Adaptive Image Restoration using Distortion-Guided Networks, Rethinking Coarse-To-Fine Approach in Single Image Deblurring, Perceptual Variousness Motion Deblurring With Light Global Context Refinement, Pyramid Architecture Search for Real-Time Image Deblurring, Searching for Controllable Image Restoration Networks, Sdwnet: A straight dilated network with wavelet transformation for image deblurring, Structure-Aware Motion Deblurring Using Multi-Adversarial Optimized CycleGAN, Degradation Aware Approach to Image Restoration Using Knowledge Distillation, Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition, Clean Images are Hard to Reblur: A New Clue for Deblurring, Deep residual fourier transformation for single image deblurring, Single-image deblurring with neural networks: A comparative survey, Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding, Deep Robust Image Deblurring via Blur Distilling and Information Comparison in Latent Space, Deep Feature Prior Guided Face Deblurring, Restormer: Efficient transformer for high-resolution image restoration, Maxim: Multi-axis mlp for image processing, Uformer: A general u-shaped transformer for image restoration, XYDeblur: Divide and Conquer for Single Image Deblurring, Deblur-NeRF: Neural Radiance Fields From Blurry Images, All-In-One Image Restoration for Unknown Corruption, Exploring and Evaluating Image Restoration Potential in Dynamic Scenes, Deep Generalized Unfolding Networks for Image Restoration, GIQE: Generic Image Quality Enhancement via Nth Order Iterative Degradation, Blind Non-Uniform Motion Deblurring Using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency, Motion Aware Double Attention Network for Dynamic Scene Deblurring, Stripformer: Strip Transformer for Fast Image Deblurring, D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restoration, Improving Image Restoration by Revisiting Global Information Aggregation, Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance, Learning Degradation Representations for Image Deblurring, Realistic Blur Synthesis for Learning Image Deblurring, Event-based Fusion for Motion Deblurring with Cross-modal Attention, Multi-scale-stage network for single image deblurring, Image deblurring in the presence of impulsive noise, Fast image deconvolution using hyper-laplacian priors, Richardson-Lucy Deblurring for Scenes under a Projective Motion Path, Handling outliers in non-blind image deconvolution, From learning models of natural image patches to whole image restoration, Bm3d frames and variational image deblurring, Robust image deblurring with an inaccurate blur kernel, A machine learning approach for non-blind image deconvolution, A general framework for regularized, similarity-based image restoration, Deep convolutional neural network for image deconvolution, Shrinkage fields for effective image restoration, Good Image Priors for Non-blind Deconvolution: Generic vs Specific, Fast Non-Blind Image De-blurring With Sparse Priors, Partial Deconvolution With Inaccurate Blur Kernel, Fast non-blind deconvolution via regularized residual networks with long/short skip-connections, Learning Deep CNN Denoiser Prior for Image Restoration, Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution, Learning proximal operators: Using denoising networks for regularizing inverse imaging problems, Learning to push the limits of efficient fft-based image deconvolution, Deep Mean-Shift Priors for Image Restoration, Modeling Realistic Degradations in Non-Blind Deconvolution, Non-blind Deblurring: Handling Kernel Uncertainty with CNNs, Learning Data Terms for Non-blind Deblurring, Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation, Deep decoder: Concise image representations from untrained non-convolutional networks, Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels, Image deconvolution with deep image and kernel priors, Denoising prior driven deep neural network for image restoration, Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring, Deep Learning for Handling Kernel/model Uncertainty in Image Deconvolution, End-to-end interpretable learning of non-blind image deblurring, Bp-dip: A backprojection based deep image prior, Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring, Learning deep gradient descent optimization for image deconvolution, Neumann networks for linear inverse problems in imaging, The Maximum Entropy on the Mean Method for Image Deblurring, Learning Spatially-Variant MAP Models for Non-Blind Image Deblurring, Learning a Non-Blind Deblurring Network for Night Blurry Images, Nonblind Image Deblurring via Deep Learning in Complex Field, Non-Blind Deblurring for Fluorescence: A Deformable Latent Space Approach With Kernel Parameterization, A Robust Non-Blind Deblurring Method Using Deep Denoiser Prior, Image Deblurring with Blurred/Noisy Image Pairs, Blind motion deblurring using multiple images, Efficient filter flow for space-variant multiframe blind deconvolution, Deconvolving PSFs for A Better Motion Deblurring using Multiple Images, Robust multichannel blind deconvolution via fast alternating minimization, Registration Based Non-uniform Motion Deblurring, Video deblurring for hand-held cameras using patch-based synthesis, Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior, Multi-Shot Imaging: Joint Alignment, Deblurring and Resolution Enhancement, Gyro-Based Multi-Image Deconvolution for Removing Handshake Blur, Hand-held video deblurring via efficient fourier aggregation, Removing camera shake via weighted fourier burst accumulation, Generalized Video Deblurring for Dynamic Scenes, Intra-Frame Deblurring by Leveraging Inter-Frame Camera Motion, Simultaneous stereo video deblurring and scene flow estimation, Deep Video Deblurring for Hand-Held Cameras, Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel, Online Video Deblurring via Dynamic Temporal Blending Network, Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks, Joint Blind Motion Deblurring and Depth Estimation of Light Field, Dynamic Video Deblurring using a Locally Adaptive Linear Blur Model, Reblur2deblur: Deblurring videos via self-supervised learning, LSD-Joint Denoising and Deblurring of Short and Long Exposure Images with Convolutional Neural Networks, Adversarial Spatio-Temporal Learning for Video Deblurring, Recurrent Neural Networks With Intra-Frame Iterations for Video Deblurring, DAVANet: Stereo Deblurring With View Aggregation, A Deep Motion Deblurring Network based on Per-Pixel Adaptive Kernels with Residual Down-Up and Up-Down Modules, Spatio-Temporal Filter Adaptive Network for Video Deblurring, Face Video Deblurring using 3D Facial Priors, Deep Recurrent Network for Fast and Full-Resolution Light Field Deblurring, Deep Video Deblurring: The Devil is in the Details, Cascaded Deep Video Deblurring Using Temporal Sharpness Prior, Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring, Learning Event-Driven Video Deblurring and Interpolation, Blur Removal Via Blurred-Noisy Image Pair, Recursive Neural Network for Video Deblurring, Motion-blurred Video Interpolation and Extrapolation, Gated Spatio-Temporal Attention-Guided Video Deblurring, ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring, Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes, Video Deblurring via Spatiotemporal Pyramid Network and Adversarial Gradient Prior, Multi-Scale Separable Network for Ultra-High-Definition Video Deblurring, Deep Recurrent Neural Network with Multi-Scale Bi-Directional Propagation for Video Deblurring, Efficient Video Deblurring Guided by Motion Magnitude, Spatio-Temporal Deformable Attention Network for Video Deblurring, ERDN: Equivalent Receptive Field Deformable Network for Video Deblurring, DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting, Towards Real-World Video Deblurring by Exploring Blur Formation Process, Real-Time Video Deblurring via Lightweight Motion Compensation, Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network, NTIRE 2019 Challenge on Video Deblurring: Methods and Results, NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study, EDVR: Video Restoration with Enhanced Deformable Convolutional Networks, Ntire 2020 challenge on image and video deblurring, Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency, High-Resolution Dual-Stage Multi-Level Feature Aggregation for Single Image and Video Deblurring, Multiframe Restoration Methods for Image Synthesis and Recovery, Joseph J.
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