Veda, N.
* 1993: Graph-Based Thinning for Binary Images
Vedadi, F.[Farhang]
* 2012: Image resolution up-conversion via maximum a posteriori interpolator sequence estimation and Viterbi algorithm
* 2013: De-Interlacing Using Nonlocal Costs and Markov-Chain-Based Estimation of Interpolation Methods
* 2014: MAP-Based Image Interpolation Method via Viterbi Decoding of Markov Chains of Interpolation Functions, A
* 2020: Automatic Visual Fingerprinting for Indoor Image-Based Localization Applications
Includes: Vedadi, F.[Farhang] Vedadi, F.
Vedaldi, A.[Andrea]
* 1900: VLFeat
* 2005: Features for Recognition: Viewpoint Invariance for Non-Planar Scenes
* 2005: KALMANSAC: Robust Filtering by Consensus
* 2006: Local Features, All Grown Up
* 2006: Viewpoint Induced Deformation Statistics and the Design of Viewpoint Invariant Features: Singularities and Occlusions
* 2007: Boosting Invariance and Efficiency in Supervised Learning
* 2007: Moving Forward in Structure From Motion
* 2007: Objects in Context
* 2008: Joint data alignment up to (lossy) transformations
* 2008: Localizing Objects with Smart Dictionaries
* 2008: Quick Shift and Kernel Methods for Mode Seeking
* 2008: Relaxed matching kernels for robust image comparison
* 2009: Class Segmentation and Object Localization with Superpixel Neighborhoods
* 2009: Multiple kernels for object detection
* 2010: Efficient Additive Kernels via Explicit Feature Maps
* 2010: Generalized Rbf feature maps for Efficient Detection
* 2011: coarse-to-fine approach for fast deformable object detection, A
* 2011: devil is in the details: An evaluation of recent feature encoding methods, The
* 2011: Learning equivariant structured output SVM regressors
* 2011: truth about cats and dogs, The
* 2012: Cats and dogs
* 2012: Descriptor Learning Using Convex Optimisation
* 2012: Efficient Additive Kernels via Explicit Feature Maps
* 2012: Self-similar Sketch
* 2012: Sparse kernel approximations for efficient classification and detection
* 2013: Blocks That Shout: Distinctive Parts for Scene Classification
* 2013: Fisher Vector Faces in the Wild
* 2014: Compact and Discriminative Face Track Descriptor, A
* 2014: Deep Features for Text Spotting
* 2014: Describing Textures in the Wild
* 2014: Learning Local Feature Descriptors Using Convex Optimisation
* 2014: Return of the Devil in the Details: Delving Deep into Convolutional Nets
* 2014: Speeding up Convolutional Neural Networks with Low Rank Expansions
* 2014: Understanding Objects in Detail with Fine-Grained Attributes
* 2015: Building the View Graph of a Category by Exploiting Image Realism
* 2015: coarse-to-fine approach for fast deformable object detection, A
* 2015: Deep Face Recognition
* 2015: Deep filter banks for texture recognition and segmentation
* 2015: Factorized appearances for object detection
* 2015: R-CNN minus R
* 2015: Understanding deep image representations by inverting them
* 2015: Understanding Image Representations by Measuring Their Equivariance and Equivalence
* 2016: Deep Filter Banks for Texture Recognition, Description, and Segmentation
* 2016: Dynamic Image Networks for Action Recognition
* 2016: Fully-Convolutional Siamese Networks for Object Tracking
* 2016: Fully-trainable deep matching
* 2016: I Have Seen Enough: Transferring Parts Across Categories
* 2016: Learning Covariant Feature Detectors
* 2016: Learning Grimaces by Watching TV
* 2016: Learning the Structure of Objects from Web Supervision
* 2016: Reading Text in the Wild with Convolutional Neural Networks
* 2016: Salient Deconvolutional Networks
* 2016: Synthetic Data for Text Localisation in Natural Images
* 2016: Visual Object Tracking VOT2016 Challenge Results, The
* 2016: Visualizing Deep Convolutional Neural Networks Using Natural Pre-images
* 2016: Weakly Supervised Deep Detection Networks
* 2017: AnchorNet: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching
* 2017: Editorial: Deep Learning for Computer Vision
* 2017: End-to-End Representation Learning for Correlation Filter Based Tracking
* 2017: H-Patches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors
* 2017: Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis
* 2017: Interpretable Explanations of Black Boxes by Meaningful Perturbation
* 2017: Learning 3D Object Categories by Looking Around Them
* 2017: Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings
* 2017: Visual Object Tracking VOT2017 Challenge Results, The
* 2018: Action Recognition with Dynamic Image Networks
* 2018: Cross Pixel Optical-Flow Similarity for Self-supervised Learning
* 2018: Deep Image Prior
* 2018: Efficient Parametrization of Multi-domain Deep Neural Networks
* 2018: Long-Term Tracking in the Wild: A Benchmark
* 2018: MapNet: An Allocentric Spatial Memory for Mapping Environments
* 2018: Net2Vec: Quantifying and Explaining How Concepts are Encoded by Filters in Deep Neural Networks
* 2018: NightOwls: A Pedestrians at Night Dataset
* 2018: Self-Supervised Learning of Geometrically Stable Features Through Probabilistic Introspection
* 2018: Self-supervised Segmentation by Grouping Optical-Flow
* 2018: Semi-convolutional Operators for Instance Segmentation
* 2018: ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking
* 2018: Sixth Visual Object Tracking VOT2018 Challenge Results, The
* 2018: Supervising the New with the Old: Learning SFM from SFM
* 2018: Tiny People Pose
* 2018: Unsupervised Intuitive Physics from Visual Observations
* 2019: C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion
* 2019: Invariant Information Clustering for Unsupervised Image Classification and Segmentation
* 2019: Learning to Discover Novel Visual Categories via Deep Transfer Clustering
* 2019: Slim DensePose: Thrifty Learning From Sparse Annotations and Motion Cues
* 2019: Small Steps and Giant Leaps: Minimal Newton Solvers for Deep Learning
* 2019: Taking visual motion prediction to new heightfields
* 2019: Understanding Deep Networks via Extremal Perturbations and Smooth Masks
* 2019: Understanding Image Representations by Measuring Their Equivariance and Equivalence
* 2019: Unsupervised Learning of Landmarks by Descriptor Vector Exchange
* 2020: Capturing the Geometry of Object Categories from Video Supervision
* 2020: Deep Image Prior
* 2020: H-Patches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors
* 2020: Self-Supervised Learning of Interpretable Keypoints From Unlabelled Videos
* 2020: Semi-Supervised Learning with Scarce Annotations
* 2020: There and Back Again: Revisiting Backpropagation Saliency Methods
* 2020: Transferring Dense Pose to Proximal Animal Classes
* 2020: Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the Wild
* 2021: DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension
* 2021: Discovering Relationships between Object Categories via Universal Canonical Maps
* 2021: Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation
* 2021: Localizing Visual Sounds the Hard Way
* 2021: LSD-C: Linearly Separable Deep Clusters
* 2021: NeuralDiff: Segmenting 3D objects that move in egocentric videos
* 2021: NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go
* 2021: On Compositions of Transformations in Contrastive Self-Supervised Learning
* 2021: Pedestrian and Ego-vehicle Trajectory Prediction from Monocular Camera
* 2021: Space-Time Crop & Attend: Improving Cross-modal Video Representation Learning
* 2021: Unsupervised Learning of 3D Object Categories from Videos in the Wild
* 2022: AutoNovel: Automatically Discovering and Learning Novel Visual Categories
* 2022: BANMo: Building Animatable 3D Neural Models from Many Casual Videos
* 2022: Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization
* 2022: End-to-End Visual Editing with a Generatively Pre-Trained Artist
* 2022: Generalized Category Discovery
* 2022: KeyTr: Keypoint Transporter for 3D Reconstruction of Deformable Objects in Videos
* 2022: Self-supervised object detection from audio-visual correspondence
* 2022: SNeS: Learning Probably Symmetric Neural Surfaces from Incomplete Data
* 2023: Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories
* 2023: Continual Detection Transformer for Incremental Object Detection
* 2023: DOVE: Learning Deformable 3D Objects by Watching Videos
* 2023: DynamicStereo: Consistent Dynamic Depth from Stereo Videos
* 2023: HOLODIFFUSION: Training a 3D Diffusion Model Using 2D Images
* 2023: Learning Universal Semantic Correspondences with No Supervision and Automatic Data Curation
* 2023: MagicPony: Learning Articulated 3D Animals in the Wild
* 2023: Novel-View Acoustic Synthesis
* 2023: Online Clustered Codebook
* 2023: PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction
* 2023: RealFusion 360° Reconstruction of Any Object from a Single Image
* 2023: Replay: Multi-modal Multi-view Acted Videos for Casual Holography
* 2023: Self-supervised Correspondence Estimation via Multiview Registration
* 2023: Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the Wild
* 2023: Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Data
* 2023: What does CLIP know about a red circle? Visual prompt engineering for VLMs
* 2024: Curious Layperson: Fine-Grained Image Recognition Without Expert Labels, The
* 2024: Training-Free Layout Control with Cross-Attention Guidance
Includes: Vedaldi, A.[Andrea] Vedaldi, A.
135 for Vedaldi, A.
Vedantam, R.[Ramakrishna]
* 2015: CIDEr: Consensus-based image description evaluation
* 2015: Learning Common Sense through Visual Abstraction
* 2016: Adopting Abstract Images for Semantic Scene Understanding
* 2016: VisualWord2Vec (Vis-W2V): Learning Visually Grounded Word Embeddings Using Abstract Scenes
* 2017: Context-Aware Captions from Context-Agnostic Supervision
* 2017: Counting Everyday Objects in Everyday Scenes
* 2017: Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
* 2020: Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
* 2023: Improving Selective Visual Question Answering by Learning from Your Peers
Includes: Vedantam, R.[Ramakrishna] Vedantam, R.
9 for Vedantam, R.
Vedantham, R.[Ramakrishna]
* 2009: Creating compact architectural models by geo-registering image collections
* 2010: Automatic Alignment and Multi-View Segmentation of Street View Data using 3D Shape Priors
* 2010: Dynamic selection of a feature-rich query frame for mobile video retrieval
* 2010: Fast geometric re-ranking for image-based retrieval
* 2010: Visual Navigation for Mobile Devices
* 2011: City-scale landmark identification on mobile devices
* 2011: Mobile Visual Search
Includes: Vedantham, R.[Ramakrishna] Vedantham, R.
7 for Vedantham, R.
Vedantham, S.
* 2010: Modeling the Performance Characteristics of Computed Radiography (CR) Systems