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