Ghodasara, G.V.
* 2011: Some new product cordial graphs
Ghoddoosian, R.[Reza]
* 2021: Action Duration Prediction for Segment-Level Alignment of Weakly-Labeled Videos
* 2022: Hierarchical Modeling for Task Recognition and Action Segmentation in Weakly-Labeled Instructional Videos
* 2022: Weakly-Supervised Online Action Segmentation in Multi-View Instructional Videos
* 2023: New Dataset and Approach for Timestamp Supervised Action Segmentation Using Human Object Interaction, A
* 2023: Weakly-Supervised Action Segmentation and Unseen Error Detection in Anomalous Instructional Videos
Ghodhbani, E.
* 2019: Close Approximation of Kullback-Leibler Divergence for Sparse Source Retrieval
* 2019: Depth-based color stereo images retrieval using joint multivariate statistical models
* 2024: Graph Neural Networks for End-to-End Information Extraction from Handwritten Documents
Includes: Ghodhbani, E. Ghodhbani, E.[Emna]
Ghodhbani, R.[Refka]
* 2019: efficient pass-parallel architecture for embedded block coder in JPEG 2000, An
Ghodousi, M.[Mostafa]
* 2018: Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS
Ghodrati, A.[Amir]
* 2014: Coupling video segmentation and action recognition
* 2014: Is 2D Information Enough For Viewpoint Estimation?
* 2015: DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
* 2015: Modeling video evolution for action recognition
* 2016: Online Action Detection
* 2016: Towards Automatic Image Editing: Learning to See another
* 2017: DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers
* 2017: Rank Pooling for Action Recognition
* 2018: Actor and Action Video Segmentation from a Sentence
* 2019: Recognizing Compressed Videos: Challenges and Promises
* 2020: ActionBytes: Learning From Trimmed Videos to Localize Actions
* 2021: FrameExit: Conditional Early Exiting for Efficient Video Recognition
* 2022: SALISA: Saliency-Based Input Sampling for Efficient Video Object Detection
* 2023: RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning
Includes: Ghodrati, A.[Amir] Ghodrati, A.
14 for Ghodrati, A.
Ghodrati, H.[Hamed]
* 2010: Localization of noncircular iris boundaries using morphology and arched Hough transform
* 2014: new accurate noise-removing approach for non-cooperative iris recognition, A
* 2016: Deep shape-aware descriptor for nonrigid 3D object retrieval
Ghodrati, S.[Sajjad]
* 2018: How accurately do different computer-based texture characterization methods predict material surface coarseness? A guideline for effective online inspection
Ghodratnama, S.[Samaneh]
* 2017: Toward semantic content-based image retrieval using Dempster-Shafer theory in multi-label classification framework
Ghods, A.H.
* 2010: Efficient Optimization Approach to Real-Time Coordinated and Integrated Freeway Traffic Control, An
Ghods, V.[Vahid]
* 2010: Feature Extraction for Online Farsi Characters
* 2013: Effect of delayed strokes on the recognition of online Farsi handwriting
Ghodsi, A.[Ali]
* 2005: Tangent-Corrected Embedding
* 2010: Rare Class Classification by Support Vector Machine
* 2011: Dictionary Learning in Texture Classification
* 2011: Guided Locally Linear Embedding
* 2011: Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds
* 2012: Supervised Texture Classification Using a Novel Compression-Based Similarity Measure
* 2013: Discriminative functional analysis of human movements
* 2016: Semi-supervised Dictionary Learning Based on Hilbert-Schmidt Independence Criterion
* 2017: Discovery Radiomics via a Mixture of Deep ConvNet Sequencers for Multi-parametric MRI Prostate Cancer Classification
* 2017: Fast Spectral Clustering Using Autoencoders and Landmarks
* 2020: Discriminant component analysis via distance correlation maximization
11 for Ghodsi, A.
Ghodsi, S.[Saeed]
* 2018: Simultaneous joint and object trajectory templates for human activity recognition from 3-D data
Ghodsi, Z.[Zahra]
* 2023: zPROBE: Zero Peek Robustness Checks for Federated Learning