Mehrtash Harandi

DIET: Machine Unlearning on a Data-Diet AAAI 2026 Exemplar-Free Continual Learning for State Space Models CVPR 2026 PCGS: Progressive Compression of 3D Gaussian Splatting AAAI 2026 pH-Strips for Selective Forgetting: A Blunt but Fast Diagnostic Baseline for Machine Unlearning CVPR 2026 Subspace-Guided Knowledge Distillation for Efficient Model Transfer WACV 2026 Test-Time Instance-Specific Parameter Composition: A New Paradigm for Adaptive Generative Modeling CVPR 2026 A Good Teacher Adapts Their Knowledge for Distillation ICCV 2025 Erasing Undesirable Influence in Diffusion Models CVPR 2025 Fast Feedforward 3D Gaussian Splatting Compression ICLR 2025 MUNBa: Machine Unlearning via Nash Bargaining ICCV 2025 SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution WACV 2025 Backpropagation-free Network for 3D Test-time Adaptation CVPR 2024 Canonical Shape Projection is All You Need for 3D Few-shot Class Incremental Learning ECCV 2024 Concealing Sensitive Samples against Gradient Leakage in Federated Learning AAAI 2024 Continual Test-Time Domain Adaptation via Dynamic Sample Selection WACV 2024 Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization NeurIPS 2024 FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models NeurIPS 2024 HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression ECCV 2024 How Far Can We Compress Instant-NGP-Based NeRF? CVPR 2024 LaViP: Language-Grounded Visual Prompting AAAI 2024 NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge Distillation CVPR 2024 Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks ECCV 2024 Text-Enhanced Data-free Approach for Federated Class-Incremental Learning CVPR 2024 Energy-based Self-Training and Normalization for Unsupervised Domain Adaptation ICCV 2023 Exploring Data Geometry for Continual Learning CVPR 2023 Hyperbolic Audio-visual Zero-shot Learning ICCV 2023 LAVA: Label-Efficient Visual Learning and Adaptation WACV 2023 Vector Quantized Wasserstein Auto-Encoder ICML 2023 Adaptive Poincaré Point to Set Distance for Few-Shot Classification AAAI 2022 Efficient Riemannian Meta-Optimization by Implicit Differentiation AAAI 2022 Hyperbolic Feature Augmentation via Distribution Estimation and Infinite Sampling on Manifolds NeurIPS 2022 Implicit Motion Handling for Video Camouflaged Object Detection CVPR 2022 Learning Instance and Task-Aware Dynamic Kernels for Few-Shot Learning ECCV 2022 Meta-Learning for Multi-Label Few-Shot Classification WACV 2022 On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation NeurIPS 2022 On Generalizing Beyond Domains in Cross-Domain Continual Learning CVPR 2022 Rethinking Generalization in Few-Shot Classification NeurIPS 2022 Towards a Robust Differentiable Architecture Search Under Label Noise WACV 2022 Curvature Generation in Curved Spaces for Few-Shot Learning ICCV 2021 Kernel Methods in Hyperbolic Spaces ICCV 2021 Learning a Gradient-free Riemannian Optimizer on Tangent Spaces AAAI 2021 On Learning the Geodesic Path for Incremental Learning CVPR 2021 Reinforced Attention for Few-Shot Learning and Beyond CVPR 2021 Semantic-Aware Knowledge Distillation for Few-Shot Class-Incremental Learning CVPR 2021 Semi-Supervised Metric Learning: A Deep Resurrection AAAI 2021 Set Augmented Triplet Loss for Video Person Re-Identification WACV 2021 Synthesized Feature Based Few-Shot Class-Incremental Learning on a Mixture of Subspaces ICCV 2021 Adaptive Subspaces for Few-Shot Learning CVPR 2020 Devon: Deformable Volume Network for Learning Optical Flow WACV 2020 Hierarchical Neural Architecture Search for Deep Stereo Matching NeurIPS 2020 Learning from Noisy Labels via Discrepant Collaborative Training WACV 2020 Learning to Optimize on SPD Manifolds CVPR 2020 On Modulating the Gradient for Meta-Learning ECCV 2020 Revisiting Bilinear Pooling: A Coding Perspective AAAI 2020 Unsupervised Metric Learning with Synthetic Examples AAAI 2020 Bilinear Attention Networks for Person Retrieval ICCV 2019 Min-Max Statistical Alignment for Transfer Learning CVPR 2019 Neural Collaborative Subspace Clustering ICML 2019 Siamese Networks: The Tale of Two Manifolds ICCV 2019 Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective CVPR 2018 Geometry Aware Constrained Optimization Techniques for Deep Learning CVPR 2018 Museum Exhibit Identification Challenge for the Supervised Domain Adaptation and Beyond ECCV 2018 Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding NeurIPS 2017 Generalized Rank Pooling for Activity Recognition CVPR 2017 Joint Dimensionality Reduction and Metric Learning: A Geometric Take ICML 2017 Learning an Invariant Hilbert Space for Domain Adaptation CVPR 2017 Learning Discriminative ab-Divergences for Positive Definite Matrices ICCV 2017 Distribution-Matching Embedding for Visual Domain Adaptation JMLR 2016 Sparse Coding and Dictionary Learning With Linear Dynamical Systems CVPR 2016 When VLAD Met Hilbert CVPR 2016 Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs ICCV 2015 Riemannian Coding and Dictionary Learning: Kernels to the Rescue CVPR 2015 Bregman Divergences for Infinite Dimensional Covariance Matrices CVPR 2014 Optimizing Over Radial Kernels on Compact Manifolds CVPR 2014 A Framework for Shape Analysis via Hilbert Space Embedding ICCV 2013 Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution ICCV 2013 Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices CVPR 2013 Non-Linear Stationary Subspace Analysis with Application to Video Classification ICML 2013