Itti, L.[Laurent]
Co Author Listing * Adaptive object tracking by learning background context
* Analysis of Scores, Datasets, and Models in Visual Saliency Prediction
* Attentional Selection for Object Recognition: A Gentle Way
* Automatic Foveation for Video Compression Using a Neurobiological Model of Visual Attention
* Batch Model Consolidation: A Multi-Task Model Consolidation Framework
* Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention
* BIK-BUS: Biologically Motivated 3D Keypoint Based on Bottom-Up Saliency
* Biologically-Inspired Face Detection: Non-Brute-Force-Search Approach
* CLR: Channel-wise Lightweight Reprogramming for Continual Learning
* Combining attention and recognition for rapid scene analysis
* Computational Modeling of Top-down Visual Attention in Interactive Environments
* Contributions of Shape, Texture, and Color in Visual Recognition
* Decomposing time series with application to temporal segmentation
* Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization
* Evaluating Pretrained Models for Deployable Lifelong Learning
* Exploiting local and global patch rarities for saliency detection
* Fixation bank: Learning to reweight fixation candidates
* Gist: A Mobile Robotics Application of Context-Based Vision in Outdoor Environment
* Goal Oriented Attention Guidance Model, A
* HOOT: Heavy Occlusions in Object Tracking Benchmark
* Human vs. Computer in Scene and Object Recognition
* iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning
* Improved Deep Learning of Object Category Using Pose Information
* Improving Zero-shot Generalization and Robustness of Multi-Modal Models
* incDFM: Incremental Deep Feature Modeling for Continual Novelty Detection
* Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed, An
* Integrating human context and occlusion reasoning to improve handheld object tracking
* Learning a Combined Model of Visual Saliency for Fixation Prediction
* Learning to Detect Salient Objects in Natural Scenes Using Visual Attention
* Learning to Recognize Objects by Retaining Other Factors of Variation
* Learning visual variation for object recognition
* Model of Contour Integration in Early Visual Cortex, A
* Model of Saliency-Based Visual Attention for Rapid Scene Analysis, A
* Multi-Task Occlusion Learning for Real-Time Visual Object Tracking
* Neural-Sim: Learning to Generate Training Data with NeRF
* Neuromorphic Bayesian Surprise for Far-Range Event Detection
* New Robotics Platform for Neuromorphic Vision: Beobots, A
* Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts, A
* Perfect Accuracy with Human-in-the-Loop Object Detection
* Performance Evaluation of Neuromorphic-Vision Object Recognition Algorithms
* Pose Augmentation: Class-agnostic Object Pose Transformation for Object Recognition
* Principled Approach to Detecting Surprising Events in Video, A
* Probabilistic learning of task-specific visual attention
* Quantifying the contribution of low-level saliency to human eye movements in dynamic scenes
* Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study
* Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
* Saliency and Gist Features for Target Detection in Satellite Images
* Saliency mapping enhanced by symmetry from local phase
* Saliency prediction based on new deep multi-layer convolution neural network
* Saliency-Based Search Mechanism for Overt and Covert Shifts of Visual Attention
* Salient object detection via a local and global method based on deep residual network
* Salient Object Detection: A Benchmark
* shapeDTW: Shape Dynamic Time Warping
* SHERLock: Self-Supervised Hierarchical Event Representation Learning
* State-of-the-Art in Visual Attention Modeling
* System for Assisting the Visually Impaired in Localization and Grasp of Desired Objects, A
* Third Eye: A Shopping Assistant for the Visually Impaired
* use of attention and spatial information for rapid facial recognition in video, The
* Visual attention guided bit allocation in video compression
* What/Where to Look Next? Modeling Top-Down Visual Attention in Complex Interactive Environments
Includes: Itti, L.[Laurent] Itti, L.
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