Itti, L.[Laurent]
* 1998: Learning to Detect Salient Objects in Natural Scenes Using Visual Attention
* 1998: Model of Saliency-Based Visual Attention for Rapid Scene Analysis, A
* 2000: Saliency-Based Search Mechanism for Overt and Covert Shifts of Visual Attention
* 2002: Attentional Selection for Object Recognition: A Gentle Way
* 2002: Goal Oriented Attention Guidance Model, A
* 2002: Model of Contour Integration in Early Visual Cortex, A
* 2002: New Robotics Platform for Neuromorphic Vision: Beobots, A
* 2004: Automatic Foveation for Video Compression Using a Neurobiological Model of Visual Attention
* 2004: Biologically-Inspired Face Detection: Non-Brute-Force-Search Approach
* 2005: Combining attention and recognition for rapid scene analysis
* 2005: Gist: A Mobile Robotics Application of Context-Based Vision in Outdoor Environment
* 2005: Principled Approach to Detecting Surprising Events in Video, A
* 2005: Quantifying the contribution of low-level saliency to human eye movements in dynamic scenes
* 2006: Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed, An
* 2006: use of attention and spatial information for rapid facial recognition in video, The
* 2007: Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention
* 2007: Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
* 2011: Computational Modeling of Top-down Visual Attention in Interactive Environments
* 2011: Saliency and Gist Features for Target Detection in Satellite Images
* 2011: Visual attention guided bit allocation in video compression
* 2012: Adaptive object tracking by learning background context
* 2012: Exploiting local and global patch rarities for saliency detection
* 2012: Neuromorphic Bayesian Surprise for Far-Range Event Detection
* 2012: Probabilistic learning of task-specific visual attention
* 2012: Saliency mapping enhanced by symmetry from local phase
* 2012: Salient Object Detection: A Benchmark
* 2013: Analysis of Scores, Datasets, and Models in Visual Saliency Prediction
* 2013: Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study
* 2013: State-of-the-Art in Visual Attention Modeling
* 2014: Human vs. Computer in Scene and Object Recognition
* 2014: Integrating human context and occlusion reasoning to improve handheld object tracking
* 2014: Performance Evaluation of Neuromorphic-Vision Object Recognition Algorithms
* 2014: System for Assisting the Visually Impaired in Localization and Grasp of Desired Objects, A
* 2014: What/Where to Look Next? Modeling Top-Down Visual Attention in Complex Interactive Environments
* 2015: BIK-BUS: Biologically Motivated 3D Keypoint Based on Bottom-Up Saliency
* 2015: Fixation bank: Learning to reweight fixation candidates
* 2016: Decomposing time series with application to temporal segmentation
* 2016: iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning
* 2016: Learning a Combined Model of Visual Saliency for Fixation Prediction
* 2016: Perfect Accuracy with Human-in-the-Loop Object Detection
* 2017: Improved Deep Learning of Object Category Using Pose Information
* 2017: Learning to Recognize Objects by Retaining Other Factors of Variation
* 2017: Saliency prediction based on new deep multi-layer convolution neural network
* 2017: Third Eye: A Shopping Assistant for the Visually Impaired
* 2018: Salient object detection via a local and global method based on deep residual network
* 2018: shapeDTW: Shape Dynamic Time Warping
* 2020: Learning visual variation for object recognition
* 2020: Pose Augmentation: Class-agnostic Object Pose Transformation for Object Recognition
* 2021: Multi-Task Occlusion Learning for Real-Time Visual Object Tracking
* 2021: Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts, A
* 2022: Contributions of Shape, Texture, and Color in Visual Recognition
* 2022: incDFM: Incremental Deep Feature Modeling for Continual Novelty Detection
* 2022: Neural-Sim: Learning to Generate Training Data with NeRF
* 2022: SHERLock: Self-Supervised Hierarchical Event Representation Learning
* 2023: Batch Model Consolidation: A Multi-Task Model Consolidation Framework
* 2023: CLR: Channel-wise Lightweight Reprogramming for Continual Learning
* 2023: Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization
* 2023: HOOT: Heavy Occlusions in Object Tracking Benchmark
* 2023: Improving Zero-shot Generalization and Robustness of Multi-Modal Models
* 2024: Evaluating Pretrained Models for Deployable Lifelong Learning
Includes: Itti, L.[Laurent] Itti, L.
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