Larlus, D.[Diane]
* 2006: Latent Mixture Vocabularies for Object Categorization
* 2007: Learning Color Names from Real-World Images
* 2008: Combining appearance models and Markov Random Fields for category level object segmentation
* 2009: Latent mixture vocabularies for object categorization and segmentation
* 2009: Learning Color Names for Real-World Applications
* 2010: Category Level Object Segmentation by Combining Bag-of-Words Models with Dirichlet Processes and Random Fields
* 2010: Extracting Structures in Image Collections for Object Recognition
* 2011: Assessing the aesthetic quality of photographs using generic image descriptors
* 2011: Combining Visible and Near-Infrared Cues for image Categorisation
* 2011: Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors
* 2012: Semantic Image Segmentation Using Visible and Near-Infrared Channels
* 2013: Predicting an Object Location Using a Global Image Representation
* 2013: What is a good evaluation measure for semantic segmentation?
* 2015: Fisher vectors meet Neural Networks: A hybrid classification architecture
* 2016: Data-Driven Detection of Prominent Objects
* 2016: Deep Image Retrieval: Learning Global Representations for Image Search
* 2016: I Have Seen Enough: Transferring Parts Across Categories
* 2016: Learning the Structure of Objects from Web Supervision
* 2017: AnchorNet: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching
* 2017: Beyond Instance-Level Image Retrieval: Leveraging Captions to Learn a Global Visual Representation for Semantic Retrieval
* 2017: End-to-End Learning of Deep Visual Representations for Image Retrieval
* 2017: Learning 3D Object Categories by Looking Around Them
* 2018: Self-Supervised Learning of Geometrically Stable Features Through Probabilistic Introspection
* 2018: Semi-convolutional Operators for Instance Segmentation
* 2019: Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings
* 2020: Capturing the Geometry of Object Categories from Video Supervision
* 2020: Learning Visual Representations with Caption Annotations
* 2021: Concept Generalization in Visual Representation Learning
* 2021: Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning
* 2021: NeuralDiff: Segmenting 3D objects that move in egocentric videos
* 2021: Probabilistic Embeddings for Cross-Modal Retrieval
* 2021: StacMR: Scene-Text Aware Cross-Modal Retrieval
* 2021: Unsupervised Meta-Domain Adaptation for Fashion Retrieval
* 2022: Granularity-Aware Adaptation for Image Retrieval Over Multiple Tasks
* 2022: On the Road to Online Adaptation for Semantic Image Segmentation
* 2023: Fake it Till You Make it: Learning Transferable Representations from Synthetic ImageNet Clones
* 2023: SLACK: Stable Learning of Augmentations with Cold-Start and KL Regularization
Includes: Larlus, D.[Diane] Larlus, D.
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