{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/scene-recognition/papers/2","list_of":"/task/scene-recognition","task":"Scene Recognition","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":3,"rows_per_page":100,"rows":[101,200],"of":207,"counts":{"archive_papers_tagged":207,"with_a_code_link":68,"where_syntology_ran_a_sample":8,"not_listed_spam_title":0,"listed":207,"listed_where_code_ran":8,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":8,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":8,"listed_every_run_a_failure_of_syntologys_instrument":0,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/scene-recognition","prev":"/task/scene-recognition","next":"/task/scene-recognition/papers/3","papers":[{"url":null,"slug":"fast-and-efficient-scene-categorization-for","title":"Fast and Efficient Scene Categorization for Autonomous Driving using VAEs","date":"2022-10-26","arxiv_id":"2210.14981","repositories_listed":0,"syntology":null},{"url":null,"slug":"rice-leaf-disease-classification-and","title":"Rice Leaf Disease Classification and Detection Using YOLOv5","date":"2022-09-04","arxiv_id":"2209.01579","repositories_listed":0,"syntology":null},{"url":null,"slug":"loglg-weakly-supervised-log-anomaly-detection","title":"LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction","date":"2022-08-23","arxiv_id":"2208.10833","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-auxiliary-or-adversarial-tasks","title":"Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding","date":"2022-08-22","arxiv_id":"2208.10077","repositories_listed":0,"syntology":null},{"url":null,"slug":"effectiveness-of-function-matching-in-driving","title":"Effectiveness of Function Matching in Driving Scene Recognition","date":"2022-08-20","arxiv_id":"2208.09694","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-refinement-of-a-scene-recognition","title":"Online Refinement of a Scene Recognition Model for Mobile Robots by Observing Human's Interaction with Environments","date":"2022-08-13","arxiv_id":"2208.06636","repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-recognition-with-objectness-attribute","title":"Scene Recognition with Objectness, Attribute and Category Learning","date":"2022-07-20","arxiv_id":"2207.10174","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-a-dct-driven-loss-in-attention","title":"Attention-based Knowledge Distillation in Multi-attention Tasks: The Impact of a DCT-driven Loss","date":"2022-05-04","arxiv_id":"2205.01997","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-power-laws-in-deep","title":"Investigating Power laws in Deep Representation Learning","date":"2022-02-11","arxiv_id":"2202.05808","repositories_listed":0,"syntology":null},{"url":null,"slug":"ala-adversarial-lightness-attack-via","title":"ALA: Naturalness-aware Adversarial Lightness Attack","date":"2022-01-16","arxiv_id":"2201.06070","repositories_listed":0,"syntology":null},{"url":null,"slug":"recognizing-scenes-from-novel-viewpoints","title":"Recognizing Scenes from Novel Viewpoints","date":"2021-12-02","arxiv_id":"2112.01520","repositories_listed":0,"syntology":null},{"url":null,"slug":"ask-adaptively-selecting-key-local-features","title":"ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition","date":"2021-10-14","arxiv_id":"2110.07703","repositories_listed":0,"syntology":null},{"url":"/paper/high-order-tensor-pooling-with-attention-for","slug":"high-order-tensor-pooling-with-attention-for","title":"High-order Tensor Pooling with Attention for Action Recognition","date":"2021-10-11","arxiv_id":"2110.05216","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-oriented-scene-recognition","title":"Data-oriented Scene Recognition","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"convnets-vs-transformers-whose-visual","title":"ConvNets vs. Transformers: Whose Visual Representations are More Transferable?","date":"2021-08-11","arxiv_id":"2108.05305","repositories_listed":0,"syntology":null},{"url":null,"slug":"developing-efficient-transfer-learning","title":"Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre-trained convolutional neural networks","date":"2021-07-23","arxiv_id":"2107.11187","repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-essence","title":"Scene Essence","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"perception-framework-through-real-time","title":"Perception Framework through Real-Time Semantic Segmentation and Scene Recognition on a Wearable System for the Visually Impaired","date":"2021-03-06","arxiv_id":"2103.04136","repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-retrieval-for-contextual-visual-mapping","title":"Scene Retrieval for Contextual Visual Mapping","date":"2021-02-25","arxiv_id":"2102.12728","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-source-pseudo-label-learning-of","title":"Multi-source Pseudo-label Learning of Semantic Segmentation for the Scene Recognition of Agricultural Mobile Robots","date":"2021-02-12","arxiv_id":"2102.06386","repositories_listed":0,"syntology":null},{"url":null,"slug":"flar-a-unified-prototype-framework-for-few","title":"FLAR: A Unified Prototype Framework for Few-Sample Lifelong Active Recognition","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"power-normalizations-in-fine-grained-image","title":"Power Normalizations in Fine-grained Image, Few-shot Image and Graph Classification","date":"2020-12-27","arxiv_id":"2012.13975","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-concept-reasoning-networks","title":"Visual Concept Reasoning Networks","date":"2020-08-26","arxiv_id":"2008.11783","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-scene-recognition-from","title":"Deep learning for scene recognition from visual data: a survey","date":"2020-07-03","arxiv_id":"2007.01806","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-biologically-interpretable-two-stage-deep","title":"A Biologically Interpretable Two-stage Deep Neural Network (BIT-DNN) For Vegetation Recognition From Hyperspectral Imagery","date":"2020-04-19","arxiv_id":"2004.08886","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-multi-task-learning-for-traffic","title":"Context-Aware Multi-Task Learning for Traffic Scene Recognition in Autonomous Vehicles","date":"2020-04-03","arxiv_id":"2004.01351","repositories_listed":0,"syntology":null},{"url":null,"slug":"i-vise-interactive-video-surveillance-as-an","title":"I-ViSE: Interactive Video Surveillance as an Edge Service using Unsupervised Feature Queries","date":"2020-03-09","arxiv_id":"2003.04169","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-integration-networks-for-action","title":"Knowledge Integration Networks for Action Recognition","date":"2020-02-18","arxiv_id":"2002.07471","repositories_listed":0,"syntology":null},{"url":"/paper/hallucinating-statistical-moment-and-subspace","slug":"hallucinating-statistical-moment-and-subspace","title":"Self-supervising Action Recognition by Statistical Moment and Subspace Descriptors","date":"2020-01-14","arxiv_id":"2001.04627","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-data-augmentation-with-multi-domain","title":"Effective Data Augmentation with Multi-Domain Learning GANs","date":"2019-12-25","arxiv_id":"1912.11597","repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-recognition-based-on-dnn-and-game","title":"Scene recognition based on DNN and game theory with its applications in human-robot interaction","date":"2019-12-03","arxiv_id":"1912.01293","repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-recognition-with-prototype-agnostic","title":"Scene Recognition with Prototype-agnostic Scene Layout","date":"2019-09-07","arxiv_id":"1909.03234","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-sensor-hallucination-via-knowledge","title":"Online Sensor Hallucination via Knowledge Distillation for Multimodal Image Classification","date":"2019-08-28","arxiv_id":"1908.10559","repositories_listed":0,"syntology":null},{"url":"/paper/fosnet-an-end-to-end-trainable-deep-neural","slug":"fosnet-an-end-to-end-trainable-deep-neural","title":"FOSNet: An End-to-End Trainable Deep Neural Network for Scene Recognition","date":"2019-07-17","arxiv_id":"1907.07570","repositories_listed":0,"syntology":null},{"url":"/paper/hallucinating-bag-of-words-and-fisher-vector","slug":"hallucinating-bag-of-words-and-fisher-vector","title":"Hallucinating IDT Descriptors and I3D Optical Flow Features for Action Recognition with CNNs","date":"2019-06-13","arxiv_id":"1906.05910","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-domain-model-compression-by","title":"Cross Domain Model Compression by Structurally Weight Sharing","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tell-me-where-i-am-object-level-scene-context","title":"Tell Me Where I Am: Object-Level Scene Context Prediction","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"local-label-propagation-for-large-scale-semi","title":"Local Label Propagation for Large-Scale Semi-Supervised Learning","date":"2019-05-28","arxiv_id":"1905.11581","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-unsupervised-familiar-scene","title":"Towards Unsupervised Familiar Scene Recognition in Egocentric Videos","date":"2019-05-10","arxiv_id":"1905.04093","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-view-learning-using-neuron-wise","title":"Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers","date":"2019-04-25","arxiv_id":"1904.11151","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-aesthetics-assessment-using-composite","title":"Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models","date":"2019-02-22","arxiv_id":"1902.08546","repositories_listed":0,"syntology":null},{"url":null,"slug":"region-based-ensemble-learning-network-for","title":"Region based Ensemble Learning Network for Fine-grained Classification","date":"2019-02-09","arxiv_id":"1902.03377","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-area-transform-for-cross-modality","title":"Local Area Transform for Cross-Modality Correspondence Matching and Deep Scene Recognition","date":"2019-01-03","arxiv_id":"1901.00927","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuning-convolutional-neural-networks-for-1","title":"Fine-tuning Convolutional Neural Networks for fine art classification","date":"2018-12-30","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-supervised-dictionary-learning","title":"An efficient supervised dictionary learning method for audio signal recognition","date":"2018-12-12","arxiv_id":"1812.04748","repositories_listed":0,"syntology":null},{"url":null,"slug":"power-normalizing-second-order-similarity","title":"Power Normalizing Second-order Similarity Network for Few-shot Learning","date":"2018-11-10","arxiv_id":"1811.04167","repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-and-object-detection-for-phosphene","title":"Semantic and structural image segmentation for prosthetic vision","date":"2018-09-25","arxiv_id":"1809.09607","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-effective-rgb-d-representations-for","title":"Learning Effective RGB-D Representations for Scene Recognition","date":"2018-09-17","arxiv_id":"1809.06269","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-large-scale-dynamic-texture-dataset","title":"A New Large Scale Dynamic Texture Dataset with Application to ConvNet Understanding","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hgmr-hierarchical-gaussian-mixtures-for","title":"HGMR: Hierarchical Gaussian Mixtures for Adaptive 3D Registration","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchy-of-alternating-specialists-for","title":"Hierarchy of Alternating Specialists for Scene Recognition","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-accurate-point-cloud-registration","title":"Fast and Accurate Point Cloud Registration using Trees of Gaussian Mixtures","date":"2018-07-06","arxiv_id":"1807.02587","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deeper-look-at-power-normalizations","title":"A Deeper Look at Power Normalizations","date":"2018-06-24","arxiv_id":"1806.09183","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometry-guided-convolutional-neural-networks","title":"Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-less-data-diversified-subset","title":"Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks","date":"2018-05-28","arxiv_id":"1805.11191","repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-recognition-with-cnns-objects-scales","title":"Scene recognition with CNNs: objects, scales and dataset bias","date":"2018-01-21","arxiv_id":"1801.06867","repositories_listed":0,"syntology":null},{"url":null,"slug":"topological-map-construction-and-scene","title":"Topological map construction and scene recognition for vehicle localization","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"relating-input-concepts-to-convolutional","title":"Relating Input Concepts to Convolutional Neural Network Decisions","date":"2017-11-21","arxiv_id":"1711.08006","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-indoor-scene-recognition-method","title":"A Robust Indoor Scene Recognition Method based on Sparse Representation","date":"2017-08-24","arxiv_id":"1708.07555","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-metric-learning-for-optical","title":"Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization","date":"2017-08-04","arxiv_id":"1708.01494","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-selective-and-invariant-representation","title":"On the Selective and Invariant Representation of DCNN for High-Resolution Remote Sensing Image Recognition","date":"2017-08-04","arxiv_id":"1708.01420","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic-scene-recognition-based-on-deep-cnn","title":"Traffic scene recognition based on deep cnn and vlad spatial pyramids","date":"2017-07-24","arxiv_id":"1707.07411","repositories_listed":0,"syntology":null},{"url":null,"slug":"manifold-regularized-slow-feature-analysis","title":"Manifold Regularized Slow Feature Analysis for Dynamic Texture Recognition","date":"2017-06-09","arxiv_id":"1706.03015","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-do-we-learn-by-semantic-scene","title":"What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?","date":"2017-05-19","arxiv_id":"1705.07077","repositories_listed":0,"syntology":null},{"url":null,"slug":"recognizing-dynamic-scenes-with-deep-dual","title":"Recognizing Dynamic Scenes with Deep Dual Descriptor based on Key Frames and Key Segments","date":"2017-02-15","arxiv_id":"1702.04479","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-joint-mining-of-deep-features","title":"Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition","date":"2017-01-23","arxiv_id":"1701.06599","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-exploration-of-convolutional-fusion","title":"On the Exploration of Convolutional Fusion Networks for Visual Recognition","date":"2016-11-16","arxiv_id":"1611.05503","repositories_listed":0,"syntology":null},{"url":null,"slug":"audio-event-and-scene-recognition-a-unified","title":"Audio Event and Scene Recognition: A Unified Approach using Strongly and Weakly Labeled Data","date":"2016-11-12","arxiv_id":"1611.04871","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-multiple-cues-for-visual-madlibs","title":"Combining Multiple Cues for Visual Madlibs Question Answering","date":"2016-11-01","arxiv_id":"1611.00393","repositories_listed":0,"syntology":null},{"url":null,"slug":"indoor-space-recognition-using-deep","title":"Indoor Space Recognition using Deep Convolutional Neural Network: A Case Study at MIT Campus","date":"2016-10-07","arxiv_id":"1610.02414","repositories_listed":0,"syntology":null},{"url":null,"slug":"places-an-image-database-for-deep-scene","title":"Places: An Image Database for Deep Scene Understanding","date":"2016-10-06","arxiv_id":"1610.02055","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeing-with-humans-gaze-assisted-neural-image","title":"Seeing with Humans: Gaze-Assisted Neural Image Captioning","date":"2016-08-18","arxiv_id":"1608.05203","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-clustering-for-robust-fine-grained","title":"Semantic Clustering for Robust Fine-Grained Scene Recognition","date":"2016-07-26","arxiv_id":"1607.07614","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnn-lte-a-class-of-1-x-pooling-convolutional","title":"CNN-LTE: a Class of 1-X Pooling Convolutional Neural Networks on Label Tree Embeddings for Audio Scene Recognition","date":"2016-07-08","arxiv_id":"1607.02303","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-multi-robot-pose-graph-slam","title":"A Framework for Multi-Robot Pose Graph SLAM","date":"2016-06-06","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-multi-modal-feature-fusion-for","title":"Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-learning-of-scene-locations-via","title":"One-Shot Learning of Scene Locations via Feature Trajectory Transfer","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-the-contribution-of-central-versus","title":"Modeling the Contribution of Central Versus Peripheral Vision in Scene, Object, and Face Recognition","date":"2016-04-25","arxiv_id":"1604.07457","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-cnn-and-dictionary-based-models-for","title":"Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation","date":"2016-01-29","arxiv_id":"1601.07977","repositories_listed":0,"syntology":null},{"url":null,"slug":"locally-supervised-deep-hybrid-model-for","title":"Locally-Supervised Deep Hybrid Model for Scene Recognition","date":"2016-01-27","arxiv_id":"1601.07576","repositories_listed":0,"syntology":null},{"url":null,"slug":"visualizing-and-understanding-deep-texture","title":"Visualizing and Understanding Deep Texture Representations","date":"2015-11-16","arxiv_id":"1511.05197","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-exploiting-os-cnns-for-better-event","title":"Better Exploiting OS-CNNs for Better Event Recognition in Images","date":"2015-10-14","arxiv_id":"1510.03979","repositories_listed":0,"syntology":null},{"url":null,"slug":"amodal-completion-and-size-constancy-in","title":"Amodal Completion and Size Constancy in Natural Scenes","date":"2015-09-27","arxiv_id":"1509.08147","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-image-super-resolution-helpful-for-other","title":"Is Image Super-resolution Helpful for Other Vision Tasks?","date":"2015-09-23","arxiv_id":"1509.07009","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-feature-extraction-method-for-scene","title":"A Novel Feature Extraction Method for Scene Recognition Based on Centered Convolutional Restricted Boltzmann Machines","date":"2015-06-24","arxiv_id":"1506.07257","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-scene-recognition-based-on","title":"Natural Scene Recognition Based on Superpixels and Deep Boltzmann Machines","date":"2015-06-24","arxiv_id":"1506.07271","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-discriminative-representation-of","title":"A Discriminative Representation of Convolutional Features for Indoor Scene Recognition","date":"2015-06-17","arxiv_id":"1506.05196","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-locally-linear-knn-model-for-visual","title":"A Novel Locally Linear KNN Model for Visual Recognition","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-multi-feature-spatial-context-for-scene","title":"Joint Multi-Feature Spatial Context for Scene Recognition on the Semantic Manifold","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"more-about-vlad-a-leap-from-euclidean-to","title":"More About VLAD: A Leap From Euclidean to Riemannian Manifolds","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-see-by-moving","title":"Learning to See by Moving","date":"2015-05-07","arxiv_id":"1505.01596","repositories_listed":0,"syntology":null},{"url":"/paper/learning-deep-features-for-scene-recognition","slug":"learning-deep-features-for-scene-recognition","title":"Learning Deep Features for Scene Recognition using Places Database","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-convolutional-filter-banks-for-texture","title":"Deep convolutional filter banks for texture recognition and segmentation","date":"2014-11-25","arxiv_id":"1411.6836","repositories_listed":0,"syntology":null},{"url":null,"slug":"conceptlearner-discovering-visual-concepts","title":"ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections","date":"2014-11-19","arxiv_id":"1411.5328","repositories_listed":0,"syntology":null},{"url":null,"slug":"capturing-spatial-interdependence-in-image","title":"Capturing spatial interdependence in image features: the counting grid, an epitomic representation for bags of features","date":"2014-10-23","arxiv_id":"1410.6264","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-machine-crfs-for-identifying","title":"Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding","date":"2014-06-16","arxiv_id":"1406.3906","repositories_listed":0,"syntology":null},{"url":null,"slug":"bags-of-spacetime-energies-for-dynamic-scene","title":"Bags of Spacetime Energies for Dynamic Scene Recognition","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"orientational-pyramid-matching-for","title":"Orientational Pyramid Matching for Recognizing Indoor Scenes","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"models-of-semantic-representation-with-visual","title":"Models of Semantic Representation with Visual Attributes","date":"2013-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"is-bottom-up-attention-useful-for-scene","title":"Is Bottom-Up Attention Useful for Scene Recognition?","date":"2013-07-22","arxiv_id":"1307.5702","repositories_listed":0,"syntology":null}],"record_sha256":"7bab2dba35fcbca7c6066865bb027e4dfffafd6f72a4146a05c9c940b0430e5f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}