{"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-classification/papers/2","list_of":"/task/scene-classification","task":"Scene Classification","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":5,"rows_per_page":100,"rows":[101,200],"of":453,"counts":{"archive_papers_tagged":453,"with_a_code_link":148,"where_syntology_ran_a_sample":22,"not_listed_spam_title":0,"listed":453,"listed_where_code_ran":22,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":19,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":19,"listed_every_run_a_failure_of_syntologys_instrument":3,"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-classification","prev":"/task/scene-classification","next":"/task/scene-classification/papers/3","papers":[{"url":"/paper/low-complexity-acoustic-scene-classification","slug":"low-complexity-acoustic-scene-classification","title":"Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systems","date":"2021-05-28","arxiv_id":"2105.13734","repositories_listed":1,"syntology":null},{"url":"/paper/receptive-field-regularization-techniques-for","slug":"receptive-field-regularization-techniques-for","title":"Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks","date":"2021-05-26","arxiv_id":"2105.12395","repositories_listed":1,"syntology":null},{"url":"/paper/spectrum-correction-acoustic-scene","slug":"spectrum-correction-acoustic-scene","title":"Spectrum Correction: Acoustic Scene Classification with Mismatched Recording Devices","date":"2021-05-25","arxiv_id":"2105.11856","repositories_listed":1,"syntology":null},{"url":"/paper/remote-sensing-image-classification-with-the","slug":"remote-sensing-image-classification-with-the","title":"Remote Sensing Image Classification with the SEN12MS Dataset","date":"2021-04-01","arxiv_id":"2104.00704","repositories_listed":1,"syntology":null},{"url":"/paper/translate-to-adapt-rgb-d-scene-recognition","slug":"translate-to-adapt-rgb-d-scene-recognition","title":"Multi-Modal RGB-D Scene Recognition Across Domains","date":"2021-03-26","arxiv_id":"2103.14672","repositories_listed":1,"syntology":null},{"url":"/paper/msmatch-semi-supervised-multispectral-scene","slug":"msmatch-semi-supervised-multispectral-scene","title":"MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels","date":"2021-03-18","arxiv_id":"2103.10368","repositories_listed":1,"syntology":null},{"url":"/paper/low-complexity-models-for-acoustic-scene","slug":"low-complexity-models-for-acoustic-scene","title":"Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping","date":"2020-11-05","arxiv_id":"2011.02955","repositories_listed":1,"syntology":null},{"url":"/paper/a-two-stage-approach-to-device-robust","slug":"a-two-stage-approach-to-device-robust","title":"A Two-Stage Approach to Device-Robust Acoustic Scene Classification","date":"2020-11-03","arxiv_id":"2011.01447","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-two-stage-approach-to-device-robust#ran","syntology_url":"https://syntology.ai/paper/2011.01447","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.01447"}},"official":{"repos":["MihawkHu/DCASE2020_task1"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/approxdet-content-and-contention-aware","slug":"approxdet-content-and-contention-aware","title":"ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles","date":"2020-10-21","arxiv_id":"2010.10754","repositories_listed":1,"syntology":null},{"url":"/paper/what-can-you-learn-from-your-muscles-learning","slug":"what-can-you-learn-from-your-muscles-learning","title":"What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions","date":"2020-10-16","arxiv_id":"2010.08539","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/what-can-you-learn-from-your-muscles-learning#ran","syntology_url":"https://syntology.ai/paper/2010.08539","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.08539"}},"official":{"repos":["ehsanik/muscleTorch"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mlrsnet-a-multi-label-high-spatial-resolution","slug":"mlrsnet-a-multi-label-high-spatial-resolution","title":"MLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene Understanding","date":"2020-10-01","arxiv_id":"2010.00243","repositories_listed":1,"syntology":null},{"url":"/paper/dcasenet-a-joint-pre-trained-deep-neural","slug":"dcasenet-a-joint-pre-trained-deep-neural","title":"DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and events","date":"2020-09-21","arxiv_id":"2009.09642","repositories_listed":1,"syntology":null},{"url":"/paper/daer-to-reject-seeds-with-dual-loss","slug":"daer-to-reject-seeds-with-dual-loss","title":"Ground-truth or DAER: Selective Re-query of Secondary Information","date":"2020-09-16","arxiv_id":"2009.07414","repositories_listed":1,"syntology":null},{"url":"/paper/citisen-a-deep-learning-based-speech-signal","slug":"citisen-a-deep-learning-based-speech-signal","title":"CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application","date":"2020-08-21","arxiv_id":"2008.09264","repositories_listed":1,"syntology":null},{"url":"/paper/device-robust-acoustic-scene-classification","slug":"device-robust-acoustic-scene-classification","title":"Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation","date":"2020-07-16","arxiv_id":"2007.08389","repositories_listed":1,"syntology":null},{"url":"/paper/dirs-on-creating-benchmark-datasets-for","slug":"dirs-on-creating-benchmark-datasets-for","title":"On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID","date":"2020-06-22","arxiv_id":"2006.12485","repositories_listed":1,"syntology":null},{"url":"/paper/multi-temporal-scene-classification-and-scene","slug":"multi-temporal-scene-classification-and-scene","title":"Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion","date":"2020-06-03","arxiv_id":"2006.02176","repositories_listed":1,"syntology":null},{"url":"/paper/a-multiple-instance-densely-connected-convnet","slug":"a-multiple-instance-densely-connected-convnet","title":"A multiple-instance densely-connected ConvNet for aerial scene classification","date":"2020-03-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/vision-based-fight-detection-from","slug":"vision-based-fight-detection-from","title":"Vision-based Fight Detection from Surveillance Cameras","date":"2020-02-11","arxiv_id":"2002.04355","repositories_listed":1,"syntology":null},{"url":"/paper/deliberative-explanations-visualizing-network","slug":"deliberative-explanations-visualizing-network","title":"Deliberative Explanations: visualizing network insecurities","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/in-domain-representation-learning-for-remote-1","slug":"in-domain-representation-learning-for-remote-1","title":"In-domain representation learning for remote sensing","date":"2019-11-15","arxiv_id":"1911.06721","repositories_listed":1,"syntology":null},{"url":"/paper/centroid-based-scene-classification-cbsc","slug":"centroid-based-scene-classification-cbsc","title":"Centroid Based Concept Learning for RGB-D Indoor Scene Classification","date":"2019-11-01","arxiv_id":"1911.00155","repositories_listed":1,"syntology":null},{"url":"/paper/deep-metric-learning-based-feature-embedding","slug":"deep-metric-learning-based-feature-embedding","title":"Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification","date":"2019-10-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/emotion-and-theme-recognition-in-music-with","slug":"emotion-and-theme-recognition-in-music-with","title":"Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs","date":"2019-10-28","arxiv_id":"1911.05833","repositories_listed":1,"syntology":null},{"url":"/paper/acoustic-scene-analysis-with-multi-head","slug":"acoustic-scene-analysis-with-multi-head","title":"Acoustic scene analysis with multi-head attention networks","date":"2019-09-16","arxiv_id":"1909.08961","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-aware-scene-recognition","slug":"semantic-aware-scene-recognition","title":"Semantic-Aware Scene Recognition","date":"2019-09-05","arxiv_id":"1909.02410","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semantic-aware-scene-recognition#ran","syntology_url":"https://syntology.ai/paper/1909.02410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.02410"}},"official":{"repos":["vpulab/Semantic-Aware-Scene-Recognition"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/exploiting-parallel-audio-recordings-to","slug":"exploiting-parallel-audio-recordings-to","title":"Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification","date":"2019-09-04","arxiv_id":"1909.02869","repositories_listed":1,"syntology":null},{"url":"/paper/city-classification-from-multiple-real-world","slug":"city-classification-from-multiple-real-world","title":"City classification from multiple real-world sound scenes","date":"2019-07-29","arxiv_id":"1905.00979","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-adversarial-domain-adaptation-1","slug":"unsupervised-adversarial-domain-adaptation-1","title":"Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification","date":"2019-04-24","arxiv_id":"1904.10678","repositories_listed":1,"syntology":null},{"url":"/paper/acoustic-scene-classification-by-implicitly","slug":"acoustic-scene-classification-by-implicitly","title":"Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events","date":"2019-04-10","arxiv_id":"1904.05204","repositories_listed":1,"syntology":null},{"url":"/paper/equivariant-multi-view-networks","slug":"equivariant-multi-view-networks","title":"Equivariant Multi-View Networks","date":"2019-04-01","arxiv_id":"1904.00993","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/equivariant-multi-view-networks#ran","syntology_url":"https://syntology.ai/paper/1904.00993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.00993"}},"official":{"repos":["daniilidis-group/emvn"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/understanding-and-visualizing-deep-visual","slug":"understanding-and-visualizing-deep-visual","title":"Understanding and Visualizing Deep Visual Saliency Models","date":"2019-03-06","arxiv_id":"1903.02501","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/understanding-and-visualizing-deep-visual#ran","syntology_url":"https://syntology.ai/paper/1903.02501","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.02501"}},"official":{"repos":["SenHe/uavdvsm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-multi-task-learning-with-rock-a","slug":"revisiting-multi-task-learning-with-rock-a","title":"Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/siftinggan-generating-and-sifting-labeled","slug":"siftinggan-generating-and-sifting-labeled","title":"SiftingGAN: Generating and Sifting Labeled Samples to Improve the Remote Sensing Image Scene Classification Baseline in vitro","date":"2018-09-13","arxiv_id":"1809.04985","repositories_listed":1,"syntology":null},{"url":"/paper/geolocation-estimation-of-photos-using-a","slug":"geolocation-estimation-of-photos-using-a","title":"Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification","date":"2018-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-adversarial-domain-adaptation","slug":"unsupervised-adversarial-domain-adaptation","title":"Unsupervised adversarial domain adaptation for acoustic scene classification","date":"2018-08-17","arxiv_id":"1808.05777","repositories_listed":1,"syntology":null},{"url":"/paper/towards-automatic-initialization-of","slug":"towards-automatic-initialization-of","title":"Towards automatic initialization of registration algorithms using simulated endoscopy images","date":"2018-06-28","arxiv_id":"1806.10748","repositories_listed":1,"syntology":null},{"url":"/paper/hexaconv","slug":"hexaconv","title":"HexaConv","date":"2018-03-06","arxiv_id":"1803.02108","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hexaconv#ran","syntology_url":"https://syntology.ai/paper/1803.02108","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.02108"}},"official":{"repos":["ehoogeboom/hexaconv"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deepcorrect-correcting-dnn-models-against","slug":"deepcorrect-correcting-dnn-models-against","title":"DeepCorrect: Correcting DNN models against Image Distortions","date":"2017-05-05","arxiv_id":"1705.02406","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-multi-label-feature-selection","slug":"semi-supervised-multi-label-feature-selection","title":"Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embedding","date":"2017-03-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mapping-between-fmri-responses-to-movies-and","slug":"mapping-between-fmri-responses-to-movies-and","title":"Mapping Between fMRI Responses to Movies and their Natural Language Annotations","date":"2016-10-13","arxiv_id":"1610.03914","repositories_listed":1,"syntology":null},{"url":"/paper/what-makes-imagenet-good-for-transfer","slug":"what-makes-imagenet-good-for-transfer","title":"What makes ImageNet good for transfer learning?","date":"2016-08-30","arxiv_id":"1608.08614","repositories_listed":1,"syntology":null},{"url":"/paper/aid-a-benchmark-dataset-for-performance","slug":"aid-a-benchmark-dataset-for-performance","title":"AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification","date":"2016-08-18","arxiv_id":"1608.05167","repositories_listed":1,"syntology":null},{"url":"/paper/classifying-variable-length-audio-files-with","slug":"classifying-variable-length-audio-files-with","title":"Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global Pooling","date":"2016-07-11","arxiv_id":"1607.02857","repositories_listed":1,"syntology":null},{"url":"/paper/towards-better-exploiting-convolutional","slug":"towards-better-exploiting-convolutional","title":"Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification","date":"2016-02-04","arxiv_id":"1602.01517","repositories_listed":1,"syntology":null},{"url":"/paper/relay-backpropagation-for-effective-learning","slug":"relay-backpropagation-for-effective-learning","title":"Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks","date":"2015-12-18","arxiv_id":"1512.05830","repositories_listed":1,"syntology":null},{"url":"/paper/object-detectors-emerge-in-deep-scene-cnns","slug":"object-detectors-emerge-in-deep-scene-cnns","title":"Object Detectors Emerge in Deep Scene CNNs","date":"2014-12-22","arxiv_id":"1412.6856","repositories_listed":1,"syntology":null},{"url":"/paper/parsing-natural-scenes-and-natural-language","slug":"parsing-natural-scenes-and-natural-language","title":"Parsing Natural Scenes and Natural Language with Recursive Neural Networks","date":"2011-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":null,"slug":"towards-scalable-and-generalizable-earth","title":"Towards Scalable and Generalizable Earth Observation Data Mining via Foundation Model Composition","date":"2025-06-25","arxiv_id":"2506.20174","repositories_listed":0,"syntology":null},{"url":null,"slug":"earthsynth-generating-informative-earth","title":"EarthSynth: Generating Informative Earth Observation with Diffusion Models","date":"2025-05-17","arxiv_id":"2505.12108","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11418","title":"Energy efficiency analysis of Spiking Neural Networks for space applications","date":"2025-05-16","arxiv_id":"2505.11418","repositories_listed":0,"syntology":null},{"url":null,"slug":"minimizing-risk-through-minimizing-model-data","title":"Minimizing Risk Through Minimizing Model-Data Interaction: A Protocol For Relying on Proxy Tasks When Designing Child Sexual Abuse Imagery Detection Models","date":"2025-05-10","arxiv_id":"2505.06621","repositories_listed":0,"syntology":null},{"url":null,"slug":"frogdognet-fourier-frequency-retained-visual","title":"FrogDogNet: Fourier frequency Retained visual prompt Output Guidance for Domain Generalization of CLIP in Remote Sensing","date":"2025-04-23","arxiv_id":"2504.16433","repositories_listed":0,"syntology":null},{"url":null,"slug":"fleximo-a-flexible-remote-sensing-foundation","title":"FlexiMo: A Flexible Remote Sensing Foundation Model","date":"2025-03-31","arxiv_id":"2503.23844","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-vocabulary-semantic-segmentation-with-3","title":"Open-Vocabulary Semantic Segmentation with Uncertainty Alignment for Robotic Scene Understanding in Indoor Building Environments","date":"2025-03-29","arxiv_id":"2503.23105","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-time-tells-us-an-explorative-study-of","title":"What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images","date":"2025-03-23","arxiv_id":"2503.17899","repositories_listed":0,"syntology":null},{"url":null,"slug":"city2scene-improving-acoustic-scene","title":"Improving Acoustic Scene Classification with City Features","date":"2025-03-21","arxiv_id":"2503.16862","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-transport-adapter-tuning-for-bridging","title":"Optimal Transport Adapter Tuning for Bridging Modality Gaps in Few-Shot Remote Sensing Scene Classification","date":"2025-03-19","arxiv_id":"2503.14938","repositories_listed":0,"syntology":null},{"url":null,"slug":"georsmllm-a-multimodal-large-language-model","title":"GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing","date":"2025-03-16","arxiv_id":"2503.12490","repositories_listed":0,"syntology":null},{"url":null,"slug":"creating-a-good-teacher-for-knowledge","title":"Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification","date":"2025-03-14","arxiv_id":"2503.11363","repositories_listed":0,"syntology":null},{"url":null,"slug":"meet-a-million-scale-dataset-for-fine-grained","title":"MEET: A Million-Scale Dataset for Fine-Grained Geospatial Scene Classification with Zoom-Free Remote Sensing Imagery","date":"2025-03-14","arxiv_id":"2503.11219","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-classification-head-self-training","title":"Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification","date":"2025-02-25","arxiv_id":"2502.17879","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-bayesian-adaptive-learning-of","title":"Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer","date":"2025-01-26","arxiv_id":"2501.15496","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-vision-language-framework-for-multispectral","title":"A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features","date":"2025-01-17","arxiv_id":"2501.10144","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-enhanced-transformers-for-robust","title":"Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments","date":"2025-01-16","arxiv_id":"2501.09394","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-label-scene-classification-in-remote","title":"Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution","date":"2025-01-12","arxiv_id":"2501.06720","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-language-models-for-autonomous-driving","title":"Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding","date":"2025-01-09","arxiv_id":"2501.05566","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-scene-classification-in-cloudy","title":"Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing Images","date":"2025-01-08","arxiv_id":"2501.04283","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedrsclip-federated-learning-for-remote","title":"FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models","date":"2025-01-05","arxiv_id":"2501.02461","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-acoustic-scene-classification-in","title":"Improving Acoustic Scene Classification in Low-Resource Conditions","date":"2024-12-30","arxiv_id":"2412.20722","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-certain-are-uncertainty-estimates-three","title":"How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning","date":"2024-12-09","arxiv_id":"2412.06451","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-remote-sensing-scene","title":"Multimodal Remote Sensing Scene Classification Using VLMs and Dual-Cross Attention Networks","date":"2024-12-03","arxiv_id":"2412.02531","repositories_listed":0,"syntology":null},{"url":null,"slug":"aerial-flood-scene-classification-using-fine","title":"Aerial Flood Scene Classification Using Fine-Tuned Attention-based Architecture for Flood-Prone Countries in South Asia","date":"2024-10-31","arxiv_id":"2411.00169","repositories_listed":0,"syntology":null},{"url":null,"slug":"neurobench-dcase-2020-acoustic-scene","title":"Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2","date":"2024-10-31","arxiv_id":"2410.23776","repositories_listed":0,"syntology":null},{"url":null,"slug":"less-yet-robust-crucial-region-selection-for","title":"Less yet robust: crucial region selection for scene recognition","date":"2024-09-23","arxiv_id":"2409.14741","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-efficient-acoustic-scene-classification","title":"Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction","date":"2024-09-18","arxiv_id":"2409.11964","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-clustering-of-remote-sensing-scenes","title":"Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learning","date":"2024-09-05","arxiv_id":"2409.03938","repositories_listed":0,"syntology":null},{"url":null,"slug":"audio-enhancement-for-computer-audition-an","title":"Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance","date":"2024-08-12","arxiv_id":"2408.06264","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-foundation-models-in-remote-sensing-a","title":"AI Foundation Models in Remote Sensing: A Survey","date":"2024-08-06","arxiv_id":"2408.03464","repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-artistic-scene-centric-datasets-for","title":"Novel Artistic Scene-Centric Datasets for Effective Transfer Learning in Fragrant Spaces","date":"2024-07-16","arxiv_id":"2407.11701","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-domain-incremental-learning-approach","title":"Online Domain-Incremental Learning Approach to Classify Acoustic Scenes in All Locations","date":"2024-06-19","arxiv_id":"2406.13386","repositories_listed":0,"syntology":null},{"url":null,"slug":"vwise-a-novel-benchmark-for-evaluating-scene","title":"VWise: A novel benchmark for evaluating scene classification for vehicular applications","date":"2024-06-05","arxiv_id":"2406.03273","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-label-propagation-strategy-for-cutmix-in","title":"A Label Propagation Strategy for CutMix in Multi-Label Remote Sensing Image Classification","date":"2024-05-22","arxiv_id":"2405.13451","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-space-separable-distillation-for","title":"Deep Space Separable Distillation for Lightweight Acoustic Scene Classification","date":"2024-05-06","arxiv_id":"2405.03567","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-toolchain-for-comprehensive-audio-video","title":"A Toolchain for Comprehensive Audio/Video Analysis Using Deep Learning Based Multimodal Approach (A use case of riot or violent context detection)","date":"2024-05-02","arxiv_id":"2407.03110","repositories_listed":0,"syntology":null},{"url":null,"slug":"pretraining-billion-scale-geospatial","title":"Pretraining Billion-scale Geospatial Foundational Models on Frontier","date":"2024-04-17","arxiv_id":"2404.11706","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-object-based-and-segmentation","title":"Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classification","date":"2024-04-11","arxiv_id":"2404.07739","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-feature-communication-in-federated","title":"Leveraging feature communication in federated learning for remote sensing image classification","date":"2024-03-20","arxiv_id":"2403.13575","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-self-supervised-learning-for-scene","title":"Leveraging Self-Supervised Learning for Scene Classification in Child Sexual Abuse Imagery","date":"2024-03-02","arxiv_id":"2403.01183","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-aware-neuron-interpretation-for","title":"Knowledge-Aware Neuron Interpretation for Scene Classification","date":"2024-01-29","arxiv_id":"2401.15820","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-adaptive-learning-to-latent","title":"Bayesian adaptive learning to latent variables via Variational Bayes and Maximum a Posteriori","date":"2024-01-24","arxiv_id":"2401.13766","repositories_listed":0,"syntology":null},{"url":null,"slug":"digital-divides-in-scene-recognition","title":"Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems","date":"2024-01-23","arxiv_id":"2401.13097","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-volumetric-saliency-guided-image","title":"A Volumetric Saliency Guided Image Summarization for RGB-D Indoor Scene Classification","date":"2024-01-19","arxiv_id":"2401.16227","repositories_listed":0,"syntology":null},{"url":null,"slug":"kronecker-product-feature-fusion-for","title":"Kronecker Product Feature Fusion for Convolutional Neural Network in Remote Sensing Scene Classification","date":"2024-01-08","arxiv_id":"2402.00036","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-assisted-3d-scene-understanding","title":"Language-Assisted 3D Scene Understanding","date":"2023-12-18","arxiv_id":"2312.11451","repositories_listed":0,"syntology":null},{"url":"/paper/cartomark-a-benchmark-dataset-for-map-pattern","slug":"cartomark-a-benchmark-dataset-for-map-pattern","title":"CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence","date":"2023-12-14","arxiv_id":"2312.08600","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascade-learning-localises-discriminant","title":"Cascade Learning Localises Discriminant Features in Visual Scene Classification","date":"2023-11-21","arxiv_id":"2311.12704","repositories_listed":0,"syntology":null},{"url":null,"slug":"unlocking-the-capabilities-of-explainable","title":"Unlocking the capabilities of explainable fewshot learning in remote sensing","date":"2023-10-12","arxiv_id":"2310.08619","repositories_listed":0,"syntology":null},{"url":null,"slug":"locality-preserving-directions-for","title":"Locality-preserving Directions for Interpreting the Latent Space of Satellite Image GANs","date":"2023-09-26","arxiv_id":"2309.14883","repositories_listed":0,"syntology":null},{"url":null,"slug":"mvp-meta-visual-prompt-tuning-for-few-shot","title":"MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification","date":"2023-09-17","arxiv_id":"2309.09276","repositories_listed":0,"syntology":null}],"record_sha256":"92af04606163cf319cdcc2ab5f3990d19b0035485818b33edbcb944f2ce71a24","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}