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[null,"1.","Record Nr.",null,"UNINA9910887885503321"]
[null,null,"Autore",null,"Wand Michael"]
[null,null,"Titolo",null,"Artificial Neural Networks and Machine Learning – ICANN 2024 : 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part II / / edited by Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"3-031-72335-X"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (486 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 15017"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computers"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computing Milieux"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Computer Communication Networks"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"-- Computer Vision: Classification.  -- A WEAKLY SUPERVISED PART DETECTION METHOD FOR ROBUST FINE-GRAINED CLASSIFICATION.  -- An Energy Sampling Replay-Based Continual Learning Framework.  -- Coarse-to-Fine Granularity in MultiScale FeatureFusion Network for SAR Ship Classification. .-Multi-scale convolutional attention fuzzy broad network for few-shot hyperspectral image classification.  -- Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification.  -- Computer Vision: Object Detection.  -- CIA-Net:Cross-modal Interaction and Depth Quality-Aware Network for RGB-D Salient Object Detection.  -- CPH DETR: Comprehensive Regression Loss for End-to-End Object Detection.  -- DecoratingFusion: A LiDAR-Camera Fusion Network with the Combination of Point-level and Feature-level Fusion.  -- EMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection.  -- Global-Guided Weighted Enhancement for Salient Object Detection.  -- KDNet: Leveraging Vision-Language Knowledge Distillation for Few-Shot Object Detection.  -- MUFASA: Multi-View "]
[{"$nulls":4},"Fusion and Adaptation Network with Spatial Awareness for Radar Object Detection.  -- One-Shot Object Detection with 4D-Correlation and 4D-Attention.  -- Small Object Detection Based on Bidirectional Feature Fusion and Multi-scale Distillation. .-SRA-YOLO: Spatial Resolution Adaptive YOLO for Semi-Supervised Cross-Domain Aerial Object Detection.  -- Computer Vision: Security and Adversarial Attacks.  -- BiFAT: Bilateral Filtering and Attention Mechanisms in a Two-Stream Model for Deepfake Detection.  -- EL-FDL: Improving Image Forgery Detection and Localization via Ensemble Learning.  -- Generalizable Deepfake Detection with Unbiased Feature Extraction and Low-level Forgery Enhancement.  -- Generative Universal Nullifying Perturbation for Countering Deepfakes through Combined Unsupervised Feature Aggregation.  -- Noise-NeRF: Hide Information in Neural Radiance Field using Trainable Noise.  -- Unconventional Face Adversarial Attack. Computer Vision: Image EnhancementComputer Vision: Image Enhancement.  -- Computer Vision: Image Enhancement.  -- A Study in Dataset Pruning for Image Super-Resolution.  -- EDAFormer:Enhancing Low-Light Images with a Dual-Attention Transformer.  -- Image Matting Based on Deep Equilibrium Models.  -- Computer Vision: 3D Methods.  -- ControlNeRF: Text-Driven 3D Scene Stylization via Diffusion Model.  -- Interactive Color Manipulation in NeRF: A Point Cloud and Palette-driven Approach.  -- Multimodal Monocular Dense Depth Estimation with Event-Frame Fusion using Transformer.  -- SAM-NeRF: NeRF-based 3D Instance Segmentation with Segment Anything Model.  -- Towards High-Accuracy Point Cloud Registration with Channel Self-Attention and Angle Invariance."]
[null,null,null,"Sommario/riassunto","The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks."]