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Artificial Neural Networks and Machine Learning - ICANN 2024 = 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024 : proceedings.. Part II /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Artificial Neural Networks and Machine Learning - ICANN 2024/ edited by Michael Wand ... [et al.].
其他題名:	33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024 : proceedings.
其他題名:	ICANN 2024
其他作者:	Wand, Michael.
團體作者:	Workshop on the Preservation of Stability under Discretization
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xxxiv, 464 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Neural networks (Computer science) -
電子資源:	https://doi.org/10.1007/978-3-031-72335-3
ISBN:	9783031723353

Artificial Neural Networks and Machine Learning - ICANN 2024 = 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024 : proceedings.. Part II /
Artificial Neural Networks and Machine Learning - ICANN 202433rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024 : proceedings.Part II /[electronic resource] :ICANN 2024edited by Michael Wand ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xxxiv, 464 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,150170302-9743 ;. - Lecture notes in computer science,7131. .

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 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.

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.

ISBN: 9783031723353

Standard No.: 10.1007/978-3-031-72335-3doiSubjects--Topical Terms:

528588
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Artificial Neural Networks and Machine Learning - ICANN 2024 = 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024 : proceedings.. Part II /
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