Artificial Neural Networks and Machine Learning - ICANN 2024 : 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part III (Lecture Notes in Computer Science) / Wand, Michael (EDT)/ Malinovská, Kristína (EDT)/ Schmidhuber, Jürgen (EDT)

Artificial Neural Networks and Machine Learning - ICANN 2024 : 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part III (Lecture Notes in Computer Science) （2024）

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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 III (Lecture Notes in Computer Science) （2024）

Wand, Michael (EDT)/ Malinovská, Kristína (EDT)/ Schmidhuber, Jürgen (EDT)

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Springer International Publishing AG（2024/10発売）
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製本 Paperback:紙装版/ペーパーバック版／ページ数 464 p.
商品コード 9783031723377

Full Description

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.

.- Computer Vision: Anomaly Detection.

.- Hybrid Encoder for Anomaly Detection Based on Latent Feature Regularization.

.- Computer Vision: Segmentation.

.- DGFormer: A Dynamic Kernel with Gaussian Fusion Transformer for Semantic Image Segmentation.

.- Integrating Audio-Visual Contexts with Refinement for Segmentation.

.- Loci-Segmented: Improving Scene Segmentation Learning.

.- Large Language Model for Action Anticipation.

.- MFPNet: A Multi-scale Feature Propagation Network for Lightweight Semantic Segmentation.

.- Weakly-Supervised Semantic Segmentation via Label Re-assignment in Dual-view Framework.

.- Computer Vision: Pose Estimation and Tracking.

.- DT2S-Pose: A Deeper Temporal-Spatial Skeleton Refine Model for Pedestrian Pose Estimation.

.- DTG: Learning A Dynamic Token Graph for 3D Pose Forecasting.

.- Dual-Branch Network with Online Knowledge Distillation for 3D Hand Pose Estimation.

.- MovePose: A High-performance Human Pose Estimation Algorithm on Mobile and Edge Devices.

.- Siamese visual tracking with correlation and awareness.

.- Computer Vision: Video Processing.

.- Alignment-Enhanced Network for Temporal Language Grounding in Videos.

.- Boundary-aware Noise-resistant Video Moment Retrieval.

.- Large Language Model for Action Anticipation.

.- Learning Object Permanence from Videos via Latent Imaginations.

.- SSFlowNet: Semi-supervised Scene Flow Estimation On Point Clouds With Pseudo Label.

.- Video Understanding Using 2D-CNNs on Salient Spatio-temporal Slices.

.- Computer Vision: Generative Methods.

.- A robust cycle generative adversarial network with an improved atmospheric scatter model for image dehazing.

.- CrossViewDiff: A Cross-View Diffusion Model for Satellite-to-Ground Image Synthesis.

.- Dual Dreamer: Extending Single-view Dreamer with Few shot of Complementary Views.

.- Hair Transfer with Efficient Heuristic Chain of Editing.

.- MAGIC: Multi-prompt Any length video Generation model with controllable Inter-frame Correlation and low barrier.

.- Make Audio Solely Drive Lip in Talking Face Video Synthesis.

.- P2H-GAN: An Effective Method For Generating Handwritten Expressions.

.- SCI-Font: Enhancing Content-Style Representation for Chinese Calligraphy Generation with Skeleton, Contour and Inexact Paired Data.

.- Topics in Computer Vision.

.- Driver Safety System: A Real-time Sleep Detection and Lane Detection Model using IoT and Deep Learning.

.- Gaze target detection with Visual Prompt Tuning based on attention.

.- Let Multi-Classification Help Deep Imbalanced Regression.

.- ProGEO: Generating Prompts through Image-Text Contrastive Learning for Visual Geo-localization.