Artificial neural networks and machine learning - ICANN 2024 Part 2
.- 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 convoluti...
Gespeichert in:
| Körperschaft: | |
|---|---|
| Weitere Verfasser: | , , , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Cham
Springer
2024
|
| Schriftenreihe: | Lecture notes in computer science
15017 |
| Schlagworte: |
Angewandte Informatik
> COM094000
> COMPUTERS / Computer Science
> COMPUTERS / Computer Vision & Pattern Recognition
> COMPUTERS / Hardware / Network Hardware
> COMPUTERS / Neural Networks
> Computer vision
> Information technology: general issues
> Machine learning
> Maschinelles Lernen
> Maschinelles Sehen, Bildverstehen
> Network hardware
> Netzwerk-Hardware
> Neural networks & fuzzy systems
> Neuronale Netze und Fuzzysysteme
> Konferenzschrift
|
| Online Zugang: | Cover |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Tag hinzufügen 
| Zusammenfassung: | .- 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 |
|---|---|
| Beschreibung: | Literaturangaben |
| Beschreibung: | xxxiv, 464 Seiten Illustrationen, Diagramme |
| ISBN: | 9783031723346 978-3-031-72334-6 |