Artificial Neural Networks and Machine Learning - ICANN 2024
Artificial Neural Networks and Machine Learning - ICANN 2024
33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part II
Wand, Michael; Tetko, Igor V.; Malinovska, Kristina; Schmidhuber, Juergen
Springer International Publishing AG
10/2024
464
Mole
9783031723346
Pré-lançamento - envio 15 a 20 dias após a sua edição
.- 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.
.- 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.