Artificial Neural Networks and Machine Learning - ICANN 2024

.- 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.

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• As redes neurais e sistemas nebulosos
• Aprendizagem mecânica
• Visão por computador
• Tecnologia da informação: aspectos gerais
• Hardware de rede

artificial intelligence;classification;deep learning;generative models;graph neural networks;image processing;large language models;machine learning;neural networks;reinforcement learning;reservoir computing;robotics;spiking neural networks