OVS-DINO structurally aligns DINO with SAM to revitalize attenuated boundary features, achieving SOTA gains of 2.1% average and 6.3% on Cityscapes in weakly-supervised open-vocabulary segmentation.
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arXiv preprint arXiv:1511.08458 (2015)
11 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models are able to far exceed the performance of previous forms of artificial intelligence in common machine learning tasks. One of the most impressive forms of ANN architecture is that of the Convolutional Neural Network (CNN). CNNs are primarily used to solve difficult image-driven pattern recognition tasks and with their precise yet simple architecture, offers a simplified method of getting started with ANNs. This document provides a brief introduction to CNNs, discussing recently published papers and newly formed techniques in developing these brilliantly fantastic image recognition models. This introduction assumes you are familiar with the fundamentals of ANNs and machine learning.
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QMC-Net maps per-band statistics to customized quantum circuit hyperparameters and achieves 93.80% and 99.34% accuracy on EuroSAT and SAT-6, outperforming classical and monolithic quantum baselines.
AP-MAE reconstructs masked attention patterns in LLMs with high accuracy, generalizes across models, predicts generation correctness at 55-70%, and enables 13.6% accuracy gains via targeted interventions.
AlayaLaser uses a SIMD-optimized on-disk graph layout plus caching and search strategies to outperform prior on-disk ANNS systems and match or exceed in-memory performance on large high-dimensional datasets.
Short-time averages within experiments plus temporal-preserving models like CNNs cut multiphase mass flow metering errors to 4.3% MAPE on air-water-oil data, outperforming single-averaged baselines.
TemPose-TF-ASF fuses bidirectional stroke context in two stages to raise accuracy and Macro-F1 for badminton stroke classification over baselines.
SAGE-GAN integrates a self-attention U-Net into a CycleGAN framework to generate realistic synthetic electron microscopy image-mask pairs that augment training data for nanoparticle segmentation without human labeling.
Pre-trained ViT representations combined with active learning and targeted design choices for annotations and selection improve object class retrieval in multi-object scenes.
The paper overviews attention-based learning methods for spectrum cartography in LEO satellite networks to enable adaptive fusion of heterogeneous measurements for inference and resource allocation.
A survey providing an overview of publicly available image-based datasets for ML/DL-based disaster management pipelines covering pre-disaster, during, and post-disaster phases.
A 75 ms Gaussian window for segmenting phonocardiography signals yields the highest biLSTM classification accuracy among tested shapes and lengths, outperforming rectangular windows and a baseline method.
citing papers explorer
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OVS-DINO: Open-Vocabulary Segmentation via Structure-Aligned SAM-DINO with Language Guidance
OVS-DINO structurally aligns DINO with SAM to revitalize attenuated boundary features, achieving SOTA gains of 2.1% average and 6.3% on Cityscapes in weakly-supervised open-vocabulary segmentation.
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QMC-Net: Data-Aware Quantum Representations for Remote Sensing Image Classification
QMC-Net maps per-band statistics to customized quantum circuit hyperparameters and achieves 93.80% and 99.34% accuracy on EuroSAT and SAT-6, outperforming classical and monolithic quantum baselines.
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Automated Attention Pattern Discovery at Scale in Large Language Models
AP-MAE reconstructs masked attention patterns in LLMs with high accuracy, generalizes across models, predicts generation correctness at 55-70%, and enables 13.6% accuracy gains via targeted interventions.
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AlayaLaser: Efficient Index Layout and Search Strategy for Large-scale High-dimensional Vector Similarity Search
AlayaLaser uses a SIMD-optimized on-disk graph layout plus caching and search strategies to outperform prior on-disk ANNS systems and match or exceed in-memory performance on large high-dimensional datasets.
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Temporal Data and Short-Time Averages Improve Multiphase Mass Flow Metering
Short-time averages within experiments plus temporal-preserving models like CNNs cut multiphase mass flow metering errors to 4.3% MAPE on air-water-oil data, outperforming single-averaged baselines.
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TemPose-TF-ASF: Two-Stage Bidirectional Stroke Context Fusion for Badminton Stroke Classification
TemPose-TF-ASF fuses bidirectional stroke context in two stages to raise accuracy and Macro-F1 for badminton stroke classification over baselines.
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SAGE-GAN: Towards Realistic and Robust Segmentation of Spatially Ordered Nanoparticles via Attention-Guided GANs
SAGE-GAN integrates a self-attention U-Net into a CycleGAN framework to generate realistic synthetic electron microscopy image-mask pairs that augment training data for nanoparticle segmentation without human labeling.
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Revisiting Human-in-the-Loop Object Retrieval with Pre-Trained Vision Transformers
Pre-trained ViT representations combined with active learning and targeted design choices for annotations and selection improve object class retrieval in multi-object scenes.
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Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: An Overview
The paper overviews attention-based learning methods for spectrum cartography in LEO satellite networks to enable adaptive fusion of heterogeneous measurements for inference and resource allocation.
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Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications
A survey providing an overview of publicly available image-based datasets for ML/DL-based disaster management pipelines covering pre-disaster, during, and post-disaster phases.
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Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals
A 75 ms Gaussian window for segmenting phonocardiography signals yields the highest biLSTM classification accuracy among tested shapes and lengths, outperforming rectangular windows and a baseline method.