Papers Image Classification
“Image Classification” 태그가 달린 논문 11,344편 · 필터 해제
CrossGMN: Graph Metanetworks for Cross-Architecture Weight-Space Transformations
Weight-space networks operate directly on parameters of other neural networks, enabling tasks such as predicting model properties, editing trained models, and generating weights. Weight-space symmetries such as neuron pe…
Knowledge DistillationImage ClassificationModel CompressionControllable Stochastic Quantization Encoding for Adversarially Robust Spiking Neural Networks
Spiking Neural Networks (SNNs) have attracted increasing attention due to their impressive temporal dynamics, energy efficiency, and brain-inspired mechanisms. Although SNNs have demonstrated promising performance in ima…
Adversarial RobustnessImage ClassificationIntBMoE: Integrating Block-Level Conditioning into Expert Composition for Full-Participation Mixture-of-Experts
Mixture-of-Experts (MoE) scales capacity, but existing designs cannot set three quantities independently. For a single token, participation is how many experts contribute knowledge to its output, execution is how many ar…
Sequential RecommendationImage ClassificationPiPS: Post-Hoc Prototypical Explanations for Interpretable Semantic Segmentation
With the increasing deployment of deep neural networks in critical systems, such as medical diagnostics and autonomous vehicles, ensuring their interpretability is crucial to building trust in decision-making systems. In…
Semantic SegmentationImage ClassificationObject LocalizationAutonomous VehiclesTAPe+ML: A Compact Structured Representation for Multi-Task Computer Vision
We present TAPe+ML v3, a compact computer vision system based on TAPe (Theory of Active Perception), a structured representation that encodes relations among perceptual elements before recognition. Instead of operating d…
Instance SegmentationImage ClassificationObject LocalizationObject DetectionEnhanced Deformable Convolution with Center-invariant Offset and Edge-aware Mask
Deformable convolution networks have recently become popular for many computer vision tasks, especially for semantic segmentation, because of their exceptional capabilities in dynamic spatial modeling. However, due to th…
Semantic SegmentationImage ClassificationEdge DetectionOne Loop, Two Gains: Can Active Learning win the Lottery for Free?
The lottery ticket hypothesis posits the existence of winning tickets: sparse subnetworks that, when trained in isolation from their original initialization, match the accuracy of the full dense network. The predominant …
Image ClassificationActive LearningLayerwise Tunable Lifting Scheme for the Convolutional Neural Network
This work introduces a family of tunable lifting schemes for biorthogonal wavelet filter banks. We propose three lifting strategies: low-pass tuning (LS-LayLatt-LP), high-pass tuning (LS-LayLatt-HP), and a sequential lif…
Image ClassificationAnomaly DetectionAn Attention-Guided Global and Local Fusion Framework for Lesion-Focused Image Classification
Lesion-focused image classification presents a core analytical challenge, as discriminative signals are often sparse, spatially dispersed, and easily obscured by background noise, while conventional convolutional neural …
Image ClassificationQuantum-Grassmann-Plucker Token Mixing for Deep Learning-Based Post-Disaster Damage Assessment
Timely post-disaster building damage assessment from satellite imagery is a critical engineering decision support task, yet it remains constrained by class imbalance, ambiguous intermediate damage states, and limited cro…
Building Damage AssessmentQuantum Machine LearningImage ClassificationConCA: Concentration-Aware Channel Attention for Fine-Grained Visual Recognition
Lightweight channel attention mechanisms are widely used in image classification, yet their effectiveness in fine-grained visual recognition (FGVR) remains limited. Most modules summarize each channel by global average p…
Fine-Grained Visual RecognitionImage ClassificationTexture Image Classification Using DWT AlexNet Feature Fusion and Deep Neural Networks
Texture image classification plays a significant role in computer vision applications, including industrial inspection, medical image analysis, remote sensing, and object recognition. Handcrafted features can capture loc…
Image ClassificationObject RecognitionQuantumBoostNet: A Hybrid Classical-Quantum Architecture for Enhanced Accuracy in Cardiac Ultrasound View Identification
Accurate identification of the correct view or angle in cardiac ultrasound (echocardiogram) is a critical component of cardiologic imaging. This step is essential for precise anatomical interpretation, reliable measureme…
Image ClassificationBeyond Classification: Task-Dependent Learnability under Privacy-Motivated Image Transformations
Privacy-Enhancing Technologies (PETs) in computer vision often rely on noise or image perturbations to protect visual data while securely processing it, creating a trade-off between task performance and protection. This …
Image ClassificationLearning from waste: Machine Learning for health risk prediction and computer vision-based sorting in Ghana
The inappropriate disposal of solid waste remains a significant public health and environmental concern worldwide, including in Ghana. Poor sanitation and improper waste management practices contribute to substantial eco…
Image ClassificationSemi-Supervised Adaptation of Vision-Language Models for Image Classification
Vision-language models like CLIP have shown sig- nificant potential in handling natural images, yet their perfor- mance is often limited by the distinct characteristics of satellite imagery. While parameter-efficient ada…
Scene ClassificationImage ClassificationPrivacy-Preserving Object Detection for Vision Transformer-Based Models
We propose a novel object detection method that enables us to protect sensitive visual information of test images. Previous studies considering visual information protection focus on image classification tasks. This pape…
Novel Object DetectionImage ClassificationDomain AdaptationLearning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets
Small-scale image classification is often limited by the scarcity of training data. Generative data augmentation (GDA) based on pretrained generative models has emerged as an effective solution. However, existing methods…
Image ClassificationData AugmentationAccuracy and Robustness of Model Cascades Under Data Perturbations
Prediction cascades significantly reduce energy consumption of Artificial Intelligence (AI) models while maintaining high predictive performance. The idea is that easy inputs are routed through a lightweight small model,…
Computational EfficiencyImage ClassificationIs Haar Enough? Exploring Symlets and Coiflets for Wavelet Convolution Layers
Wavelet convolution layers have recently emerged as an efficient mechanism for enlarging receptive fields through multiresolution analysis, but prior work has fixed the wavelet basis to Haar or Daubechies at a chosen dec…
Semantic SegmentationImage Classification