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Image Classification

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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Image Classification

논문

Transferring Visual Explainability from Self-Explaining to Prediction-Only Vision Transformers via Task Arithmetic

Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning

Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations

Federated Learning for Commercial Image Sources

MUPAX: Multidimensional Problem Agnostic eXplainable AI

Adversarial attacks to image classification systems using evolutionary algorithms

Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy

CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels

Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking

Transferring Styles for Reduced Texture Bias and Improved Robustness in Semantic Segmentation Networks

ZClassifier: Temperature Tuning and Manifold Approximation via KL Divergence on Logit Space

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation

Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning

Model-free Optical Processors using In Situ Reinforcement Learning with Proximal Policy Optimization

Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation

SoftReMish: A Novel Activation Function for Enhanced Convolutional Neural Networks for Visual Recognition Performance

MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture

FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection

Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual Descriptor

Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics

Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference

Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language Models

VisionGuard: Synergistic Framework for Helmet Violation Detection

FR-CapsNet: Enhancing Low-Resolution Image Classification via Frequency Routed Capsules

Hierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image Classification

Learning Moderately Input-Sensitive Functions: A Case Study in QR Code Decoding

Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networks

Counterfactual Influence as a Distributional Quantity

Disentangled representations of microscopy images

Iterative Quantum Feature Maps

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy

One Prototype Is Enough: Single-Prototype Activation for Interpretable Image Classification

SIM-Net: A Multimodal Fusion Network Using Inferred 3D Object Shape Point Clouds from RGB Images for 2D Classification

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation