Computer Vision
 
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Image Classification

3772 papers with code • 168 benchmarks • 240 datasets

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

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Coreset Selection for Object Detection

no code yet • 14 Apr 2024

Coreset selection is a method for selecting a small, representative subset of an entire dataset.

 
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Exploring Explainability in Video Action Recognition

no code yet • 13 Apr 2024

To address these, we introduce Video-TCAV, by building on TCAV for Image Classification tasks, which aims to quantify the importance of specific concepts in the decision-making process of Video Action Recognition models.

 
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PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification

no code yet • 13 Apr 2024

The other is to classification on feature distribution of visual tokens from vision encoder.

 
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A Survey of Neural Network Robustness Assessment in Image Recognition

no code yet • 12 Apr 2024

We investigate the perturbation metrics and range representations used to measure the degree of perturbations on images, as well as the robustness metrics specifically for the robustness conditions of classification models.

 
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SpectralMamba: Efficient Mamba for Hyperspectral Image Classification

no code yet • 12 Apr 2024

Recurrent neural networks and Transformers have recently dominated most applications in hyperspectral (HS) imaging, owing to their capability to capture long-range dependencies from spectrum sequences.

 
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Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example

no code yet • 12 Apr 2024

To address the challenges of dependence on pathologists expertise and the time-consuming nature of achieving accurate breast pathological image classification, this paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images, aiming to enhance the efficiency of breast pathological image detection.

 
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Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase Sampling

no code yet • 11 Apr 2024

Downsampling operators break the shift invariance of convolutional neural networks (CNNs) and this affects the robustness of features learned by CNNs when dealing with even small pixel-level shift.

 
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Learning to Classify New Foods Incrementally Via Compressed Exemplars

no code yet • 11 Apr 2024

Therefore, food image classification systems should adapt to and manage data that continuously evolves.

 
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CopilotCAD: Empowering Radiologists with Report Completion Models and Quantitative Evidence from Medical Image Foundation Models

no code yet • 11 Apr 2024

Computer-aided diagnosis systems hold great promise to aid radiologists and clinicians in radiological clinical practice and enhance diagnostic accuracy and efficiency.

 
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Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark

no code yet • 10 Apr 2024

This method aims to mitigate forgetting while adapting to new classes and domain shifts by combining the advantages of the Replay and Pseudo-Label methods and solving their limitations in the proposed scenario.

 
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