Computer Vision
 
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Image Classification

3788 papers with code • 142 benchmarks • 238 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

Libraries

Use these libraries to find Image Classification models and implementations

Datasets

Subtasks

InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification

kunzhan/infomatch • • 17 Apr 2024

To address this, we employ information entropy neural estimation to utilize the potential of unlabeled samples.

20
17 Apr 2024
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Vocabulary-free Image Classification and Semantic Segmentation

altndrr/vicss • • 16 Apr 2024

To address VIC, we propose Category Search from External Databases (CaSED), a training-free method that leverages a pre-trained vision-language model and an external database.

3
16 Apr 2024
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A variable metric proximal stochastic gradient method: an application to classification problems

koblererich/lisavm • • EURO Journal on Computational Optimization 2024

To control the variance of the objective's gradients, we use an automatic sample size selection along with a variable metric to precondition the stochastic gradient directions.

0
15 Apr 2024
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Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification

dhuseljic/dal-toolbox • • 13 Apr 2024

BAIT, a recently proposed AL strategy based on the Fisher Information, has demonstrated impressive performance across various datasets.

3
13 Apr 2024
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SpectralMamba: Efficient Mamba for Hyperspectral Image Classification

danfenghong/spectralmamba • 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.

26
12 Apr 2024
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HGRN2: Gated Linear RNNs with State Expansion

sustcsonglin/flash-linear-attention • • 11 Apr 2024

Hierarchically gated linear RNN (HGRN, Qin et al. 2023) has demonstrated competitive training speed and performance in language modeling, while offering efficient inference.

448
11 Apr 2024
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Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classification

faceonlive/ai-research • 11 Apr 2024

Recent advancements in deep learning have proven highly effective in medical image classification, notably within histopathology.

161
11 Apr 2024
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On adversarial training and the 1 Nearest Neighbor classifier

faceonlive/ai-research • 9 Apr 2024

The ability to fool deep learning classifiers with tiny perturbations of the input has lead to the development of adversarial training in which the loss with respect to adversarial examples is minimized in addition to the training examples.

161
09 Apr 2024
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Variational Stochastic Gradient Descent for Deep Neural Networks

generativeai-tue/vsgd • • 9 Apr 2024

We model gradient updates as a probabilistic model and utilize stochastic variational inference (SVI) to derive an efficient and effective update rule.

1
09 Apr 2024
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Allowing humans to interactively guide machines where to look does not always improve human-AI team's classification accuracy

anguyen8/chm-corr-interactive • • 8 Apr 2024

We build CHM-Corr++, an interactive interface for CHM-Corr, enabling users to edit the feature importance map provided by CHM-Corr and observe updated model decisions.

2
08 Apr 2024
Paper
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