Image Classification

3715 papers with code • 165 benchmarks • 239 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

The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision

and-mill/2024-sponge-example-analysis 27 Mar 2024

We empirically examine our findings in a comprehensive evaluation with multiple image classification models and show that our attack achieves the same sparsity effect as prior sponge-example methods, but at a fraction of computation effort.

0
27 Mar 2024

PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition

chenhongyiyang/plainmamba 26 Mar 2024

In this paper, we further adapt the selective scanning process of Mamba to the visual domain, enhancing its ability to learn features from two-dimensional images by (i) a continuous 2D scanning process that improves spatial continuity by ensuring adjacency of tokens in the scanning sequence, and (ii) direction-aware updating which enables the model to discern the spatial relations of tokens by encoding directional information.

14
26 Mar 2024

The Need for Speed: Pruning Transformers with One Recipe

skhaki18/optin-transformer-pruning 26 Mar 2024

We introduce the $\textbf{O}$ne-shot $\textbf{P}$runing $\textbf{T}$echnique for $\textbf{I}$nterchangeable $\textbf{N}$etworks ($\textbf{OPTIN}$) framework as a tool to increase the efficiency of pre-trained transformer architectures $\textit{without requiring re-training}$.

9
26 Mar 2024

Tiny Models are the Computational Saver for Large Models

QingyuanWang/tinysaver 26 Mar 2024

By searching and employing the most appropriate tiny model as the computational saver for a given large model, the proposed approaches work as a novel and generic method to model compression.

0
26 Mar 2024

DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks

frankkramer-lab/deepgleason 25 Mar 2024

Our tool contributes to the wider adoption of AI-based Gleason grading within the research community and paves the way for broader clinical application of deep learning models in digital pathology.

1
25 Mar 2024

Task2Box: Box Embeddings for Modeling Asymmetric Task Relationships

cvl-umass/task2box 25 Mar 2024

Modeling and visualizing relationships between tasks or datasets is an important step towards solving various meta-tasks such as dataset discovery, multi-tasking, and transfer learning.

0
25 Mar 2024

Histogram Layers for Neural Engineered Features

advanced-vision-and-learning-lab/nehd_nlbp 25 Mar 2024

These engineered features include local binary patterns and edge histogram descriptors among others and they have been shown to be informative features for a variety of computer vision tasks.

0
25 Mar 2024

CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data

shreyasharma99/cbgt-net 24 Mar 2024

This paper describes CBGT-Net, a neural network model inspired by the cortico-basal ganglia-thalamic (CBGT) circuits found in mammalian brains.

1
24 Mar 2024

iDAT: inverse Distillation Adapter-Tuning

jcruan519/idat 23 Mar 2024

Adapter-Tuning (AT) method involves freezing a pre-trained model and introducing trainable adapter modules to acquire downstream knowledge, thereby calibrating the model for better adaptation to downstream tasks.

5
23 Mar 2024

VLM-CPL: Consensus Pseudo Labels from Vision-Language Models for Human Annotation-Free Pathological Image Classification

lanfz2000/vlm-cpl 23 Mar 2024

To address this issue, we introduce VLM-CPL, a novel approach based on consensus pseudo labels that integrates two noisy label filtering techniques with a semi-supervised learning strategy.

0
23 Mar 2024