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Object Recognition

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하위 태스크: 3
평가표: 9
보존된 평가 기록: 31

Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.

<span style="color:grey; opacity: 0.6">( Image credit: Tensorflow Object Detection API )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/hysonlab/pointdistill

공식 코드

Topology-Guided Knowledge Distillation for Efficient Point Cloud Processing

https://github.com/iamsulabh/cnn_variants

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Comparative evaluation of CNN architectures for Image Caption Generation

https://github.com/icvteam/part

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Part-guided Relational Transformers for Fine-grained Visual Recognition

https://github.com/ifp-uiuc/an-analysis-of-unsupervised-pre-training-iclr-2015

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An Analysis of Unsupervised Pre-training in Light of Recent Advances

https://github.com/ifp-uiuc/anna

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An Analysis of Unsupervised Pre-training in Light of Recent Advances

https://github.com/in-dist-adversarials/in_distribution_adversarial_examples

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In-distribution adversarial attacks on object recognition models using gradient-free search

https://github.com/isaaccorley/chesapeakersc

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Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery

https://github.com/j-min/dalleval

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DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models

https://github.com/jannctu/TIN

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Traditional Method Inspired Deep Neural Network for Edge Detection

https://github.com/jiangtaoxie/MPN-COV

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Is Second-order Information Helpful for Large-scale Visual Recognition?

https://github.com/jiayi-wu-umd/marvis

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MARVIS: Motion & Geometry Aware Real and Virtual Image Segmentation

https://github.com/jiexuanyan/cprfl

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Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification

https://github.com/jinfengx/sasep

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SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds

https://github.com/jingweimo/food-image-classification-

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Food Image Recognition by Using Convolutional Neural Networks (CNNs)

https://github.com/jlezama/OrthogonalLowrankEmbedding

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OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning

https://github.com/jmiemirza/dua

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The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization

https://github.com/joeyy5588/vrl

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Verbalized Representation Learning for Interpretable Few-Shot Generalization

https://github.com/jorisguerin/SemanticViewSelection_dataset

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Semantically Meaningful View Selection

https://github.com/js05212/BayesianDeepLearning-Survey

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A Survey on Bayesian Deep Learning

https://github.com/julimueller/disparity-sliding-window

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Disparity Sliding Window: Object Proposals From Disparity Images

https://github.com/jungeumkim/sense

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Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classification

https://github.com/jwhitehill/compositionalclustering

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Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker Identification

https://github.com/jylinok/freedrawingdetecting

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Multi-area Target Individual Detection with Free Drawing on Video

https://github.com/kaibrach/Moment-Propagation

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Single Shot MC Dropout Approximation

https://github.com/kaiyuyue/nxtp

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Object Recognition as Next Token Prediction

https://github.com/kellyiss/situformer

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Rethinking the Two-Stage Framework for Grounded Situation Recognition

https://github.com/kmi-robots/object_reasoner

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Fit to Measure: Reasoning about Sizes for Robust Object Recognition

https://github.com/koala719/bvit

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BViT: Broad Attention based Vision Transformer

https://github.com/kobayashikanna01/probing_MLLM_rep

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Probing Multimodal Large Language Models for Global and Local Semantic Representations

https://github.com/kreimanlab/Put-In-Context

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Putting visual object recognition in context

https://github.com/kreimanlab/RefixationModel

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Look Twice: A Generalist Computational Model Predicts Return Fixations across Tasks and Species

https://github.com/krishkribo/3d_gdm-rson

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Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization Networks

https://github.com/ksgr5566/unseggnet

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UnSegGNet: Unsupervised Image Segmentation using Graph Neural Networks

https://github.com/kunglab/ddnn

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Distributed Deep Neural Networks over the Cloud, the Edge and End Devices

https://github.com/laiyingxin2/shield

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SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models

https://github.com/lanfenghuanyu/Sill-Net

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Sill-Net: Feature Augmentation with Separated Illumination Representation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.