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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/sustech-isus/sustechgan

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SUSTechGAN: Image Generation for Object Detection in Adverse Conditions of Autonomous Driving

https://github.com/tacju/partimagenet

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PartImageNet: A Large, High-Quality Dataset of Parts

https://github.com/tensorflow/models/tree/master/research/lstm_object_detection

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Looking Fast and Slow: Memory-Guided Mobile Video Object Detection

https://github.com/thomasgolda/Human-Pose-Estimation-for-Real-World-Crowded-Scenarios

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Human Pose Estimation for Real-World Crowded Scenarios

https://github.com/thuml/debiased-self-training

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Debiased Self-Training for Semi-Supervised Learning

https://github.com/tim-learn/SHOT-plus

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Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer

https://github.com/triumph123aaa/grid-augmented-vision

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Grid-augmented vision: A simple yet effective approach for enhanced spatial understanding in multi-modal agents

https://github.com/tsborkar/DeepCorrect

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DeepCorrect: Correcting DNN models against Image Distortions

https://github.com/tteepe/tracktacular

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Lifting Multi-View Detection and Tracking to the Bird's Eye View

https://github.com/tztztztztz/eql.detectron2

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Equalization Loss for Long-Tailed Object Recognition

https://github.com/uni-medical/imis-bench

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Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline

https://github.com/uoguelph-mlrg/theano_alexnet

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Theano-based Large-Scale Visual Recognition with Multiple GPUs

https://github.com/uoguelph-mlrg/theano_multi_gpu

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Theano-based Large-Scale Visual Recognition with Multiple GPUs

https://github.com/utsavgarg/cnn-fixations

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CNN Fixations: An unraveling approach to visualize the discriminative image regions

https://github.com/utsavgarg/fast-feature-fool

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Fast Feature Fool: A data independent approach to universal adversarial perturbations

https://github.com/uzh-rpg/event-based_vision_resources

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v2e: From Video Frames to Realistic DVS Events

https://github.com/uzh-rpg/event_representation_study

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From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection

https://github.com/uzh-rpg/rpg_asynet

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Event-based Asynchronous Sparse Convolutional Networks

https://github.com/uzh-rpg/rpg_event_representation_learning

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End-to-End Learning of Representations for Asynchronous Event-Based Data

https://github.com/uzh-rpg/rpg_vid2e

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Video to Events: Recycling Video Datasets for Event Cameras

https://github.com/val-iisc/cnn-fixations

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CNN Fixations: An unraveling approach to visualize the discriminative image regions

https://github.com/val-iisc/gd-uap

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Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations

https://github.com/val-iisc/sketch-obj-rec

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Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition

https://github.com/val-iisc/swiden

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SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition

https://github.com/valeriob88/gestalt-dnns

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Mixed Evidence for Gestalt Grouping in Deep Neural Networks

https://github.com/vignywang/awesome-local-feature-matching

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Local Feature Matching Using Deep Learning: A Survey

https://github.com/virajprabhu/pacmac

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Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency

https://github.com/vittorioerba/pyFCI

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Intrinsic dimension estimation for locally undersampled data

https://github.com/vlislab22/Deep-Learning-for-Event-based-Vision

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Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks

https://github.com/voanna/eccentricity

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Do Deep Neural Networks Suffer from Crowding?

https://github.com/wanglimin/Places205-VGGNet

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Places205-VGGNet Models for Scene Recognition

https://github.com/waterdisappear/nudt4mstar

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NUDT4MSTAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild

https://github.com/wichmann-lab/error-consistency

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Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency

https://github.com/wichmann-lab/robustness-development

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The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networks

https://github.com/wkentaro/safepicking

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SafePicking: Learning Safe Object Extraction via Object-Level Mapping

https://github.com/wkfdb/marvelovd

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MarvelOVD: Marrying Object Recognition and Vision-Language Models for Robust Open-Vocabulary Object Detection

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