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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/rmkemker/RIT-18

공식 코드

Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning

https://github.com/roads/psiz

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Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings

https://github.com/roboflow-ai/roboflow-100-benchmark

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Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark

https://github.com/row11n/prova

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Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection

https://github.com/roysubhankar/dwt-domain-adaptation

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Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss

https://github.com/ruotianluo/Context-aware-ZSR

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Context-Aware Zero-Shot Recognition

https://github.com/safouaneelg/copulasimilarity

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CSIM: A Copula-based similarity index sensitive to local changes for Image quality assessment

https://github.com/sagerpascal/dynamic-link-architecture

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The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patterns

https://github.com/sai-lab-nyu/foveated-instance-segmentation

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Foveated Instance Segmentation

https://github.com/sarkar-rohan/objectswithstatechange

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A Dataset and Framework for Learning State-invariant Object Representations

https://github.com/sarthak268/sample-efficient-visual-concept-learning

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Sample-Efficient Learning of Novel Visual Concepts

https://github.com/sayantondibbo/sca

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Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures

https://github.com/sckangz/RGC

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Robust Graph Learning from Noisy Data

https://github.com/selgroup/sit-hss

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Hierarchical Superpixel Segmentation via Structural Information Theory

https://github.com/selimfirat/multilingual-sentiment-analysis

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Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label Imbalance

https://github.com/serre-lab/VPT

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The 3D-PC: a benchmark for visual perspective taking in humans and machines

https://github.com/shanshuo/MVT

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MVT: Multi-view Vision Transformer for 3D Object Recognition

https://github.com/shanshuo/R2-MLP

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R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition

https://github.com/shantanuj/tdam_top_down_attention_module

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TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs

https://github.com/shaox192/NeuralGuidance

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Probing Human Visual Robustness with Neurally-Guided Deep Neural Networks

https://github.com/shen-aoyu/CREST

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CREST: An Efficient Conjointly-trained Spike-driven Framework for Event-based Object Detection Exploiting Spatiotemporal Dynamics

https://github.com/shijiangming1/clip2fl

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CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data

https://github.com/shikhartuli/cnn_txf_bias

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Are Convolutional Neural Networks or Transformers more like human vision?

https://github.com/shivram1987/adanorm

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AdaNorm: Adaptive Gradient Norm Correction based Optimizer for CNNs

https://github.com/shoaibahmed/investigating_3d_generalization

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Investigating the Nature of 3D Generalization in Deep Neural Networks

https://github.com/siddk/vqa-outliers

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Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

https://github.com/slab-nlp/multimodal_clustering

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A Computational Acquisition Model for Multimodal Word Categorization

https://github.com/smartslab/THOR2

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THOR2: Topological Analysis for 3D Shape and Color-Based Human-Inspired Object Recognition in Unseen Environments

https://github.com/smartslab/objectRecognitionTopologicalFeatures

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Visual Object Recognition in Indoor Environments Using Topologically Persistent Features

https://github.com/smartslab/thor

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Persistent Homology Meets Object Unity: Object Recognition in Clutter

https://github.com/snehaputul/allmatch

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Improving 3D Semi-supervised Learning by Effectively Utilizing All Unlabelled Data

https://github.com/spandan-madan/in_distribution_adversarial_examples

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

https://github.com/stevenlauhkhk/large-separable-kernel-attention

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Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN

https://github.com/stusynakowski/Unsup_Recognition_of_Intent

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Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent

https://github.com/supernlogn/squeezeDetTL

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A Framework of Transfer Learning in Object Detection for Embedded Systems

https://github.com/surrey-uplab/recognize-any-regions

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Recognize Any Regions

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