https://github.com/asankagp/mod20
공식 코드A Multi-viewpoint Outdoor Dataset for Human Action Recognition
Papers with Code 아카이브
Object Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/asbroad/geom_rcnn
공식 코드Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop Objects
https://github.com/ashu1069/perceptual-piercing
공식 코드Perceptual Piercing: Human Visual Cue-based Object Detection in Low Visibility Conditions
https://github.com/ashwinitonge/deepprivate
공식 코드https://github.com/asmaa4may/DeTraC_COVId19
공식 코드Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
https://github.com/aspek1/QuadricCurvature
공식 코드A Fast Method For Computing Principal Curvatures From Range Images
https://github.com/asucicilab/explainable-geoai
공식 코드https://github.com/bafanas/dlc-object-recognition-analysis
공식 코드https://github.com/baxiyi/dut-useg
공식 코드https://github.com/bespontaneous/mca-pytorch
공식 코드Don't Judge by the Look: Towards Motion Coherent Video Representation
https://github.com/bethgelab/model-vs-human
공식 코드Partial success in closing the gap between human and machine vision
https://github.com/bethgelab/notorious_difficulty_of_comparing_human_and_machine_perception
공식 코드Five Points to Check when Comparing Visual Perception in Humans and Machines
https://github.com/biomedia-mira/masf
공식 코드Domain Generalization via Model-Agnostic Learning of Semantic Features
https://github.com/brain-aalto/unusual_poses
공식 코드A comparison between humans and AI at recognizing objects in unusual poses
https://github.com/brown-palm/object-states
공식 코드https://github.com/btgraham/SparseConvNet
공식 코드https://github.com/buildingamind/vit-cot
공식 코드Are Vision Transformers More Data Hungry Than Newborn Visual Systems?
https://github.com/c0ldstudy/pointsecguard
공식 코드On Adversarial Robustness of Point Cloud Semantic Segmentation
https://github.com/ceciliacasolo/cip
공식 코드https://github.com/cest-andre/residual-stream-interp
공식 코드Naturally Computed Scale Invariance in the Residual Stream of ResNet18
https://github.com/cfederer/TrainCNNsWithNeuralData
공식 코드https://github.com/chaoma99/HCFTstar
공식 코드Robust Visual Tracking via Hierarchical Convolutional Features
https://github.com/chenghan111/dnc
공식 코드https://github.com/chihhuiho/VISPE
공식 코드Exploit Clues from Views: Self-Supervised and Regularized Learning for Multiview Object Recognition
https://github.com/chohoonhee/ev-lafor
공식 코드Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from Events
https://github.com/chrisyu-zz/pure-event-based-recognition-based-llm
공식 코드Can Large Language Models Grasp Event Signals? Exploring Pure Zero-Shot Event-based Recognition
https://github.com/cipriancorneanu/dnn-topology
공식 코드https://github.com/clear-nus/TactileSGNet
공식 코드TactileSGNet: A Spiking Graph Neural Network for Event-based Tactile Object Recognition
https://github.com/cmsflash/efficient-attention
공식 코드https://github.com/comp-syn/comp-syn
공식 코드Divergences in Color Perception between Deep Neural Networks and Humans
https://github.com/contextualai/lens
공식 코드Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language
https://github.com/croitorualin/cbm
공식 코드https://github.com/cvlab-columbia/ct4recognition
공식 코드https://github.com/cvlab-columbia/doubleright
공식 코드Doubly Right Object Recognition: A Why Prompt for Visual Rationales
https://github.com/cvlab-columbia/pix2gestalt
공식 코드https://github.com/cxqnet/masseg
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.