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/wq2012/SimpleMatrix
공식 코드Feature Learning by Multidimensional Scaling and its Applications in Object Recognition
https://github.com/wuyxin/disc
공식 코드Discover and Cure: Concept-aware Mitigation of Spurious Correlation
https://github.com/wyddmw/WiseAD
공식 코드WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language Model
https://github.com/wzell/cmd
공식 코드Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
https://github.com/wzell/mann
공식 코드Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment
https://github.com/xguo7/MDCC-for-open-world-recognition
공식 코드Multi-stage Deep Classifier Cascades for Open World Recognition
https://github.com/xiangyu-gao/radar-multiple-perspective-object-detection
공식 코드RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition
https://github.com/xingruiwang/spatial457
공식 코드Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models
https://github.com/xmu-qcj/e2pnet
공식 코드E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning
https://github.com/xmu-xiaoma666/lstnet
공식 코드https://github.com/xud-yan/Trident
공식 코드Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot Learning
https://github.com/xvjiarui/GCNet
공식 코드GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
https://github.com/yangyanli/FPNN
공식 코드https://github.com/yilundu/irem_code_release
공식 코드https://github.com/yuhed/proxymix
공식 코드ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation
https://github.com/yzhou359/3DIndoor-SceneGraphNet
공식 코드SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation
https://github.com/yzlianghik2022/ssranking-for-object-ba
공식 코드https://github.com/zbs881314/Temporal-Coded-Deep-SNN
공식 코드Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance
https://github.com/zeeshankaleem/ghostautoanchornet
공식 코드GAANet: Ghost Auto Anchor Network for Detecting Varying Size Drones in Dark
https://github.com/zengsn/research
공식 코드https://github.com/zhangyy708/closure-in-CNNs
공식 코드Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks
https://github.com/zubair-irshad/shapo
공식 코드FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects
https://github.com/zycheiheihei/transferable-visual-prompting
공식 코드Exploring the Transferability of Visual Prompting for Multimodal Large Language Models
https://worksheets.codalab.org/worksheets/0xbcd424d2bf544c4786efcc0063759b1a
공식 코드https://bitbucket.org/woalsdnd/codes_and_data
Training Deep Neural Networks on Noisy Labels with Bootstrapping
https://github.com/0492wzl/tensorflow_slim_densenet
https://github.com/0h-n0/tfdbonas
https://github.com/0h-n0/thdbonas
https://github.com/0jason000/DenseNet
https://github.com/0jason000/GoogleNet
https://github.com/1170500804/tbmsl
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
https://github.com/2023-MindSpore-1/ms-code-146
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/3D_DenseNet
https://github.com/2023-MindSpore-1/ms-code-161
https://github.com/2023-MindSpore-1/ms-code-211/tree/main/DeepID
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.