Papers with Code 아카이브

Object Categorization

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 0
평가표: 1
보존된 평가 기록: 4

Object categorization identifies which label, from a given set, best corresponds to an image region defined by an input image and bounding box.

코드

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

https://github.com/MRLoghmani/rcfusion

공식 코드

Recurrent Convolutional Fusion for RGB-D Object Recognition

https://github.com/YBZh/PartNet

공식 코드

Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Network

https://github.com/YudiXie/multitask-vision

공식 코드

Vision CNNs trained to estimate spatial latents learned similar ventral-stream-aligned representations

https://github.com/aimerykong/coRFL

공식 코드

Collaborative Receptive Field Learning

https://github.com/alexhernandezgarcia/data-aug-invariance

공식 코드

Data augmentation instead of explicit regularization

https://github.com/allenai/grit_official

공식 코드

GRIT: General Robust Image Task Benchmark

https://github.com/blue-blue272/ImgReID-IAnet

공식 코드

IAUnet: Global Context-Aware Feature Learning for Person Re-Identification

https://github.com/cfederer/TrainCNNsWithNeuralData

공식 코드

Improved object recognition using neural networks trained to mimic the brain's statistical properties

https://github.com/dicarlolab/cornet

공식 코드

Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs

https://github.com/dicarlolab/neurips2019

공식 코드

Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs

https://github.com/ducha-aiki/caffenet-benchmark

공식 코드

Systematic evaluation of CNN advances on the ImageNet

https://github.com/github-songsong/fine-grained-pointcloud-object-dataset

공식 코드

Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN Models

https://github.com/kanezaki/rotationnet

공식 코드

RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints

https://github.com/kietzmannlab/svrhm21_rnn_explain

공식 코드

Category-orthogonal object features guide information processing in recurrent neural networks trained for object categorization

https://github.com/ofa-sys/ofa

공식 코드

OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

https://github.com/openai/CLIP

공식 코드

Learning Transferable Visual Models From Natural Language Supervision

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

공식 코드

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

https://github.com/xiaomeiyy/wmm-voc

공식 코드

Vocabulary-informed Zero-shot and Open-set Learning

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