Papers with Code 아카이브
Zero-Shot Image Classification
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Zero-shot image classification is a technique in computer vision where a model can classify images into categories that were not present during training. This is achieved by leveraging semantic information about the categories, such as textual descriptions or relationships between classes.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mendelxu/zsseg.baseline
공식 코드A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
https://github.com/miccunifi/cross-the-gap
공식 코드Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion
https://github.com/mitsuichen14/lrsclip
공식 코드LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing Image with Longer Text
https://github.com/mvrl/taxabind
공식 코드TaxaBind: A Unified Embedding Space for Ecological Applications
https://github.com/ofa-sys/chinese-clip
공식 코드Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
https://github.com/pallgeuer/novic
공식 코드https://github.com/pallgeuer/object_noun_dictionary
공식 코드https://github.com/paulgavrikov/vlm_shapebias
공식 코드https://github.com/rsCPSyEu/ovd_cod
공식 코드https://github.com/salesforce/must
공식 코드Masked Unsupervised Self-training for Label-free Image Classification
https://github.com/sarahesl/alignclip
공식 코드Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIP
https://github.com/sarahpratt/cupl
공식 코드What does a platypus look like? Generating customized prompts for zero-shot image classification
https://github.com/sauradip/stale
공식 코드Zero-Shot Temporal Action Detection via Vision-Language Prompting
https://github.com/shivam-chandhok/cpsc-540-project
공식 코드Do Vision-Language Foundational models show Robust Visual Perception?
https://github.com/tanganke/fusion_bench
공식 코드https://github.com/tensorflow/tpu
공식 코드Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
https://github.com/tensorflow/tpu/tree/master/models/official/detection/projects/vild
공식 코드Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
https://github.com/umd-huang-lab/perceptionclip
공식 코드PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts
https://github.com/wusize/clipself
공식 코드CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction
https://github.com/xuanlinli17/large_vlm_distillation_ood
공식 코드Distilling Large Vision-Language Model with Out-of-Distribution Generalizability
https://github.com/yuxie11/R2D2
공식 코드https://github.com/zhengli97/promptkd
공식 코드PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
https://github.com/zjukg/DUET
공식 코드DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning
https://github.com/zjukg/dozsl
공식 코드https://github.com/zjukg/kgtransformer
공식 코드Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
https://github.com/0jason000/wukong
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark
https://github.com/LiYinqi/DIVE
https://github.com/MicPie/clasp
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
https://github.com/dazhangyu123/acmil
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration
https://github.com/donatoaz/what-do-you-see-zero-shot-image-classification-multimodal-llm
What Do You See? Enhancing Zero-Shot Image Classification with Multimodal Large Language Models
https://github.com/dyabel/detpro
Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
https://github.com/eify/clip_benchmark
https://github.com/eify/open_clip
Reproducible scaling laws for contrastive language-image learning
https://github.com/eric-ai-lab/pevit
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/hanoonaR/object-centric-ovd
Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.