https://github.com/AlexMoreo/tensorflow-Tex2Vis
공식 코드Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Cross-Modal Information Retrieval (CMIR) is the task of finding relevant items across different modalities. For example, given an image, find a text or vice versa. The main challenge in CMIR is known as the heterogeneity gap: since items from different modalities have different data types, the similarity between them cannot be measured directly. Therefore, the majority of CMIR methods published to date attempt to bridge this gap by learning a latent representation space, where the similarity between items from different modalities can be measured.
Source: Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders
CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing
Learning the Best Pooling Strategy for Visual Semantic Embedding
Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.