https://github.com/priscillalui/StackGAN-Stories
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
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.
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Dual-Path Convolutional Image-Text Embeddings with Instance Loss
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
Semantic-Conditional Diffusion Networks for Image Captioning
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models
Deep Visual-Semantic Alignments for Generating Image Descriptions
Merlin: A Vision Language Foundation Model for 3D Computed Tomography
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
VinVL: Revisiting Visual Representations in Vision-Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.