https://github.com/camma-public/surgvlp
Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation
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.
Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation
VinVL: Revisiting Visual Representations in Vision-Language Models
An Empirical Study of Training End-to-End Vision-and-Language Transformers
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-modal Retrieval
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images
Dual Attention Networks for Multimodal Reasoning and Matching
Merlin: A Vision Language Foundation Model for 3D Computed Tomography
An Empirical Study of Training End-to-End Vision-and-Language Transformers
X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.