https://github.com/cshizhe/hgr_v2t
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Cross-Modal Retrieval (CMR) is a task of retrieving items across different modalities, such as image, text, video, and audio. The core challenge of CMR is the heterogeneity gap, which arises because data from different modalities have distinct representations, making direct comparison difficult. To address this, most CMR methods focus on learning a shared latent embedding space. In this space, concepts from different modalities are projected, allowing their similarity to be measured using a distance metric.
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.
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
Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.