• Datasets
  • Image-Text
  • Video-Text
• Papers and Code
  • 2024 Papers
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  • 2019 Papers
    
    

1. [ACL-16] Multi30K(multi-lingual version of Filickr30K)-[English|German|French|Czech]: Multi30K: Multilingual English-German Image Descriptions. [paper] [dataset]
2. MSCOCO-[English|Chinese|Japanese]:

• (English) [ARXIV-15] Microsoft COCO Captions: Data Collection and Evaluation Server. [paper] [dataset]
• (Chinese) [TMM-19] COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval. [paper] [dataset]
• (Japanese) [ACL-17] STAIR Captions:Constructing a Large-Scale Japanese Image Caption Dataset. [paper] [dataset]

3. CC3M(mutli-lingual version) [dataset]
4. Wukong(Chinese) [dataset]

1. [ICCV-19] VATEX-[English|Chinese]: VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research. [Paper] [dataset]
2. [ACM MM-22] MSRVTT-CN(multi-lingual version of MSRVTT)-[English|Chinese]: Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning. [paper] [dataset]

Note: This repository provides English captions and other language(Machine-translation version) captions of Multi30K, MSCOCO, VATEX, and MSRVTT-CN.

• [Wang et al. TIP] Dual-view Curricular Optimal Transport for Cross-lingual Cross-modal Retrieval. [paper]
• [Wang et al. AAAI] CL2CM: Improving Cross-Lingual Cross-Modal Retrieval via Cross-Lingual Knowledge Transfer. [paper]
• [Cai et al. TKDE] Cross-Lingual Cross-Modal Retrieval with Noise-Robust Fine-Tuning. [paper]

• [Zeng et al. ACL] Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training. [paper] [code]
• [Li et al. ACL] Unifying Cross-Lingual and Cross-Modal Modeling Towards Weakly Supervised Multilingual Vision-Language Pre-training. [paper]
• [Rouditchenko et al. ICASSP] C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval. [paper] [code]

• [Wang et al. ACM MM] Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning. [paper] [code]

• [Zhou et al. CVPR21] UC2:Universal Cross-lingual Cross-modal Vision-and-Language Pre-training. [paper] [code]
• [Ni et al. CVPR21] M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training. [paper] [code]
• [Huang et al. NAACL21] Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models. [paper]
• [Fei et al. NAACL21] Cross-lingual Cross-modal Pretraining for Multimodal Retrieval. [paper]

• [Aggarwal et al. ARXIV] Towards Zero-shot Cross-lingual Image Retrieval. [paper]

• [Portaz et al. ARXIV] Image search using multilingual texts: a cross-modal learning approach between image and text. [paper]

• [Gu et al. NIPS22] Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark. [paper] [code]
• [Xie et al. ARXIV22] ZERO and R2D2: A Large-scale Chinese Cross-modal Benchmark and a Vision-Language Framework. [paper] [code]