[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. 接近GPT-4o表现的开源多模态对话模型
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May 29, 2025 - Python
[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. 接近GPT-4o表现的开源多模态对话模型
Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.
[AAAI2021] The code of “Similarity Reasoning and Filtration for Image-Text Matching”
Official implementation of the ICASSP-2022 paper "Text2Poster: Laying Out Stylized Texts on Retrieved Images"
Research Code for Multimodal-Cognition Team in Ant Group
PyTorch code for BagFormer: Better Cross-Modal Retrieval via bag-wise interaction
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections. (EMNLP 2022)
ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration
Image captioning using python and BLIP
Official implementation and dataset for the NAACL 2024 paper "ComCLIP: Training-Free Compositional Image and Text Matching"
Official implementation of our EMNLP 2022 paper "CPL: Counterfactual Prompt Learning for Vision and Language Models"
[TIP2023] The code of “Plug-and-Play Regulators for Image-Text Matching”
[EMNLP 2024] Preserving Multi-Modal Capabilities of Pre-trained VLMs for Improving Vision-Linguistic Compositionality
Noise of Web (NoW) is a challenging noisy correspondence learning (NCL) benchmark containing 100K image-text pairs for robust image-text matching/retrieval models.
[TIP2024] The code of “Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching”
[TIP2024] The code of "GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric Learning"
An end-to-end multimodal framework incorporating explicit knowledge graphs and OOD-detection. (NeurIPS23)
[KDD 2024] Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning
项目取材自 2024 年 ”泰迪杯“ 数据挖掘挑战赛 B 题,基于共享特征空间对比学习的跨模态图文互检模型
The Unified Code of Image-Text Retrieval for Further Exploration.
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