🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
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Updated
Jan 23, 2024 - Python
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
X-modaler is a versatile and high-performance codebase for cross-modal analytics(e.g., image captioning, video captioning, vision-language pre-training, visual question answering, visual commonsense reasoning, and cross-modal retrieval).
[AAAI2021] The code of “Similarity Reasoning and Filtration for Image-Text Matching”
PyTorch code for BagFormer: Better Cross-Modal Retrieval via bag-wise interaction
Deep Supervised Cross-modal Retrieval (CVPR 2019, PyTorch Code)
Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval (CVPR 2019)
Code for "Learning the Best Pooling Strategy for Visual Semantic Embedding", CVPR 2021
Official Pytorch implementation of "Probabilistic Cross-Modal Embedding" (CVPR 2021)
[CVPR 2020, Oral] "Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval”, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2020. .
Scalable deep multimodal learning for cross-modal retrieval (SIGIR 2019, PyTorch Code)
Source code for paper "Adversary Guided Asymmetric Hashing for Cross-Modal Retrieval".
Unsupervised Contrastive Cross-modal Hashing (IEEE TPAMI 2023, PyTorch Code)
Learning Cross-Modal Retrieval with Noisy Labels (CVPR 2021, PyTorch Code)
Official implementation of "Contrastive Audio-Language Learning for Music" (ISMIR 2022)
[NeurIPS 2022 Spotlight] Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
Preserving Semantic Neighborhoods for Robust Cross-modal Retrieval [ECCV 2020]
Multimodal Adversarial Network for Cross-modal Retrieval (PyTorch Code)
Code, dataset and models for our CVPR 2022 publication "Text2Pos"
Multi-view Linear Discriminant Analysis Network for Cross-modal Retrieval and Cross-view Recognition (Keras&Theano Code)
Official implementation of the paper "ALADIN: Distilling Fine-grained Alignment Scores for Efficient Image-Text Matching and Retrieval"
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