(CVPR'22) COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval (ECCV'22) TS2-Net: Token Shift and Selection Transformer for Text-Video Retrieval (ArXiv'22) M2HF: Multi-level Multi-modal Hybrid Fusion for Text-Video Retrieval (ArXiv'22)UATVR: Uncertaint...
experimentally comparing with multiple existing methods on MSR-VTT and MSVD datasets, the model achieves R@1 (recall at 1) metrics of 51.5% and 52.4% on MSR-VTT and MSVD datasets, respectively, which indicates that the proposed model can improve the efficiency of ...
The aforementioned methods all employ an offline learning model for batch-based training, which may fail to adapt to changing data and consequently reduce retrieval efficiency when faced with large volumes of streaming data. To address these limitations, several online hashing methods25,26 have been ...
Performance of 3D in-domain and cross-modal retrieval task on ModelNet40 dataset in terms of mAP. When the target or source are from image domain, the results are reported for multi- view images: 1 view, 2 views, and 4 views denoted by v1, v2, and v4. AP = 1 R p...
Deep Unsupervised Hashing for Large-Scale Cross-Modal Retrieval Using Knowledge Distillation Model. 来自 国家科技图书文献中心 喜欢 0 阅读量: 127 作者:M Li,Q Li,L Tang,S Peng,Y Ma,D Yang 摘要: Cross-modal hashing encodes heterogeneous multimedia data into compact binary code to achieve fast ...
In this paper, we propose a cross-modal retrieval model aligning visual and textual data (like pictures of dishes and their recipes) in a shared representation space. We describe an effective learning scheme, capable of tackling large-scale problems, and validate it on the Recipe1M dataset ...
在本文中,作者设计了一种有效的全局-局部对齐方法 。多模态视频序列和文本特征通过一组共享语义中心自...
Cross-Domain Image Captioning via Cross-Modal Retrieval and Model Adaptation 来自 国家科技图书文献中心 喜欢 0 阅读量: 199 作者:W Zhao,X Wu,J Luo 摘要: In recent years, large scale datasets of paired images and sentences have enabled the remarkable success in automatically generating descriptions ...
finetuned_sbert_model = finetuner.get_model(sbert_run.artifact_id) 然后,提取成对的产品图像和类别名称,用相同的步骤构造用于微调的 CLIP DocumentArray 对象,其目的是通过训练模型使得类别名称向量和图像向量的距离更近。 # create and submit CLIP finetuning job clip_run = finetuner.fit( model='...
As the rapid development of deep neural networks, multi-modal learning techniques are widely concerned. Cross-modal retrieval is an important branch of multimodal learning. Its fundamental purpose is to reveal the relation between different modal samples