Geoffrey E. Hinton, Oriol Vinyals, Jeffrey Dean. Distilling the Knowledge in a Neural Network. NeurIPS 2014 Deep Learning Workshop Siyu Ren, Kenny Zhu. Leaner and Faster: Two-stage Model Compression for Lightweight Text-image Retrieval. NAACL-HLT 2022 论文信息 论文标题:ConaCLIP: Exploring Distil...
所以在训练后融合的双编码器时,可以用query和D各自的编码器得到的Vq和VD用上边的注意力公式分别计算VD对Vq的互注意力 、Vq和VD的互注意力 ,只要和分布一致,就可以说明后融合模型也充分的学习到了query和D的交互信息。 所以在训练后交互模型时加入互注意力矩阵的拟合(采用KL散度)Loss,公式如下: 在论文中,也使用...
Geoffrey E. Hinton, Oriol Vinyals, Jeffrey Dean. Distilling the Knowledge in a Neural Network. NeurIPS 2014 Deep Learning Workshop Siyu Ren, Kenny Zhu. Leaner and Faster: Two-stage Model Compression for Lightweight Text-image Retrieval. NAACL-HLT 2022 论文信息 论文标题:ConaCLIP: Exploring Distil...
Intermittent Deployment for Large-Scale Multi-Robot Forage Perception: Data Synthesis, Prediction, and Planning 13 p. 3D-TSV: The 3D Trajectory-based Stress Visualizer 6 p. Semantic-Based Few-Shot Learning by Interactive Psychometric Testing 40 p. Statistical inference on representational geometries...
Finally, on the Houston2013 dataset and the Trento dataset, we demonstrate through a series of experiments that the dual-encoder model for hyperspectral and LiDAR joint classification via contrastive learning achieves state-of-the-art classification performance.Yuji Iwahori...
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/model vision_text_dual_encoder Sorry, something went wrong. Copy link Contributor Author HenonBamboo commented Jun 12, 2024 deit本地测试结果: vision_text_dual_encoder本地测试结果: Sorry, something went wrong. Copy link Contributor Author HenonBamboo commented Jun 12, 2024 https://github...
The Rectified Linear Unit (ReLU) activation function is used to introduce nonlinearity into the model. The Squeeze-and-Excite (SE)44block, which is shown in Fig. 1c, is then applied to enhance the feature map’s quality. The second DL encoder is built from scratch. We utilize the prior...
To enhance the per-formance of dense retrieval models withoutloss of eff iciency, we propose a GNN-encodermodel in which query (passage) information isfused into passage (query) representations viagraph neural networks that are constructed byqueries and their top retrieved passages. Bythis means,...
By incorporating Dynamic Weight Composing (DWC) loss dynamically adjusts model's focus based on training progression, DEFN achieves SOTA performance on the OIMHS public dataset, showcasing effectiveness in indistinct boundary contexts. Source code for DEFN is available at: https://github.com/IMOP-...