Meanwhile, a distribution alignment constraint is adopted to help keep the distribution of the learned semantic embeddings consistent with the distribution of real image features. Moreover, an auxiliary classifier is adopted to strengthen the quality of the learned semantic embeddings. Finally, a ...
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment Runqi Wang1,2∗, Hao Zheng2,3∗, Xiaoyue Duan1*, Jianzhuang Liu2, Yuning Lu2,4, Tian Wang1, Songcen Xu2, Baochang Zhang1,5† 1Beihang University 2H...
4.6.1. Performance of image and map alignment In CMPANet, the IMAM module effectively achieves cross-modal feature distribution alignment between maps and images, facilitating knowledge transfer and sharing from pre-trained large visual models. To illustrate the effectiveness of this alignment method, ...
We initialize hyperparameters using a Gaussian distribution with zero mean and a standard deviation of 0.5. We use adversarial loss and L2 to train \( \Theta _3\), and only L2 for \( \Theta _1\) and \( \Theta _2\). Tables 17 and 18 detail the architecture of the encoder and ...
test_distribution_shit.sh test_retrieval.sh test_zeroshot_cls.sh train_alignCLIP.sh train_sharedCLIP.sh Repository files navigation README Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIP This is the official implementation of AlignCLIP and provides the ...
Cross-Modal Match Module integrates time series and textual inputs through principal word embedding extraction and a cross-attention mechanism, ensuring efficient alignment of the marginal input distribution between time series and text. Feature Regularization Loss aligns the outputs of each intermediate ...
In the proposed UGACH, given a data of one modality, the generative model tries to fit the distribution over the manifold structure, and select informative data of another modality to challenge the discriminative model. The discriminative model learns to distinguish the generated data and the true...
according to the distribution state of the text length to truncate or fill the text. Figure8shows the data distribution. As shown in the figure, the length of the Chinese data is mostly 0–25, whereas the distribution of English text is more uniform, and the number of points is mostly ...
Furthermore, to facilitate the multimodal crowd counting, we propose a cross-modal collaborative representation learning framework, which consists of multiple modality-specific branches, a modality-shared branch, and an Information Aggregation-Distribution Module (IADM) to fully capture the complementary ...
Meanwhile, a cross-modal context module is proposed to explicitly facilitate alignment and interaction between distinct modalities, effectively bridging the gap between the extensive visual sequences of WSIs and corresponding highly summarized reports. Experimental results on WSI report generation show the ...