Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery Auhtor: FANG Qingyun and WANG ZhaokuiIntroCMAFF:Cross-Modality Attentive Feature FusionDifferential Enhancive
Local featureHybrid weighted attentionTwo-stream networkCross-modal person re-identification between the visible (RGB) modality and infrared (IR) modality is extremely important for nighttime surveillance applications. In addition to the cross-modal differences caused by different camera spectra, RGB-IR ...
To solve the depth noise problem, the cross-modal fusion networks [28], [29] combine features from other modalities in the spatial and channel dimensions and calibrate the features of the current modality at each stage of the network. That could achieve better multi-modal feature extraction and...
ZHANG Y Y, LI G R, CHU L Y, et al. Cross-media topic detection: a multi-modality fusion framework[C]//Proceed-ings of the 2013 IEEE International Conference on Multi-media and Expo, San Jose, Jul 15-19, 2013. Washington: IEEE Computer Society, 2013: 1-6.. ...
Additionally, to align the feature space across different modalities, we tailor a meta adapter that extracts textual information into an object query. This serves as an instruction for cross-modality matching. These two modules collaboratively ensure the alignment of multi-modal representations while ...
Attentive Intra-modality Fusion for Multimodal Sentiment Analysis Chapter © 2021 Multimodal Social Media Sentiment Analysis Based on Cross-Modal Hierarchical Attention Fusion Chapter © 2022 Explore related subjects Discover the latest articles and news from researchers in related subjects, suggested...
large cross-modality heterogeneity. Feature similarity learning is designed to further reduce the discrepancy in cross-domain features. The contributions of this paper are summarized as follows: 1. We introduce a joint network for cross-domain feature learning to address SBSR and ZS-SBIR, which ...
A higher weight indicates that the modality has a large influence on the drug feature representation. The drug is encoded as Xdrug: Xdrug = 1Ximg + 2Xtxt (1) For target sequence, we directly use its FASTA sequence as its text information. Simi- lar to the chemical feature text of ...
temporal attentive cross-modality transformer model for long-term traffic predictions, namely xMTrans, with capability of exploring the temporal correlations between the data of two modalities: one target modality (for prediction, e.g., traffic congestion) and one support modality (e.g., people ...
Method Modality Accuracy Mac. F1 Unimodal EDA 78.65 70.47 Unimodal ECG 78.51 76.96 Feat. Fus. EDA, ECG 81.49 78.60 Ours w/o att. EDA, ECG 87.09 83.10 Ours (AttX) EDA, ECG 92.08 91.11 Results in Papers With Code (↓ scroll down to see all results) ...