Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery - rubbish-qi/CMAFF
Cross-modality fusing complementary information of multispectral remote sensing image pairs can improve the perception ability of detection algorithms, making them more robust and reliable for a wider range of applications, such as nighttime detection. Compared with prior methods, we think different featu...
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...
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 ...
A new Multi-granularity Shared Feature Fusion (MSFF) network is proposed in this paper, which combines global and local features to learn different granularities representations of the two modalities, extracting multi-scale and multi-level features from the backbone network, where the coarse ...
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.. Google Scholar [94] YUAN Z Q, SANG J T, LIU Y, et al. ...
To download the pre-processed IEMOCAP dataset, use the link given inhttps://github.com/david-yoon/attentive-modality-hopping-for-SEROnce you have it downloaded, replace the 'data_path' in 'multi_run.sh' with your folder path. Note: The processed dataset repo from Dr. David Yoon contains,...
ATLA uses LLMs to generate these descriptions as well as obtain the respective feature representations. InstructRL (Liu et al., 2023) uses one unified multimodal encoder to encode both language and vision for robotic tasks in a virtual environment. 2.3. Action-centric crossmodality Action-centric...
We explore semantics among the multimodal inputs in two aspects: the modality-shared consistency and the modality-specific variation. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross...
4.2. Implementation Details The proposed Multi-Modality Cross-Attention Network is implemented in PyTorch framework [27] with a NVIDIA GeForce GTX 2080Ti GPU. In the self-attention module, for the image branch, the image region feature vector extracted by a bo...