To address these challenges, we propose CEDT2M, a framework for text to motion generation based on cross-modal mixture of encoder-decoder. Specifically, our CEDT2M introduces a multitask learning approach to jointly train unsupervised motion-text alignment and motion generation without relying on ...
In the second stage, a cross-modal decoder is constructed to fuse the image and language features, and then generate segmentation results. The decoding process can be seen as the alignment process of image and language. Currently, many strategies (recurrent interaction [10], cross-modal graph [...
In the feature decoding part, we design a progressive decoder to gradually fuse low-level features and filter noise information to accurately predict salient objects. Extensive experimental results on 6 benchmarks demonstrated that our network surpasses 12 state-of-the-art methods in terms of four ...
In DETR, object queries directly interact with the image to- kens through cross-attention in transformer decoder. For 3D object detection, one intuitive way is to concatenate the im- age and point cloud tokens together for further interaction with object queries. Ho...
The decoder transforms l embeddings of size d, where l is the maximum sequence length to which the transformer can attend. We use the decoder for non-autoregressive text generation by predicting a description for the input image in one forward pass. Let \(v^{*} = concat(TokenID[CLS],\;...
Notes https://github.com/Lyken17/pytorch-OpCounter. References Carion, N., Massa, F., Synnaeve, G., et al.: End-to-end object detection with transformers. In: European conference on computer vision, Springer, pp 213–229 (2020) Chen, L., Ma, W., Xiao, J., et al.: Ref-nms: ...
Here, we extended the inClust by adding two new modules, namely, the input-mask module in front of encoder and the output-mask module behind decoder (Fig. 1A). We named the augmented inClust as inClust+, and demonstrated that it could complete not only data integration but also gene ...
The transformer decoder aims to generate the top pixel features that can represent the target object of each frame. Motivated by Fnet [30], we also replace the self-attention sublayers with simple linear transformations. The self attention-free deco...
While tools like scglue have their merits in single-cell multi-omics data integration, they inherently lack the capability for cross-modal generation. Specifically, scglue’s entire framework emphasizes its unsuitability for cross-modal data generation, especially when noting that its decoder is design...
The pose decoder is a cascaded 4-layer bi- directional GRU with a hidden size ds of 300 for each level of pose hierarchy. Empirically, we set τ = 0.07, ϵ = 1000, da = 32, λh = 200, λp = 0.1, λs = 0.05, λk = 0.1, λc = 0.1....