This paper presentsMATDet, an end-toendencoder-decoder detection network based on the Transformer designed for oriented object detection. The network employsmulti-layer feature aggregation and rotated anchor ma
Vision Transformer models, while promising, also have computational concerns. This study presents a novel method that merges Vision Transformer strengths with a unique knowledge distillation technique. A pivotal element of our approach is the Token Importance Ranking Distillation, which facilitates the ...
The decoder takes the features from the transformer and generates the transformed image. Its structure includes transposed convolution, instance normalization, and ReLU activation function, producing the final output. The detailed structure is illustrated in Figure 3. On the other hand, the discriminator...
[5] combined several cascaded encoder-decoder networks on different resolution images and updated the landmark positions at each stage. Sun et al. [6] regard MLP as a graph transformer network to be embedded into a cascaded regression framework. Wu et al. [7] used a 3-way factorized ...
To build a Transformer like the one in theoriginal paper, we need a self-attention layer in the encoder. To implement it, you currently need to explicitly pass theattention_mask. It must be computed from the encoder's input padding mask, but you must not forget to add a second axis, ...
Ma, L., Ren, H., Zhang, X.: Effective cascade dual-decoder model for joint entity and relation extraction.arXiv:2106.14163(2021) Yuan, Y., Zhou, X., Pan, S., Zhu, Q., Song, Z., Guo, L.: A relation-specific attention network for joint entity and relation extraction. In: Intern...
The inverse frequency transformer (360) may apply an 8×8, 8×4, 4×8, 4×4, or other size inverse frequency transform.For a predicted picture, the decoder (300) combines the reconstructed prediction residual (345) with the motion compensated prediction (335) to form the reconstructed ...
For the image decoder 500 to be applied in the video decoding apparatus 200 according to an exemplary embodiment, all elements of the image decoder 500, i.e., the entropy decoder 515, the dequantizer 520, the inverse transformer 525, the intra predictor 540, the inter predictor 535, the ...
This paper presents MATDet, an end-to-end encoder-decoder detection network based on the Transformer designed for oriented object detection. The network employs multi-layer feature aggregation and rotated anchor matching methods to improve oriented small and densely distributed ...
The model comprises (1) the specialized CNN feature extraction, (2) the GRU-SKIP enhanced long-temporal module adept at capturing extended patterns, (3) a transformer module employing encoder-decoder and multi-attention mechanisms to hone prediction accuracy and trim model complexity, and (4) a ...