Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
NTIRE 2023 challenge on efficient super-resolution: Methods and results
This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution
with a focus on the proposed solutions and results. The aim of this challenge is to devise a …
with a focus on the proposed solutions and results. The aim of this challenge is to devise a …
Run, don't walk: chasing higher FLOPS for faster neural networks
To design fast neural networks, many works have been focusing on reducing the number of
floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does …
floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does …
Internimage: Exploring large-scale vision foundation models with deformable convolutions
Compared to the great progress of large-scale vision transformers (ViTs) in recent years,
large-scale models based on convolutional neural networks (CNNs) are still in an early …
large-scale models based on convolutional neural networks (CNNs) are still in an early …
Vision mamba: Efficient visual representation learning with bidirectional state space model
Recently the state space models (SSMs) with efficient hardware-aware designs, ie, the
Mamba deep learning model, have shown great potential for long sequence modeling …
Mamba deep learning model, have shown great potential for long sequence modeling …
Rwkv: Reinventing rnns for the transformer era
Transformers have revolutionized almost all natural language processing (NLP) tasks but
suffer from memory and computational complexity that scales quadratically with sequence …
suffer from memory and computational complexity that scales quadratically with sequence …
Efficientvit: Memory efficient vision transformer with cascaded group attention
Vision transformers have shown great success due to their high model capabilities.
However, their remarkable performance is accompanied by heavy computation costs, which …
However, their remarkable performance is accompanied by heavy computation costs, which …
Large selective kernel network for remote sensing object detection
Recent research on remote sensing object detection has largely focused on improving the
representation of oriented bounding boxes but has overlooked the unique prior knowledge …
representation of oriented bounding boxes but has overlooked the unique prior knowledge …
Vision gnn: An image is worth graph of nodes
Network architecture plays a key role in the deep learning-based computer vision system.
The widely-used convolutional neural network and transformer treat the image as a grid or …
The widely-used convolutional neural network and transformer treat the image as a grid or …
Hornet: Efficient high-order spatial interactions with recursive gated convolutions
Recent progress in vision Transformers exhibits great success in various tasks driven by the
new spatial modeling mechanism based on dot-product self-attention. In this paper, we …
new spatial modeling mechanism based on dot-product self-attention. In this paper, we …