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Advances in medical image analysis with vision transformers: a comprehensive review
The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …
has recently also triggered broad interest in Computer Vision. Among other merits …
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 …
Vmamba: Visual state space model
Designing computationally efficient network architectures remains an ongoing necessity in
computer vision. In this paper, we adapt Mamba, a state-space language model, into …
computer vision. In this paper, we adapt Mamba, a state-space language model, into …
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 …
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 …
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 …
Repvit: Revisiting mobile cnn from vit perspective
Abstract Recently lightweight Vision Transformers (ViTs) demonstrate superior performance
and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on …
and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on …
Unireplknet: A universal perception large-kernel convnet for audio video point cloud time-series and image recognition
Large-kernel convolutional neural networks (ConvNets) have recently received extensive
research attention but two unresolved and critical issues demand further investigation. 1) …
research attention but two unresolved and critical issues demand further investigation. 1) …
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 …