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- Medical Image Segmentation using Squeeze-and-Expansion Transformers201
Segtran is an alternative segmentation framework based on transformers, which has unlimited effective receptive fields even at high feature resolutions, and a new positional encoding scheme for transformers is proposed, imposing a continuity inductive bias for images.
- CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow149
This work proposes a new architecture “CRoss-Attentional Flow Trans-former” (CRAFT), aiming to revitalize the correlation volume computation, and designed an image shifting attack that shifts input images to generate large artificial motions.
- Multi-Instance Multi-Scale CNN for Medical Image Classification62
A Multi-Instance Multi-Scale (MIMS) CNN is proposed, which extracts patterns of different receptive fields with a shared set of convolutional kernels so that scale-invariant patterns are captured by this compact set of kernels.
- RoboCoDraw: Robotic Avatar Drawing with GAN-Based Style Transfer and Time-Efficient Path Optimization35
The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm by using the Generative Adversarial Network (GAN) based style transfer and a Random-Key Genetic Algorithm (RKGA) based path optimization.
- Few-Shot Domain Adaptation with Polymorphic Transformers28
A Polymorphic Transformer (Polyformer), which can be incorporated into any DNN backbones for few-shot domain adaptation, and which can perform robustly on the target domain after being trained on a few annotated images.
- Functional Connectivity Hubs Could Serve as a Potential Biomarker in Alzheimer’s Disease: A Reproducible Study26
By reflecting a robust and reproducible global shift in brain functions, FCD provides an fMRI biomarker for the underlying mechanism in AD and correlates with cognitive score and could distinguish MCI from controls with high accuracy.
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- Confidence-Adaptive SwiGLU for Mixture-of-Experts–
Confidence-Aware SwiGLU ($\kappa$-SwiGLU), a variant of SwiGLU for Mixture-of-Experts (MoE) models that adjusts expert gate sharpness according to token-level routing confidence, is proposed, demonstrating that confidence-aware gate sharpness is a promising mechanism for improving MoE MLPs.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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