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most citedCAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications

22 citations · 23 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024

EGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera

Zheng Fang, Ke Ye, Yaofang Liu +7

Point clouds or depth images captured by current RGB-D cameras often suffer from low resolution, rendering them insufficient for applications such as 3D reconstruction and robots.…

cs.LG2024★ 1 cited

Towards Dynamic Message Passing on Graphs

Junshu Sun, Chenxue Yang, Xiangyang Ji +2

Message passing plays a vital role in graph neural networks (GNNs) for effective feature learning. However, the over-reliance on input topology diminishes the efficacy of message p…

cs.CV2024

Expanding Sparse Tuning for Low Memory Usage

Shufan Shen, Junshu Sun, Xiangyang Ji +2

Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained vision models to downstream tasks by tuning a small subset of parameters. Among PEFT methods,…

cs.CV2024★ 22 cited

CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications

Tianfang Zhang, Lei Li, Yang Zhou +4

Vision Transformers (ViTs) mark a revolutionary advance in neural networks with their token mixer's powerful global context capability. However, the pairwise token affinity and com…

cs.CV2024

VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models

Yabo Zhang, Yuxiang Wei, Xianhui Lin +5

Text-to-image diffusion models (T2I) have demonstrated unprecedented capabilities in creating realistic and aesthetic images. On the contrary, text-to-video diffusion models (T2V)…

cs.CV2024

MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models

Zijie Fang, Yifeng Wang, Ye Zhang +4

Recently, pathological diagnosis has achieved superior performance by combining deep learning models with the multiple instance learning (MIL) framework using whole slide images (W…