22 citations · 23 across the 8 of their papers we have counts for
8 papers
FlattenGPT: Depth Compression for Transformer with Layer Flattening
Ruihan Xu, Qingpei Guo, Yao Zhu +3
Recent works have indicated redundancy across transformer blocks, prompting the research of depth compression to prune less crucial blocks. However, current ways of entire-block pr…
UniStateDLO: Unified Generative State Estimation and Tracking of Deformable Linear Objects Under Occlusion for Constrained Manipulation
Kangchen Lv, Mingrui Yu, Shihefeng Wang +2
Perception of deformable linear objects (DLOs), such as cables, ropes, and wires, is the cornerstone for successful downstream manipulation. Although vision-based methods have been…
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.…
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…
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,…
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…
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)…
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…