Search Results for author:
Found 7 papers, 6 papers with code
vid-TLDR: Training Free Token merging for Light-weight Video Transformer
To tackle these issues, we propose training free token merging for lightweight video Transformer (vid-TLDR) that aims to enhance the efficiency of video Transformers by merging the background tokens without additional training.
Ranked #2 on
Video Retrieval
on SSv2-template retrieval
(using extra training data)
Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision Transformers
Here, we argue that token fusion needs to consider diverse relations between tokens to minimize information loss.
Ranked #1 on
Efficient ViTs
on ImageNet-1K (With LV-ViT-S)
Read-only Prompt Optimization for Vision-Language Few-shot Learning
RPO leverages masked attention to prevent the internal representation shift in the pre-trained model.
Ranked #6 on
Prompt Engineering
on Caltech-101
Self-positioning Point-based Transformer for Point Cloud Understanding
In this paper, we present a Self-Positioning point-based Transformer (SPoTr), which is designed to capture both local and global shape contexts with reduced complexity.
Ranked #2 on
3D Part Segmentation
on ShapeNet-Part
SageMix: Saliency-Guided Mixup for Point Clouds
Mixup is a simple and widely-used data augmentation technique that has proven effective in alleviating the problems of overfitting and data scarcity.
Ranked #40 on
3D Part Segmentation
on ShapeNet-Part
Difference in Differences and Ratio in Ratios for Limited Dependent Variables
We evaluate DD and the related approaches with simulation and empirical studies, and recommend 'Poisson Quasi-MLE' for non-negative (such as count or zero-censored) Y and (multinomial) logit MLE for binary, fractional or multinomial Y.
Point Cloud Augmentation with Weighted Local Transformations
Although data augmentation is a standard approach to compensate for the scarcity of data, it has been less explored in the point cloud literature.
Ranked #11 on
Point Cloud Classification
on PointCloud-C
