108 citations · 297 across the 19 of their papers we have counts for
24 papers
LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models
Peng Xu, Wenqi Shao, Kaipeng Zhang +7
Large Vision-Language Models (LVLMs) have recently played a dominant role in multimodal vision-language learning. Despite the great success, it lacks a holistic evaluation of their…
ConvMAE: Masked Convolution Meets Masked Autoencoders
Peng Gao, Teli Ma, Hongsheng Li +3
Vision Transformers (ViT) become widely-adopted architectures for various vision tasks. Masked auto-encoding for feature pretraining and multi-scale hybrid convolution-transformer…
POS-BERT: Point Cloud One-Stage BERT Pre-Training
Kexue Fu, Peng Gao, ShaoLei Liu +3
Recently, the pre-training paradigm combining Transformer and masked language modeling has achieved tremendous success in NLP, images, and point clouds, such as BERT. However, dire…
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning
Kexue Fu, Peng Gao, Renrui Zhang +3
In this paper, we propose a simple and general framework for self-supervised point cloud representation learning. Human beings understand the 3D world by extracting two levels of i…
UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning
Kunchang Li, Yali Wang, Peng Gao +4
It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global dependency between vi…
TerViT: An Efficient Ternary Vision Transformer
Sheng Xu, Yanjing Li, Teli Ma +4
Vision transformers (ViTs) have demonstrated great potential in various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-con…