19 citations · 50 across the 12 of their papers we have counts for
7 papers · 1 filter
Texts as Images in Prompt Tuning for Multi-Label Image Recognition
Zixian Guo, Bowen Dong, Zhilong Ji +3
Prompt tuning has been employed as an efficient way to adapt large vision-language pre-trained models (e.g. CLIP) to various downstream tasks in data-limited or label-limited setti…
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training
Tianyu Huang, Bowen Dong, Yunhan Yang +4
Pre-training across 3D vision and language remains under development because of limited training data. Recent works attempt to transfer vision-language pre-training models to 3D vi…
LPT: Long-tailed Prompt Tuning for Image Classification
Bowen Dong, Pan Zhou, Shuicheng Yan +1
For long-tailed classification, most works often pretrain a big model on a large-scale dataset, and then fine-tune the whole model for adapting to long-tailed data. Though promisin…
W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection
Zitong Huang, Yiping Bao, Bowen Dong +2
Weakly-supervised object detection (WSOD) aims to train an object detector only requiring the image-level annotations. Recently, some works have managed to select the accurate boxe…
Symmetry-Aware Transformer-based Mirror Detection
Tianyu Huang, Bowen Dong, Jiaying Lin +3
Mirror detection aims to identify the mirror regions in the given input image. Existing works mainly focus on integrating the semantic features and structural features to mine spec…
Prompt Tuning for Discriminative Pre-trained Language Models
Yuan Yao, Bowen Dong, Ao Zhang +6
Recent works have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing (NLP) tasks. However, to the best of ou…
Self-Promoted Supervision for Few-Shot Transformer
Bowen Dong, Pan Zhou, Shuicheng Yan +1
The few-shot learning ability of vision transformers (ViTs) is rarely investigated though heavily desired. In this work, we empirically find that with the same few-shot learning fr…