68 citations · 105 across the 19 of their papers we have counts for
8 papers · 1 filter
ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning
Zhiwei Hao, Jianyuan Guo, Li Shen +3
Recent advancements in multimodal fusion have witnessed the remarkable success of vision-language (VL) models, which excel in various multimodal applications such as image captioni…
DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices
Guanyu Xu, Zhiwei Hao, Yong Luo +3
Recent years have witnessed the great success of vision transformer (ViT), which has achieved state-of-the-art performance on multiple computer vision benchmarks. However, ViT mode…
PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning
Mengya Han, Heliang Zheng, Chaoyue Wang +4
In this work, we address the task of few-shot part segmentation, which aims to segment the different parts of an unseen object using very few labeled examples. It is found that lev…
Rethinking the Localization in Weakly Supervised Object Localization
Rui Xu, Yong Luo, Han Hu +3
Weakly supervised object localization (WSOL) is one of the most popular and challenging tasks in computer vision. This task is to localize the objects in the images given only the…
LGViT: Dynamic Early Exiting for Accelerating Vision Transformer
Guanyu Xu, Jiawei Hao, Li Shen +4
Recently, the efficient deployment and acceleration of powerful vision transformers (ViTs) on resource-limited edge devices for providing multimedia services have become attractive…
Multi-Granularity Hand Action Detection
Ting Zhe, Jing Zhang, Yongqian Li +3
Detecting hand actions in videos is crucial for understanding video content and has diverse real-world applications. Existing approaches often focus on whole-body actions or coarse…
Leveraging GAN Priors for Few-Shot Part Segmentation
Mengya Han, Heliang Zheng, Chaoyue Wang +3
Few-shot part segmentation aims to separate different parts of an object given only a few annotated samples. Due to the challenge of limited data, existing works mainly focus on le…
CDFKD-MFS: Collaborative Data-free Knowledge Distillation via Multi-level Feature Sharing
Zhiwei Hao, Yong Luo, Zhi Wang +2
Recently, the compression and deployment of powerful deep neural networks (DNNs) on resource-limited edge devices to provide intelligent services have become attractive tasks. Alth…