16 citations · 23 across the 6 of their papers we have counts for
6 papers
Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone
Zeyinzi Jiang, Chaojie Mao, Ziyuan Huang +5
Parameter-efficient tuning has become a trend in transferring large-scale foundation models to downstream applications. Existing methods typically embed some light-weight tuners in…
Logic Diffusion for Knowledge Graph Reasoning
Xiaoying Xie, Biao Gong, Yiliang Lv +3
Most recent works focus on answering first order logical queries to explore the knowledge graph reasoning via multi-hop logic predictions. However, existing reasoning models are li…
Enhancing Unsupervised Audio Representation Learning via Adversarial Sample Generation
Yulin Pan, Xiangteng He, Biao Gong +2
Existing audio analysis methods generally first transform the audio stream to spectrogram, and then feed it into CNN for further analysis. A standard CNN recognizes specific visual…
ViM: Vision Middleware for Unified Downstream Transferring
Yutong Feng, Biao Gong, Jianwen Jiang +4
Foundation models are pre-trained on massive data and transferred to downstream tasks via fine-tuning. This work presents Vision Middleware (ViM), a new learning paradigm that targ…
Rethinking Efficient Tuning Methods from a Unified Perspective
Zeyinzi Jiang, Chaojie Mao, Ziyuan Huang +3
Parameter-efficient transfer learning (PETL) based on large-scale pre-trained foundation models has achieved great success in various downstream applications. Existing tuning metho…
MAR: Masked Autoencoders for Efficient Action Recognition
Zhiwu Qing, Shiwei Zhang, Ziyuan Huang +5
Standard approaches for video recognition usually operate on the full input videos, which is inefficient due to the widely present spatio-temporal redundancy in videos. Recent prog…