5 citations · 5 across the 6 of their papers we have counts for
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cs.CV2024★ 5 cited
Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion Augmentation
Muquan Li, Dongyang Zhang, Tao He +3
Data-free knowledge distillation (DFKD) has emerged as a pivotal technique in the domain of model compression, substantially reducing the dependency on the original training data.…
cs.AI2024
MuAP: Multi-step Adaptive Prompt Learning for Vision-Language Model with Missing Modality
Ruiting Dai, Yuqiao Tan, Lisi Mo +3
Recently, prompt learning has garnered considerable attention for its success in various Vision-Language (VL) tasks. However, existing prompt-based models are primarily focused on…
cs.CV2024
Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning
Tao He, Tongtong Wu, Dongyang Zhang +3
Scene graph generation (SGG) endeavors to predict visual relationships between pairs of objects within an image. Prevailing SGG methods traditionally assume a one-off learning proc…