3 citations · 3 across the 9 of their papers we have counts for
9 papers
Understanding Model Reprogramming for CLIP via Decoupling Visual Prompts
Chengyi Cai, Zesheng Ye, Lei Feng +2
Model reprogramming adapts pretrained models to downstream tasks by modifying only the input and output spaces. Visual reprogramming (VR) is one instance for vision tasks that adds…
Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference
Jing Du, Zesheng Ye, Bin Guo +5
Recent cross-domain recommendation (CDR) studies assume that disentangled domain-shared and domain-specific user representations can mitigate domain gaps and facilitate effective k…
Bayesian-guided Label Mapping for Visual Reprogramming
Chengyi Cai, Zesheng Ye, Lei Feng +2
Visual reprogramming (VR) leverages the intrinsic capabilities of pretrained vision models by adapting their input or output interfaces to solve downstream tasks whose labels (i.e.…
Sample-specific Masks for Visual Reprogramming-based Prompting
Chengyi Cai, Zesheng Ye, Lei Feng +2
Visual reprogramming (VR) is a prompting technique that aims to re-purpose a pre-trained model (e.g., a classifier on ImageNet) to target tasks (e.g., medical data prediction) by l…
Joint Identifiability of Cross-Domain Recommendation via Hierarchical Subspace Disentanglement
Jing Du, Zesheng Ye, Bin Guo +2
Cross-Domain Recommendation (CDR) seeks to enable effective knowledge transfer across domains. Existing works rely on either representation alignment or transformation bridges, but…
Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation
Jing Du, Zesheng Ye, Bin Guo +2
Learning accurate cross-domain preference mappings in the absence of overlapped users/items has presented a persistent challenge in Non-overlapping Cross-domain Recommendation (NOC…