Search Results for author:
Found 5 papers, 3 papers with code
Binary Linear Tree Commitment-based Ownership Protection for Distributed Machine Learning
Distributed machine learning enables parallel training of extensive datasets by delegating computing tasks across multiple workers.
Watermarking Vision-Language Pre-trained Models for Multi-modal Embedding as a Service
Our extensive experiments on various datasets indicate that the proposed watermarking approach is effective and safe for verifying the copyright of VLPs for multi-modal EaaS and robust against model extraction attacks.
VFedMH: Vertical Federated Learning for Training Multiple Heterogeneous Models
Then the passive party, who owns only features of the sample, injects the blinding factor into the local embedding and sends it to the active party.
Align before Search: Aligning Ads Image to Text for Accurate Cross-Modal Sponsored Search
Conventional research mainly studies from the view of modeling the implicit correlations between images and texts for query-ads matching, ignoring the alignment of detailed product information and resulting in suboptimal search performance. In this work, we propose a simple alignment network for explicitly mapping fine-grained visual parts in ads images to the corresponding text, which leverages the co-occurrence structure consistency between vision and language spaces without requiring expensive labeled training data.
Ranked #1 on
Image-text matching
on CommercialAdsDataset
Context-I2W: Mapping Images to Context-dependent Words for Accurate Zero-Shot Composed Image Retrieval
Different from Composed Image Retrieval task that requires expensive labels for training task-specific models, Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent that could be related to domain, scene, object, and attribute.
