most citedAdversarial Robustness of Visual Dialog

1 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20231 cited

Volume Feature Rendering for Fast Neural Radiance Field Reconstruction

Kang Han, Wei Xiang, Lu Yu

Neural radiance fields (NeRFs) are able to synthesize realistic novel views from multi-view images captured from distinct positions and perspectives. In NeRF's rendering pipeline,…

cs.CV2023

Camera-Incremental Object Re-Identification with Identity Knowledge Evolution

Hantao Yao, Lu Yu, Jifei Luo +1

Object Re-identification (ReID) aims to retrieve the probe object from many gallery images with the ReID model inferred based on a stationary camera-free dataset by associating and…

cs.CV2023

Quality-agnostic Image Captioning to Safely Assist People with Vision Impairment

Lu Yu, Malvina Nikandrou, Jiali Jin +1

Automated image captioning has the potential to be a useful tool for people with vision impairments. Images taken by this user group are often noisy, which leads to incorrect and e…

cs.IR20231 cited

DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation

Feng Zhu, Mingjie Zhong, Xinxing Yang +8

In recommendation scenarios, there are two long-standing challenges, i.e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-…

cs.CV20221 cited

Adversarial Robustness of Visual Dialog

Lu Yu, Verena Rieser

Adversarial robustness evaluates the worst-case performance scenario of a machine learning model to ensure its safety and reliability. This study is the first to investigate the ro…