3 citations · 7 across the 7 of their papers we have counts for
7 papers
V-FIND: Revealing the Intrinsic Forgery Knowledge Encoded in Video Forgery Detectors
Shichao Kan, Chengpeng Hong, Jingtong Dou +8
As generated videos become increasingly realistic, reliable video forgery detection is increasingly important. Existing studies typically optimize and use video forgery detectors a…
Object Retrieval for Visual Question Answering with Outside Knowledge
Shichao Kan, Yuhai Deng, Jiale Fu +5
Retrieval-augmented generation (RAG) with large language models (LLMs) plays a crucial role in question answering, as LLMs possess limited knowledge and are not updated with contin…
POAR: Towards Open Vocabulary Pedestrian Attribute Recognition
Yue Zhang, Suchen Wang, Shichao Kan +3
Pedestrian attribute recognition (PAR) aims to predict the attributes of a target pedestrian in a surveillance system. Existing methods address the PAR problem by training a multi-…
Contrastive Bayesian Analysis for Deep Metric Learning
Shichao Kan, Zhiquan He, Yigang Cen +3
Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feat…
Coded Residual Transform for Generalizable Deep Metric Learning
Shichao Kan, Yixiong Liang, Min Li +3
A fundamental challenge in deep metric learning is the generalization capability of the feature embedding network model since the embedding network learned on training classes need…
A GAN-Based Input-Size Flexibility Model for Single Image Dehazing
Shichao Kan, Yue Zhang, Fanghui Zhang +1
Image-to-image translation based on generative adversarial network (GAN) has achieved state-of-the-art performance in various image restoration applications. Single image dehazing…
Unsupervised Deep Metric Learning with Transformed Attention Consistency and Contrastive Clustering Loss
Yang Li, Shichao Kan, Zhihai He
Existing approaches for unsupervised metric learning focus on exploring self-supervision information within the input image itself. We observe that, when analyzing images, human ey…