Search Results for author: Xiaobao Guo

Found 6 papers, 2 papers with code

Towards Photo-Realistic Virtual Try-On by Adaptively Generating$\leftrightarrow$Preserving Image Content

3 code implementations12 Mar 2020 Han Yang, Ruimao Zhang, Xiaobao Guo, Wei Liu, WangMeng Zuo, Ping Luo

First, a semantic layout generation module utilizes semantic segmentation of the reference image to progressively predict the desired semantic layout after try-on.

Ranked #4 on Virtual Try-on on VITON (IS metric)

Semantic Segmentation Virtual Try-on

Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning

1 code implementation ICCV 2023 Xiaobao Guo, Nithish Muthuchamy Selvaraj, Zitong Yu, Adams Wai-Kin Kong, Bingquan Shen, Alex Kot

Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as the difficulties of learning multimodal features effectively.

Deception Detection Multi-Task Learning

Unimodal and Crossmodal Refinement Network for Multimodal Sequence Fusion

no code implementations EMNLP 2021 Xiaobao Guo, Adams Kong, Huan Zhou, Xianfeng Wang, Min Wang

Specifically, to improve unimodal representations, a unimodal refinement module is designed to refine modality-specific learning via iteratively updating the distribution with transformer-based attention layers.

Representation Learning

Flexible-modal Deception Detection with Audio-Visual Adapter

no code implementations11 Feb 2023 Zhaoxu Li, Zitong Yu, Nithish Muthuchamy Selvaraj, Xiaobao Guo, Bingquan Shen, Adams Wai-Kin Kong, Alex Kot

Detecting deception by human behaviors is vital in many fields such as custom security and multimedia anti-fraud.

Deception Detection

Retrieving Multimodal Information for Augmented Generation: A Survey

no code implementations20 Mar 2023 Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty

As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better interact with the world.

Retrieval

MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition

no code implementations14 Jan 2024 Fan Zhang, Xiaobao Guo, Xiaojiang Peng, Alex Kot

In addition, when compared with the domain disparity existing between face datasets and FER datasets, the divergence between general datasets and FER datasets is more pronounced.

Contrastive Learning Face Recognition +3

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