U-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation

1 code implementation • 29 Mar 2024 • You Wu, Kean Liu, Xiaoyue Mi, Fan Tang, Juan Cao, Jintao Li

Extensive experiments on various kinds of visual attributes with SOTA personalization methods show the ability of the proposed method to mimic target visual appearance in novel contexts, thus improving the controllability and flexibility of personalization.

Attribute Disentanglement +1

Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

1 code implementation • 8 Oct 2023 • Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Mykola Pechenizkiy, Yi Liang, Zhangyang Wang, Shiwei Liu

Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge when it comes to practical deployment due to their colossal model size.

Network Pruning

Zero-shot Generative Model Adaptation via Image-specific Prompt Learning

1 code implementation • CVPR 2023 • Jiayi Guo, Chaofei Wang, You Wu, Eric Zhang, Kai Wang, Xingqian Xu, Shiji Song, Humphrey Shi, Gao Huang

Recently, CLIP-guided image synthesis has shown appealing performance on adapting a pre-trained source-domain generator to an unseen target domain.

Image Generation

Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters

2 code implementations • 20 Dec 2022 • Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, Huan Sun

Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs).

AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistant

4 code implementations • 8 Mar 2022 • Benita Wong, Joya Chen, You Wu, Stan Weixian Lei, Dongxing Mao, Difei Gao, Mike Zheng Shou

In this paper, we define a new task called Affordance-centric Question-driven Task Completion, where the AI assistant should learn from instructional videos to provide step-by-step help in the user's view.

Visual Question Answering (VQA)

ReasonBERT: Pre-trained to Reason with Distant Supervision

1 code implementation • EMNLP 2021 • Xiang Deng, Yu Su, Alyssa Lees, You Wu, Cong Yu, Huan Sun

We present ReasonBert, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts.

Extractive Question-Answering Question Answering +1

TURL: Table Understanding through Representation Learning

1 code implementation • 26 Jun 2020 • Xiang Deng, Huan Sun, Alyssa Lees, You Wu, Cong Yu

In this paper, we present TURL, a novel framework that introduces the pre-training/fine-tuning paradigm to relational Web tables.

Cell Entity Annotation Columns Property Annotation +3

Generating Representative Headlines for News Stories

2 code implementations • 26 Jan 2020 • Xiaotao Gu, Yuning Mao, Jiawei Han, Jialu Liu, Hongkun Yu, You Wu, Cong Yu, Daniel Finnie, Jiaqi Zhai, Nicholas Zukoski

In this work, we study the problem of generating representative headlines for news stories.