Search Results for author: Xiaowei Hu

Found 26 papers, 7 papers with code

Spatial Autoregressive Coding for Graph Neural Recommendation

no code implementations19 May 2022 Jiayi Zheng, Ling Yang, Heyuan Wang, Cheng Yang, Yinghong Li, Xiaowei Hu, Shenda Hong

To adequately leverage neighbor proximity and high-order information, we design a novel spatial autoregressive paradigm.

Graph Embedding

K-LITE: Learning Transferable Visual Models with External Knowledge

no code implementations20 Apr 2022 Sheng Shen, Chunyuan Li, Xiaowei Hu, Yujia Xie, Jianwei Yang, Pengchuan Zhang, Anna Rohrbach, Zhe Gan, Lijuan Wang, Lu Yuan, Ce Liu, Kurt Keutzer, Trevor Darrell, Jianfeng Gao

In this paper, we propose K-LITE (Knowledge-augmented Language-Image Training and Evaluation), a simple strategy to leverage external knowledge to build transferable visual systems: In training, it enriches entities in natural language with WordNet and Wiktionary knowledge, leading to an efficient and scalable approach to learning image representations that can understand both visual concepts and their knowledge; In evaluation, the natural language is also augmented with external knowledge and then used to reference learned visual concepts (or describe new ones) to enable zero-shot and few-shot transfer of the pre-trained models.

Image Classification Object Detection +1

Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation

1 code implementation21 Jan 2022 Huifeng Yao, Xiaowei Hu, Xiaomeng Li

With these augmentations as perturbations, we feed the input to a confidence-aware cross pseudo supervision network to measure the variance of pseudo labels and regularize the network to learn with more confident pseudo labels.

Medical Image Segmentation Semantic Segmentation

Injecting Semantic Concepts into End-to-End Image Captioning

no code implementations9 Dec 2021 Zhiyuan Fang, JianFeng Wang, Xiaowei Hu, Lin Liang, Zhe Gan, Lijuan Wang, Yezhou Yang, Zicheng Liu

In this paper, we are concerned with a better-performing detector-free image captioning model, and propose a pure vision transformer-based image captioning model, dubbed as ViTCAP, in which grid representations are used without extracting the regional features.

Image Captioning

Scaling Up Vision-Language Pre-training for Image Captioning

no code implementations24 Nov 2021 Xiaowei Hu, Zhe Gan, JianFeng Wang, Zhengyuan Yang, Zicheng Liu, Yumao Lu, Lijuan Wang

In this paper, we present LEMON, a LargE-scale iMage captiONer, and provide the first empirical study on the scaling behavior of VLP for image captioning.

Image Captioning

Crossing the Format Boundary of Text and Boxes: Towards Unified Vision-Language Modeling

no code implementations23 Nov 2021 Zhengyuan Yang, Zhe Gan, JianFeng Wang, Xiaowei Hu, Faisal Ahmed, Zicheng Liu, Yumao Lu, Lijuan Wang

In this paper, we propose UNICORN, a vision-language (VL) model that unifies text generation and bounding box prediction into a single architecture.

Image Captioning Language Modelling +5

UFO: A UniFied TransfOrmer for Vision-Language Representation Learning

no code implementations19 Nov 2021 JianFeng Wang, Xiaowei Hu, Zhe Gan, Zhengyuan Yang, Xiyang Dai, Zicheng Liu, Yumao Lu, Lijuan Wang

In this paper, we propose a single UniFied transfOrmer (UFO), which is capable of processing either unimodal inputs (e. g., image or language) or multimodal inputs (e. g., the concatenation of the image and the question), for vision-language (VL) representation learning.

Image Captioning Language Modelling +6

Strategic Inventories in a Supply Chain with Downstream Cournot Duopoly

no code implementations14 Sep 2021 Xiaowei Hu, Jaejin Jang, Nabeel Hamoud, Amirsaman Bajgiran

The inventories carried in a supply chain as a strategic tool to influence the competing firms are considered to be strategic inventories (SI).

An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA

no code implementations10 Sep 2021 Zhengyuan Yang, Zhe Gan, JianFeng Wang, Xiaowei Hu, Yumao Lu, Zicheng Liu, Lijuan Wang

To address this challenge, we propose PICa, a simple yet effective method that Prompts GPT3 via the use of Image Captions, for knowledge-based VQA.

Image Captioning Question Answering +2

Compressing Visual-linguistic Model via Knowledge Distillation

no code implementations ICCV 2021 Zhiyuan Fang, JianFeng Wang, Xiaowei Hu, Lijuan Wang, Yezhou Yang, Zicheng Liu

In this paper, we study knowledge distillation (KD) to effectively compress a transformer-based large VL model into a small VL model.

Image Captioning Knowledge Distillation +3

Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images

no code implementations5 Apr 2021 Cheng Xue, Lei Zhu, Huazhu Fu, Xiaowei Hu, Xiaomeng Li, Hai Zhang, Pheng Ann Heng

The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement.

Boundary Detection Lesion Segmentation +1

Deep Texture-Aware Features for Camouflaged Object Detection

no code implementations5 Feb 2021 Jingjing Ren, Xiaowei Hu, Lei Zhu, Xuemiao Xu, Yangyang Xu, Weiming Wang, Zijun Deng, Pheng-Ann Heng

Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings.

Object Detection

Incorporating Vision Bias into Click Models for Image-oriented Search Engine

no code implementations7 Jan 2021 Ningxin Xu, Cheng Yang, Yixin Zhu, Xiaowei Hu, Changhu Wang

Most typical click models assume that the probability of a document to be examined by users only depends on position, such as PBM and UBM.

VinVL: Revisiting Visual Representations in Vision-Language Models

6 code implementations CVPR 2021 Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, Jianfeng Gao

In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model \oscar \cite{li2020oscar}, and utilize an improved approach \short\ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks.

Image Captioning Object Detection

MiniVLM: A Smaller and Faster Vision-Language Model

no code implementations13 Dec 2020 JianFeng Wang, Xiaowei Hu, Pengchuan Zhang, Xiujun Li, Lijuan Wang, Lei Zhang, Jianfeng Gao, Zicheng Liu

We design a Two-stage Efficient feature Extractor (TEE), inspired by the one-stage EfficientDet network, to significantly reduce the time cost of visual feature extraction by $95\%$, compared to a baseline model.

Language Modelling

VIVO: Visual Vocabulary Pre-Training for Novel Object Captioning

no code implementations28 Sep 2020 Xiaowei Hu, Xi Yin, Kevin Lin, Lijuan Wang, Lei Zhang, Jianfeng Gao, Zicheng Liu

It is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated in the novel object captioning challenge (nocaps).

Image Captioning TAG

Revisiting Shadow Detection: A New Benchmark Dataset for Complex World

2 code implementations16 Nov 2019 Xiaowei Hu, Tianyu Wang, Chi-Wing Fu, Yitong Jiang, Qiong Wang, Pheng-Ann Heng

Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world.

Shadow Detection

Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration

no code implementations3 Jul 2019 Lihao Liu, Xiaowei Hu, Lei Zhu, Pheng-Ann Heng

This paper presents a novel framework for unsupervised 3D brain image registration by capturing the feature-level transformation relationships between the unaligned image and reference image.

Image Registration

Depth-Attentional Features for Single-Image Rain Removal

no code implementations CVPR 2019 Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Pheng-Ann Heng

Rain is a common weather phenomenon, where object visibility varies with depth from the camera and objects faraway are visually blocked more by fog than by rain streaks.

Single Image Deraining

Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data

4 code implementations ICCV 2019 Xiaowei Hu, Yitong Jiang, Chi-Wing Fu, Pheng-Ann Heng

This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples.

Shadow Removal

SAC-Net: Spatial Attenuation Context for Salient Object Detection

no code implementations25 Mar 2019 Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Tianyu Wang, Pheng-Ann Heng

This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects.

RGB Salient Object Detection Salient Object Detection

Direction-aware Spatial Context Features for Shadow Detection and Removal

1 code implementation12 May 2018 Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Jing Qin, Pheng-Ann Heng

This paper presents a novel deep neural network design for shadow detection and removal by analyzing the spatial image context in a direction-aware manner.

Shadow Detection And Removal Shadow Removal

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

no code implementations2 Apr 2018 Xiaowei Hu, Xuemiao Xu, Yongjie Xiao, Hao Chen, Shengfeng He, Jing Qin, Pheng-Ann Heng

Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales.

Fast Vehicle Detection Object Detection

Direction-aware Spatial Context Features for Shadow Detection

1 code implementation CVPR 2018 Xiaowei Hu, Lei Zhu, Chi-Wing Fu, Jing Qin, Pheng-Ann Heng

To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN.

Detecting Shadows Shadow Detection

(Bandit) Convex Optimization with Biased Noisy Gradient Oracles

no code implementations22 Sep 2016 Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári

Algorithms for bandit convex optimization and online learning often rely on constructing noisy gradient estimates, which are then used in appropriately adjusted first-order algorithms, replacing actual gradients.

online learning

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