Search Results for author: Miaojing Shi

Found 16 papers, 3 papers with code

Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model

1 code implementation28 Mar 2022 Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, Guoqi Li

In this paper, we introduce a novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model.

Image Classification Language Modelling +3

MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning

no code implementations30 Oct 2021 Miao Zhang, Miaojing Shi, Li Li

Last, to enhance the embedding space learning, an additional pixel-wise metric learning module is introduced with triplet loss formulated on the pixel-level embedding of the input image.

Few-Shot Semantic Segmentation Image Classification +2

Learning to Recommend Items to Wikidata Editors

no code implementations13 Jul 2021 Kholoud Alghamdi, Miaojing Shi, Elena Simperl

The system uses a hybrid of content-based and collaborative filtering techniques to rank items for editors relying on both item features and item-editor previous interaction.

Collaborative Filtering Recommendation Systems

Detecting Human-Object Interaction with Mixed Supervision

no code implementations10 Nov 2020 Suresh Kirthi Kumaraswamy, Miaojing Shi, Ewa Kijak

Human object interaction (HOI) detection is an important task in image understanding and reasoning.

Human-Object Interaction Detection

Fast Fourier Intrinsic Network

no code implementations9 Nov 2020 Yanlin Qian, Miaojing Shi, Joni-Kristian Kämäräinen, Jiri Matas

We address the problem of decomposing an image into albedo and shading.

Restoring Negative Information in Few-Shot Object Detection

1 code implementation NeurIPS 2020 Yukuan Yang, Fangyun Wei, Miaojing Shi, Guoqi Li

In this paper, we restore the negative information in few-shot object detection by introducing a new negative- and positive-representative based metric learning framework and a new inference scheme with negative and positive representatives.

Few-Shot Learning Few-Shot Object Detection +2

Defending Adversarial Examples via DNN Bottleneck Reinforcement

no code implementations12 Aug 2020 Wenqing Liu, Miaojing Shi, Teddy Furon, Li Li

This paper presents a DNN bottleneck reinforcement scheme to alleviate the vulnerability of Deep Neural Networks (DNN) against adversarial attacks.

Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer

no code implementations12 Aug 2020 Yuting Liu, Zheng Wang, Miaojing Shi, Shin'ichi Satoh, Qijun Zhao, Hongyu Yang

We formulate the mutual transformations between the outputs of regression- and detection-based models as two scene-agnostic transformers which enable knowledge distillation between the two models.

Crowd Counting Knowledge Distillation +2

Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images

no code implementations arXiv 2019 Zhaohui Yang, Miaojing Shi, Chao Xu, Vittorio Ferrari, Yannis Avrithis

Weakly-supervised object detection attempts to limit the amount of supervision by dispensing the need for bounding boxes, but still assumes image-level labels on the entire training set.

Ranked #21 on Weakly Supervised Object Detection on PASCAL VOC 2012 test (using extra training data)

Weakly Supervised Object Detection

Point in, Box out: Beyond Counting Persons in Crowds

no code implementations CVPR 2019 Yuting Liu, Miaojing Shi, Qijun Zhao, Xiaofang Wang

In the end, we propose a curriculum learning strategy to train the network from images of relatively accurate and easy pseudo ground truth first.

Crowd Counting

Revisiting Perspective Information for Efficient Crowd Counting

no code implementations CVPR 2019 Miaojing Shi, Zhaohui Yang, Chao Xu, Qijun Chen

Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions.

Crowd Counting

Crowd counting via scale-adaptive convolutional neural network

1 code implementation13 Nov 2017 Lu Zhang, Miaojing Shi, Qiaobo Chen

The task of crowd counting is to automatically estimate the pedestrian number in crowd images.

Crowd Counting

Weakly Supervised Object Localization Using Things and Stuff Transfer

no code implementations ICCV 2017 Miaojing Shi, Holger Caesar, Vittorio Ferrari

We propose to help weakly supervised object localization for classes where location annotations are not available, by transferring things and stuff knowledge from a source set with available annotations.

Multiple Instance Learning Weakly-Supervised Object Localization

Weakly Supervised Object Localization Using Size Estimates

no code implementations15 Aug 2016 Miaojing Shi, Vittorio Ferrari

We present a technique for weakly supervised object localization (WSOL), building on the observation that WSOL algorithms usually work better on images with bigger objects.

Weakly-Supervised Object Localization

Early Burst Detection for Memory-Efficient Image Retrieval

no code implementations CVPR 2015 Miaojing Shi, Yannis Avrithis, Herve Jegou

Then, we show the interest of using this strategy in an asymmetrical manner, with only the database features being aggregated but not those of the query.

Image Retrieval

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