Search Results for author: Xiongwei Wu

Found 11 papers, 3 papers with code

OVFoodSeg: Elevating Open-Vocabulary Food Image Segmentation via Image-Informed Textual Representation

no code implementations CVPR 2024 Xiongwei Wu, Sicheng Yu, Ee-Peng Lim, Chong-Wah Ngo

The pre-training phase equips FoodLearner with the capability to align visual information with corresponding textual representations that are specifically related to food, while the second phase adapts both the FoodLearner and the Image-Informed Text Encoder for the segmentation task.

Image Segmentation Segmentation +1

A Map-matching Algorithm with Extraction of Multi-group Information for Low-frequency Data

no code implementations18 Sep 2022 Jie Fang, Xiongwei Wu, DianChao Lin, Mengyun Xu, Huahua Wu, Xuesong Wu, Ting Bi

In addition, there are a large amount of other data, e. g., other vehicles' state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths.

Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation

1 code implementation CVPR 2022 Zhaozheng Chen, Tan Wang, Xiongwei Wu, Xian-Sheng Hua, Hanwang Zhang, Qianru Sun

Specifically, due to the sum-over-class pooling nature of BCE, each pixel in CAM may be responsive to multiple classes co-occurring in the same receptive field.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Attention-based Feature Aggregation

no code implementations29 Sep 2021 Xiongwei Wu, Ee-Peng Lim, Steven Hoi, Qianru Sun

To implement this module, we define two variants of attention: self-attention on the summed-up feature map, and cross-attention between two feature maps before summed up.

Instance Segmentation object-detection +2

A Large-Scale Benchmark for Food Image Segmentation

2 code implementations12 May 2021 Xiongwei Wu, Xin Fu, Ying Liu, Ee-Peng Lim, Steven C. H. Hoi, Qianru Sun

Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-grained ingredient labels and pixel-wise location masks -- the existing datasets either carry coarse ingredient labels or are small in size; and (2) the complex appearance of food makes it difficult to localize and recognize ingredients in food images, e. g., the ingredients may overlap one another in the same image, and the identical ingredient may appear distinctly in different food images.

Ranked #3 on Semantic Segmentation on FoodSeg103 (using extra training data)

Image Segmentation Segmentation +1

Meta-RCNN: Meta Learning for Few-Shot Object Detection

no code implementations25 Sep 2019 Xiongwei Wu, Doyen Sahoo, Steven C. H. Hoi

Specifically, Meta-RCNN learns an object detector in an episodic learning paradigm on the (meta) training data.

Few-Shot Object Detection Meta-Learning +3

Recent Advances in Deep Learning for Object Detection

1 code implementation10 Aug 2019 Xiongwei Wu, Doyen Sahoo, Steven C. H. Hoi

Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades.

Image Classification Object +2

Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection

no code implementations22 Mar 2018 Xiongwei Wu, Daoxin Zhang, Jianke Zhu, Steven C. H. Hoi

Recent years have witnessed many exciting achievements for object detection using deep learning techniques.

Object object-detection +2

Feature Agglomeration Networks for Single Stage Face Detection

no code implementations3 Dec 2017 Jialiang Zhang, Xiongwei Wu, Jianke Zhu, Steven C. H. Hoi

In this paper, we propose a novel simple yet effective framework of "Feature Agglomeration Networks" (FANet) to build a new single stage face detector, which not only achieves state-of-the-art performance but also runs efficiently.

Face Detection

LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

no code implementations8 Nov 2015 Steven C. H. Hoi, Xiongwei Wu, Hantang Liu, Yue Wu, Huiqiong Wang, Hui Xue, Qiang Wu

In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images.

Logo Recognition object-detection +1

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