no code implementations • 1 Apr 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.
no code implementations • 18 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.
no code implementations • 26 Mar 2022 • Jie Zhang, Jun Li, Yijin Zhang, Qingqing Wu, Xiongwei Wu, Feng Shu, Shi Jin, Wen Chen
Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks.
Multi-agent Reinforcement Learning reinforcement-learning +1
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
no code implementations • 29 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.
2 code implementations • 12 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)
no code implementations • 25 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.
1 code implementation • 10 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.
no code implementations • 22 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.
no code implementations • 3 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.
no code implementations • 8 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.