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Changan Wang

7 accepted papers

2023

RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation

IJCAI 2023poster

Glass-like objects are widespread in daily life but remain intractable to be segmented for most existing methods. The transparent property makes it difficult to be distinguished from background, while the tiny separation boundary further impedes the acquisition of their exact contour. In this paper,…

2022

LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization

AAAI 2022technical

Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. The convolution neural network (CNN) based techniques often result in highlighting the most discriminative part of objects while ignoring the entire object extent. Recently, the transforme…

Cited by 57SourcePDFScholar
2021

Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework

ICCV 2021poster

Localizing individuals in crowds is more in accordance with the practical demands of subsequent high-level crowd analysis tasks than simply counting. However, existing localization based methods relying on intermediate representations (i.e., density maps or pseudo boxes) serving as learning targets…

Cited by 365PDFcodeScholar
2021

To Choose or to Fuse? Scale Selection for Crowd Counting

AAAI 2021technical

In this paper, we address the large scale variation problem in crowd counting by taking full advantage of the multi-scale feature representations in a multi-level network. We implement such an idea by keeping the counting error of a patch as small as possible with a proper feature level selection st…

2021

Uniformity in Heterogeneity: Diving Deep Into Count Interval Partition for Crowd Counting

ICCV 2021poster

Recently, the problem of inaccurate learning targets in crowd counting draws increasing attention. Inspired by a few pioneering work, we solve this problem by trying to predict the indices of pre-defined interval bins of counts instead of the count values themselves. However, an inappropriate interv…

Cited by 49PDFcodeScholar
2020

Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking

ECCV 2020poster

Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution. Going beyond these sub-optimal frame…