Search Results for author: Weigang Zhang

Found 7 papers, 2 papers with code

Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision

no code implementations ECCV 2020 Xinzhe Han, Shuhui Wang, Chi Su, Weigang Zhang, Qingming Huang, Qi Tian

In this paper, we rethink implicit reasoning process in VQA, and propose a new formulation which maximizes the log-likelihood of joint distribution for the observed question and predicted answer.

Question Answering Visual Question Answering +1

Progressive Multi-resolution Loss for Crowd Counting

no code implementations8 Dec 2022 Ziheng Yan, Yuankai Qi, Guorong Li, Xinyan Liu, Weigang Zhang, Qingming Huang, Ming-Hsuan Yang

Crowd counting is usually handled in a density map regression fashion, which is supervised via a L2 loss between the predicted density map and ground truth.

Crowd Counting

Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis

1 code implementation23 Nov 2021 Zhaobo Qi, Shuhui Wang, Chi Su, Li Su, Weigang Zhang, Qingming Huang

Based on TDC, we propose the temporal dynamic concept modeling network (TDCMN) to learn an accurate and complete concept representation for efficient untrimmed video analysis.

Image Categorization

Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network

no code implementations ICCV 2021 Xinyan Liu, Guorong Li, Zhenjun Han, Weigang Zhang, Yifan Yang, Qingming Huang, Nicu Sebe

Specifically, we propose a task-driven similarity metric based on sample's mutual enhancement, referred as co-fine-tune similarity, which can find a more efficient subset of data for training the expert network.

Crowd Counting

The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking

no code implementations ECCV 2018 Dawei Du, Yuankai Qi, Hongyang Yu, Yifan Yang, Kaiwen Duan, Guorong Li, Weigang Zhang, Qingming Huang, Qi Tian

Selected from 10 hours raw videos, about 80, 000 representative frames are fully annotated with bounding boxes as well as up to 14 kinds of attributes (e. g., weather condition, flying altitude, camera view, vehicle category, and occlusion) for three fundamental computer vision tasks: object detection, single object tracking, and multiple object tracking.

Multiple Object Tracking object-detection +2

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