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Alex Hauptmann

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3 papers

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IJCAI Conference 2021 Conference Paper

Person Search Challenges and Solutions: A Survey

Xiangtan Lin
Pengzhen Ren
Yun Xiao
Xiaojun Chang
Alex Hauptmann

Person search has drawn increasing attention due to its real-world applications and research significance. Person search aims to find a probe person in a gallery of scene images with a wide range of applications, such as criminals search, multicamera tracking, missing person search, etc. Early person search works focused on image-based person search, which uses person image as the search query. Text-based person search is another major person search category that uses free-form natural language as the search query. Person search is challenging, and corresponding solutions are diverse and complex. Therefore, systematic surveys on this topic are essential. This paper surveyed the recent works on image-based and text-based person search from the perspective of challenges and solutions. Specifically, we provide a brief analysis of highly influential person search methods considering the three significant challenges: the discriminative person features, the query-person gap, and the detection-identification inconsistency. We summarise and compare evaluation results. Finally, we discuss open issues and some promising future research directions.

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AAAI Conference 2017 System Paper

Visual Memory QA: Your Personal Photo and Video Search Agent

Lu Jiang
LiangLiang Cao
Yannis Kalantidis
Sachin Farfade
Alex Hauptmann

The boom of mobile devices and cloud services has led to an explosion of personal photo and video data. However, due to the missing user-generated metadata such as titles or descriptions, it usually takes a user a lot of swipes to ﬁnd some video on the cell phone. To solve the problem, we present an innovative idea called Visual Memory QA which allow a user not only to search but also to ask questions about her daily life captured in the personal videos. The proposed system automatically analyzes the content of personal videos without usergenerated metadata, and offers a conversational interface to accept and answer questions. To the best of our knowledge, it is the ﬁrst to answer personal questions discovered in personal photos or videos. The example questions are “what was the lat time we went hiking in the forest near San Francisco? ”; “did we have pizza last week? ”; “with whom did I have dinner in AAAI 2015? ”.

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AAAI Conference 2015 Conference Paper

Complex Event Detection via Event Oriented Dictionary Learning

Yan Yan
Yi Yang
Haoquan Shen
Deyu Meng
Gaowen Liu
Alex Hauptmann
Nicu Sebe

Complex event detection is a retrieval task with the goal of finding videos of a particular event in a largescale unconstrained internet video archive, given example videos and text descriptions. Nowadays, different multimodal fusion schemes of low-level and high-level features are extensively investigated and evaluated for the complex event detection task. However, how to effectively select the high-level semantic meaningful concepts from a large pool to assist complex event detection is rarely studied in the literature. In this paper, we propose two novel strategies to automatically select semantic meaningful concepts for the event detection task based on both the events-kit text descriptions and the concepts high-level feature descriptions. Moreover, we introduce a novel event oriented dictionary representation based on the selected semantic concepts. Towards this goal, we leverage training samples of selected concepts from the Semantic Indexing (SIN) dataset with a pool of 346 concepts, into a novel supervised multitask dictionary learning framework. Extensive experimental results on TRECVID Multimedia Event Detection (MED) dataset demonstrate the efficacy of our proposed method.

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