Lisa Anne Hendricks


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Localizing Moments in Video with Temporal Language
Lisa Anne Hendricks | Oliver Wang | Eli Shechtman | Josef Sivic | Trevor Darrell | Bryan Russell
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Localizing moments in a longer video via natural language queries is a new, challenging task at the intersection of language and video understanding. Though moment localization with natural language is similar to other language and vision tasks like natural language object retrieval in images, moment localization offers an interesting opportunity to model temporal dependencies and reasoning in text. We propose a new model that explicitly reasons about different temporal segments in a video, and shows that temporal context is important for localizing phrases which include temporal language. To benchmark whether our model, and other recent video localization models, can effectively reason about temporal language, we collect the novel TEMPOral reasoning in video and language (TEMPO) dataset. Our dataset consists of two parts: a dataset with real videos and template sentences (TEMPO - Template Language) which allows for controlled studies on temporal language, and a human language dataset which consists of temporal sentences annotated by humans (TEMPO - Human Language).

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Object Hallucination in Image Captioning
Anna Rohrbach | Lisa Anne Hendricks | Kaylee Burns | Trevor Darrell | Kate Saenko
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Despite continuously improving performance, contemporary image captioning models are prone to “hallucinating” objects that are not actually in a scene. One problem is that standard metrics only measure similarity to ground truth captions and may not fully capture image relevance. In this work, we propose a new image relevance metric to evaluate current models with veridical visual labels and assess their rate of object hallucination. We analyze how captioning model architectures and learning objectives contribute to object hallucination, explore when hallucination is likely due to image misclassification or language priors, and assess how well current sentence metrics capture object hallucination. We investigate these questions on the standard image captioning benchmark, MSCOCO, using a diverse set of models. Our analysis yields several interesting findings, including that models which score best on standard sentence metrics do not always have lower hallucination and that models which hallucinate more tend to make errors driven by language priors.


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Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
Subhashini Venugopalan | Lisa Anne Hendricks | Raymond Mooney | Kate Saenko
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing