Video Event Understanding Using Natural Language Descriptions

By Vignesh Ramanathan, Percy Liang, Li Fei-Fei. 2013

61 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books (2015, background): “The field has tackled a diverse set of tasks such as captioning [15, 13, 40, 39, 24], alignment [13, 17, 38], Q&A [22, 21], visual model learning from textual descriptions [9, 29], and semantic visual search with natural multisentence queries [19].”
Learning Transferable Visual Models From Natural Language Supervision (2021, background): “Natural language supervision has also been explored for tasks beyond image classification including video understanding (Ramanathan et al., 2013; Miech et al., 2019), Reinforcement Learning (Hermann et al.”
MovieQA: Understanding Stories in Movies through Question-Answering (2015, background): “In [21], the authors retrieve relevant video clips for natural language queries, while [29] exploits captioned clips to learn action and role models.”
Jointly Modeling Deep Video and Compositional Text to Bridge Vision and Language in a Unified Framework (2015, background): “(Ramanathan, Liang, and Fei-Fei 2013) use a topic-based semantic relatedness measure to help action and role classification, (Yu and Siskind 2013) use HMMs to track sentences and learn the representation for word meanings with the help of video clips.”
Visual Storytelling (2016, background): “, 2015; Malinowski and Fritz, 2014), visual phrases (Sadeghi and Farhadi, 2011), video understanding (Ramanathan et al., 2013), and visual concepts (Krishna et al.”
Translating Video Content to Natural Language Descriptions (2013, background): “This work could be combined with approaches which automatically extract a semantic representation from a text description, which has recently been proposed in [22] for activities.”
Linking People in Videos with "Their" Names Using Coreference Resolution (2014, background): “Others propagate image captions to uncaptioned images using visual similarities [41, 3, 14, 33].”
Learning Temporal Embeddings for Complex Video Analysis (2015, methodology): “We follow the protocol of [7, 30] and use the same train/test splits.”
Semantic Aware Video Transcription Using Random Forest Classifiers (2014, methodology): “Topic model was used in [24] to convert text into topic distribution vectors and group mid-level actions.”
Learning semantic relationships for better action retrieval in images (2015, background): “Another line of work shares data between visually similar classes by learning grouping of class labels [3, 17, 21, 22, 28, 30, 31, 38, 45].”
A Review of Human Activity Recognition Methods (2015, background): “The recognition accuracy of such complex videos can also be improved by relating textual descriptions and visual context to a unified framework (Ramanathan et al., 2013).”
Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos (2017, background): “Another line of work aim to learn visual classifiers based on only textual supervision [5, 7, 11, 48].”
Tell me Dave: Context-sensitive grounding of natural language to manipulation instructions (2014, background): “In the area of computer vision, some works have considered relating phrases and attributes to images and videos [39, 15, 26, 25, 21, 50].”