Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals
By Katsuyuki Nakamura, Serena Yeung, Alexandre Alahi, Li Fei-Fei. 2017
73 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Multimodal Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Antonino Furnari
- Giovanni Maria Farinella
- Kristen Grauman
- Luc Van Gool
- Cordelia Schmid
- Kate Saenko
- Jitendra Malik
- Trevor Darrell
In Multimodal Learning
- Kate Saenko
- Giovanni Maria Farinella
- Antonino Furnari
- Vasili Ramanishka
- Yi-Ting Chen
- Marcus Rohrbach
- Trevor Darrell
- Devi Parikh
Citations in the corpus
- Ego4D: Around the World in 3,000 Hours of Egocentric Video (2021, neutral): “Aside from daily life activity, prior ego datasets focus on conversation [170], inter-person interactions [66,168,194,231], place localization [183,208], multimodal sensor data [124,166,204], human hands [16, 134] human-object interaction [106, 184], and object tracking [56].”
- Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, background): “If high-fidelity synthetic wearable sensor data can be generated from the available real-world video datasets (such as Stanford-ECM dataset [294]) or online video corpus, it may help alleviate the in-the-field data scarcity issue.”
- Predicting the Future from First Person (Egocentric) Vision: A Survey (2021, background): “Some examples of these datasets are: EgoMotion dataset [2], Daily Intentions Dataset [103], First-person Locomotion [76], Stanford-ECM [104], Stanford 2D-3D-S [105].”
- Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning (2018, methodology): “The Stanford-ECM dataset [25] is related to our dataset in the following two aspects.”