Temporal Aggregate Representations for Long-Range Video Understanding

By Fadime Sener, Dipika Singhania, Angela Yao. 2020

180 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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

1. Arsha Nagrani
2. Paul Hongsuck Seo
3. Cordelia Schmid
4. Andrew Zisserman
5. Luc Van Gool
6. Xiaogang Wang
7. Trevor Darrell
8. Andrea Vedaldi

Citations in the corpus

• Look Before you Speak: Visually Contextualized Utterances (2020, methodology): “2, we also compare to two state-of-the-art vision-only models for next step prediction, RULSTM [24] and TAR [66]6 Pretraining on BERT and using the MLM loss particularly help prevent our multimodal model from overfitting on this small dataset.”
• MovieChat: From Dense Token to Sparse Memory for Long Video Understanding (2023, methodology): “Prior arts use 3D volumn [90], object/humancentric [67, 91], or other forms [71, 92] as video representations.”
• Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos (2022, background): “What space should the model predict on? Recent approaches [25,26,28,72] have been proposed to predict the discrete future action category given a sequence of frames as inputs, namely action anticipation.”