activity
20142023
most citedLook at What I'm Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos

13 citations · 26 across the 7 of their papers we have counts for

collaborators
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6 papers · 1 filter

cs.CV20222 cited

Video Activity Localisation with Uncertainties in Temporal Boundary

Jiabo Huang, Hailin Jin, Shaogang Gong +1

Current methods for video activity localisation over time assume implicitly that activity temporal boundaries labelled for model training are determined and precise. However, in un…

cs.CV2021

Time-Equivariant Contrastive Video Representation Learning

Simon Jenni, Hailin Jin

We introduce a novel self-supervised contrastive learning method to learn representations from unlabelled videos. Existing approaches ignore the specifics of input distortions, e.g…

cs.CV202113 cited

Look at What I'm Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos

Reuben Tan, Bryan A. Plummer, Kate Saenko +2

We introduce the task of spatially localizing narrated interactions in videos. Key to our approach is the ability to learn to spatially localize interactions with self-supervision…

cs.CV20214 cited

Cross Modal Retrieval with Querybank Normalisation

Simion-Vlad Bogolin, Ioana Croitoru, Hailin Jin +2

Profiting from large-scale training datasets, advances in neural architecture design and efficient inference, joint embeddings have become the dominant approach for tackling cross-…

cs.CV20155 cited

Collaborative Feature Learning from Social Media

Chen Fang, Hailin Jin, Jianchao Yang +1

Image feature representation plays an essential role in image recognition and related tasks. The current state-of-the-art feature learning paradigm is supervised learning from labe…

cs.CV20141 cited

Decomposition-Based Domain Adaptation for Real-World Font Recognition

Zhangyang Wang, Jianchao Yang, Hailin Jin +4

We present a domain adaption framework to address a domain mismatch between synthetic training and real-world testing data. We demonstrate our method on a challenging fine-grain cl…