activity
20162022
most citedCoarse to Fine Multi-Resolution Temporal Convolutional Network

21 citations · 66 across the 12 of their papers we have counts for

collaborators

24 papers

cs.CV20216 cited

Robust Semantic Segmentation with Superpixel-Mix

Gianni Franchi, Nacim Belkhir, Mai Lan Ha +4

Along with predictive performance and runtime speed, reliability is a key requirement for real-world semantic segmentation. Reliability encompasses robustness, predictive uncertain…

cs.CV20214 cited

Technical Report: Temporal Aggregate Representations

Fadime Sener, Dibyadip Chatterjee, Angela Yao

This technical report extends our work presented in [9] with more experiments. In [9], we tackle long-term video understanding, which requires reasoning from current and past or fu…

cs.CV20215 cited

Towards Compact Single Image Super-Resolution via Contrastive Self-distillation

Yanbo Wang, Shaohui Lin, Yanyun Qu +4

Convolutional neural networks (CNNs) are highly successful for super-resolution (SR) but often require sophisticated architectures with heavy memory cost and computational overhead…

cs.CV202121 cited

Coarse to Fine Multi-Resolution Temporal Convolutional Network

Dipika Singhania, Rahul Rahaman, Angela Yao

Temporal convolutional networks (TCNs) are a commonly used architecture for temporal video segmentation. TCNs however, tend to suffer from over-segmentation errors and require addi…

cs.CV202110 cited

NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions

Junbin Xiao, Xindi Shang, Angela Yao +1

We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. Based on…

cs.CV2020

Multi-Stage Fusion for One-Click Segmentation

Soumajit Majumder, Ansh Khurana, Abhinav Rai +1

Segmenting objects of interest in an image is an essential building block of applications such as photo-editing and image analysis. Under interactive settings, one should achieve g…