110 citations · 420 across the 55 of their papers we have counts for
14 papers · 1 filter
Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation
Ziwei Yu, Linlin Yang, Shicheng Chen +1
This paper addresses the 3D point cloud reconstruction and 3D pose estimation of the human hand from a single RGB image. To that end, we present a novel pipeline for local and glob…
Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentation
Dipika Singhania, Rahul Rahaman, Angela Yao
Temporal action segmentation classifies the action of each frame in (long) video sequences. Due to the high cost of frame-wise labeling, we propose the first semi-supervised method…
Video as Conditional Graph Hierarchy for Multi-Granular Question Answering
Junbin Xiao, Angela Yao, Zhiyuan Liu +3
Video question answering requires the models to understand and reason about both the complex video and language data to correctly derive the answers. Existing efforts have been foc…
Weakly-Supervised Dense Action Anticipation
Haotong Zhang, Fuhai Chen, Angela Yao
Dense anticipation aims to forecast future actions and their durations for long horizons. Existing approaches rely on fully-labelled data, i.e. sequences labelled with all future a…
Temporal Action Segmentation with High-level Complex Activity Labels
Guodong Ding, Angela Yao
The temporal action segmentation task segments videos temporally and predicts action labels for all frames. Fully supervising such a segmentation model requires dense frame-wise ac…
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…
Accelerating Video Object Segmentation with Compressed Video
Kai Xu, Angela Yao
We propose an efficient plug-and-play acceleration framework for semi-supervised video object segmentation by exploiting the temporal redundancies in videos presented by the compre…
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…
Learning Deep Morphological Networks with Neural Architecture Search
Yufei Hu, Nacim Belkhir, Jesus Angulo +2
Deep Neural Networks (DNNs) are generated by sequentially performing linear and non-linear processes. Using a combination of linear and non-linear procedures is critical for genera…
Transformed ROIs for Capturing Visual Transformations in Videos
Abhinav Rai, Fadime Sener, Angela Yao
Modeling the visual changes that an action brings to a scene is critical for video understanding. Currently, CNNs process one local neighbourhood at a time, thus contextual relatio…
Transferring Knowledge from Text to Video: Zero-Shot Anticipation for Procedural Actions
Fadime Sener, Rishabh Saraf, Angela Yao
Can we teach a robot to recognize and make predictions for activities that it has never seen before? We tackle this problem by learning models for video from text. This paper prese…
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…
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…
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…