no code implementations • 1 Apr 2024 • Akshita Gupta, Gaurav Mittal, Ahmed Magooda, Ye Yu, Graham W. Taylor, Mei Chen
Temporal Action Localization (TAL) involves localizing and classifying action snippets in an untrimmed video.
1 code implementation • 24 Jul 2023 • Christopher Clarke, Matthew Hall, Gaurav Mittal, Ye Yu, Sandra Sajeev, Jason Mars, Mei Chen
In this paper, we present Rule By Example (RBE): a novel exemplar-based contrastive learning approach for learning from logical rules for the task of textual content moderation.
no code implementations • 17 May 2023 • Jialin Yuan, Ye Yu, Gaurav Mittal, Matthew Hall, Sandra Sajeev, Mei Chen
There is a rapidly growing need for multimodal content moderation (CM) as more and more content on social media is multimodal in nature.
no code implementations • CVPR 2023 • Mamshad Nayeem Rizve, Gaurav Mittal, Ye Yu, Matthew Hall, Sandra Sajeev, Mubarak Shah, Mei Chen
To address this, we present PivoTAL, Prior-driven Supervision for Weakly-supervised Temporal Action Localization, to approach WTAL from a localization-by-localization perspective by learning to localize the action snippets directly.
Weakly Supervised Action Localization Weakly Supervised Temporal Action Localization
no code implementations • CVPR 2023 • Lan Wang, Gaurav Mittal, Sandra Sajeev, Ye Yu, Matthew Hall, Vishnu Naresh Boddeti, Mei Chen
We present ProTeGe as the first method to perform VTG-based untrimmed pretraining to bridge the gap between trimmed pretrained backbones and downstream VTG tasks.
1 code implementation • Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2022 • Jun Yu, Liwen Zhang, Shenshen Du, Hao Chang, Keda Lu, Zhong Zhang, Ye Yu, Lei Wang, Qiang Ling
To overcome these difficulties, this paper first select fewer but suitable data augmentation methods to improve the accuracy of the supervised model based on the labeled training set, which is suitable for the characteristics of hyperspectral images.
no code implementations • 1 Aug 2022 • Ye Yu, Jialin Yuan, Gaurav Mittal, Li Fuxin, Mei Chen
It captures object motion in the video via a novel optical flow calibration module that fuses the segmentation mask with optical flow estimation to improve within-object optical flow smoothness and reduce noise at object boundaries.
Ranked #1 on Video Object Segmentation on DAVIS 2017 (test-dev) (using extra training data)
2 code implementations • Machine Learning 2022 • Hao Chang, Guochen Xie, Jun Yu, Qiang Ling, Fang Gao, Ye Yu
Semi-supervised Fine-Grained Recognition is a challenging task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch.
no code implementations • CVPR 2022 • Junwen Chen, Gaurav Mittal, Ye Yu, Yu Kong, Mei Chen
We present GateHUB, Gated History Unit with Background Suppression, that comprises a novel position-guided gated cross-attention mechanism to enhance or suppress parts of the history as per how informative they are for current frame prediction.
Ranked #1 on Online Action Detection on TVSeries
no code implementations • 25 Oct 2021 • Yu Gong, Ye Yu, Gaurav Mittal, Greg Mori, Mei Chen
Importantly, we argue and empirically demonstrate that MUSE, compared to other feature discrepancy functions, is a more functional proxy to introduce dependency and effectively improve the expressivity of all features in the knowledge distillation framework.
no code implementations • ICCV 2021 • Jay Patravali, Gaurav Mittal, Ye Yu, Fuxin Li, Mei Chen
We present MetaUVFS as the first Unsupervised Meta-learning algorithm for Video Few-Shot action recognition.
no code implementations • ECCV 2020 • Ye Yu, Abhimitra Meka, Mohamed Elgharib, Hans-Peter Seidel, Christian Theobalt, William A. P. Smith
Outdoor scene relighting is a challenging problem that requires good understanding of the scene geometry, illumination and albedo.
1 code implementation • ICLR 2021 • Yunsheng Li, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Ye Yu, Lu Yuan, Zicheng Liu, Mei Chen, Nuno Vasconcelos
It has two limitations: (a) it increases the number of convolutional weights by K-times, and (b) the joint optimization of dynamic attention and static convolution kernels is challenging.
1 code implementation • NeurIPS 2021 • Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang Wang, Zicheng Liu, Mei Chen, Lu Yuan
We propose a paradigm shift from fitting the whole architecture space using one strong predictor, to progressively fitting a search path towards the high-performance sub-space through a set of weaker predictors.
1 code implementation • 12 Feb 2021 • Ye Yu, William A. P. Smith
In this paper we show how to perform scene-level inverse rendering to recover shape, reflectance and lighting from a single, uncontrolled image using a fully convolutional neural network.
no code implementations • 1 Jan 2021 • Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang Wang, Zicheng Liu, Mei Chen, Lu Yuan
Rather than expecting a single strong predictor to model the whole space, we seek a progressive line of weak predictors that can connect a path to the best architecture, thus greatly simplifying the learning task of each predictor.
no code implementations • 2 Sep 2019 • Ye Yu, Niraj K. Jha
To take advantage of sparsity, some accelerator designs explore sparsity encoding and evaluation on CNN accelerators.
Hardware Architecture
no code implementations • 18 Mar 2019 • Ye Yu, Yingmin Li, Shuai Che, Niraj K. Jha, Weifeng Zhang
It models the accelerator design task as a multi-dimensional optimization problem.
1 code implementation • CVPR 2019 • Ye Yu, William A. P. Smith
By incorporating a differentiable renderer, our network can learn from self-supervision.