no code implementations • 12 Apr 2024 • Tianyu Ding, Jinxin Zhou, Tianyi Chen, Zhihui Zhu, Ilya Zharkov, Luming Liang
Existing angle-based contour descriptors suffer from lossy representation for non-starconvex shapes.
no code implementations • 11 Apr 2024 • Guangzhi Wang, Tianyi Chen, Kamran Ghasedi, HsiangTao Wu, Tianyu Ding, Chris Nuesmeyer, Ilya Zharkov, Mohan Kankanhalli, Luming Liang
S3Editor is model-agnostic and compatible with various editing approaches.
1 code implementation • 15 Dec 2023 • Tianyi Chen, Tianyu Ding, Zhihui Zhu, Zeyu Chen, HsiangTao Wu, Ilya Zharkov, Luming Liang
Compressing a predefined deep neural network (DNN) into a compact sub-network with competitive performance is crucial in the efficient machine learning realm.
1 code implementation • 1 Dec 2023 • Tianyu Ding, Tianyi Chen, Haidong Zhu, Jiachen Jiang, Yiqi Zhong, Jinxin Zhou, Guangzhi Wang, Zhihui Zhu, Ilya Zharkov, Luming Liang
The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains, reshaping the artificial general intelligence landscape.
1 code implementation • 30 Nov 2023 • Jinxin Zhou, Tianyu Ding, Tianyi Chen, Jiachen Jiang, Ilya Zharkov, Zhihui Zhu, Luming Liang
We present DREAM, a novel training framework representing Diffusion Rectification and Estimation Adaptive Models, requiring minimal code changes (just three lines) yet significantly enhancing the alignment of training with sampling in diffusion models.
1 code implementation • 27 Nov 2023 • Haidong Zhu, Tianyu Ding, Tianyi Chen, Ilya Zharkov, Ram Nevatia, Luming Liang
CaesarNeRF explicitly models pose differences of reference views to combine scene-level semantic representations, providing a calibrated holistic understanding.
no code implementations • 7 Nov 2023 • Yatao Zhong, Ilya Zharkov
We present a lightweight model for high resolution portrait matting.
1 code implementation • 24 Oct 2023 • Tianyi Chen, Tianyu Ding, Badal Yadav, Ilya Zharkov, Luming Liang
Large Language Models (LLMs) have transformed the landscape of artificial intelligence, while their enormous size presents significant challenges in terms of computational costs.
1 code implementation • ICCV 2023 • Yiqi Zhong, Luming Liang, Ilya Zharkov, Ulrich Neumann
A central challenge of video prediction lies where the system has to reason the objects' future motions from image frames while simultaneously maintaining the consistency of their appearances across frames.
1 code implementation • 25 May 2023 • Tianyi Chen, Luming Liang, Tianyu Ding, Ilya Zharkov
To search an optimal sub-network within a general deep neural network (DNN), existing neural architecture search (NAS) methods typically rely on handcrafting a search space beforehand.
1 code implementation • 13 Mar 2023 • Tianyi Chen, Luming Liang, Tianyu Ding, Zhihui Zhu, Ilya Zharkov
We propose the second generation of Only-Train-Once (OTOv2), which first automatically trains and compresses a general DNN only once from scratch to produce a more compact model with competitive performance without fine-tuning.
no code implementations • 5 Oct 2022 • Yatao Zhong, Faezeh Amjadi, Ilya Zharkov
The model can optionally use one of the patterns as guidance for displacement estimation.
1 code implementation • 9 Sep 2022 • Tianyu Ding, Luming Liang, Zhihui Zhu, Tianyi Chen, Ilya Zharkov
As a result, we achieve a considerable performance gain with a quarter of the size of the original AdaCoF.
1 code implementation • CVPR 2022 • Zhicheng Geng, Luming Liang, Tianyu Ding, Ilya Zharkov
Space-time video super-resolution (STVSR) is the task of interpolating videos with both Low Frame Rate (LFR) and Low Resolution (LR) to produce High-Frame-Rate (HFR) and also High-Resolution (HR) counterparts.
Ranked #2 on Space-time Video Super-resolution on Vimeo90K-Medium
Space-time Video Super-resolution Video Frame Interpolation +1
1 code implementation • CVPR 2021 • Tianyu Ding, Luming Liang, Zhihui Zhu, Ilya Zharkov
DNN-based frame interpolation--that generates the intermediate frames given two consecutive frames--typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e. g., mobile devices.
Ranked #1 on Video Frame Interpolation on Middlebury (LPIPS metric)
no code implementations • 18 Apr 2020 • Hamid Reza Vaezi Joze, Ilya Zharkov, Karlton Powell, Carl Ringler, Luming Liang, Andy Roulston, Moshe Lutz, Vivek Pradeep
To our knowledge this is the most complete dataset for super resolution, ISP and image quality enhancement.
no code implementations • 29 Mar 2018 • Mehran Khodabandeh, Hamid Reza Vaezi Joze, Ilya Zharkov, Vivek Pradeep
Therefore, the ability to generate de novo data or expand an existing data set, however small, in order to satisfy data requirement of current networks may be invaluable.