no code implementations • 21 Dec 2023 • Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, Krishna Somandepalli, Hassan Akbari, Yair Alon, Yong Cheng, Josh Dillon, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez, David Minnen, Mikhail Sirotenko, Kihyuk Sohn, Xuan Yang, Hartwig Adam, Ming-Hsuan Yang, Irfan Essa, Huisheng Wang, David A. Ross, Bryan Seybold, Lu Jiang
We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals.
Ranked #3 on Text-to-Video Generation on MSR-VTT
no code implementations • NeurIPS 2023 • Hassan Akbari, Dan Kondratyuk, Yin Cui, Rachel Hornung, Huisheng Wang, Hartwig Adam
We conduct extensive empirical studies and reveal the following key insights: 1) Performing gradient descent updates by alternating on diverse modalities, loss functions, and tasks, with varying input resolutions, efficiently improves the model.
Ranked #1 on Zero-Shot Action Recognition on Kinetics (using extra training data)
no code implementations • 14 Dec 2021 • Qing Li, Boqing Gong, Yin Cui, Dan Kondratyuk, Xianzhi Du, Ming-Hsuan Yang, Matthew Brown
The experiments show that the resultant unified foundation transformer works surprisingly well on both the vision-only and text-only tasks, and the proposed knowledge distillation and gradient masking strategy can effectively lift the performance to approach the level of separately-trained models.
3 code implementations • CVPR 2021 • Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong
We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference.
Ranked #3 on Action Classification on Charades
no code implementations • ICLR 2022 • Xiaofang Wang, Dan Kondratyuk, Eric Christiansen, Kris M. Kitani, Yair Alon, Elad Eban
Committee-based models (ensembles or cascades) construct models by combining existing pre-trained ones.
no code implementations • 1 May 2020 • Dan Kondratyuk, Mingxing Tan, Matthew Brown, Boqing Gong
Ensembling is a simple and popular technique for boosting evaluation performance by training multiple models (e. g., with different initializations) and aggregating their predictions.
1 code implementation • WS 2019 • Dan Kondratyuk
We present our CHARLES-SAARLAND system for the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology, in task 2, Morphological Analysis and Lemmatization in Context.
3 code implementations • IJCNLP 2019 • Dan Kondratyuk, Milan Straka
We present UDify, a multilingual multi-task model capable of accurately predicting universal part-of-speech, morphological features, lemmas, and dependency trees simultaneously for all 124 Universal Dependencies treebanks across 75 languages.
Ranked #2 on Dependency Parsing on French GSD