Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Dvornik, Nikita"'
Autor:
Yang, Anqi Joyce, Casas, Sergio, Dvornik, Nikita, Segal, Sean, Xiong, Yuwen, Hu, Jordan Sir Kwang, Fang, Carter, Urtasun, Raquel
Publikováno v:
CoRL 2023
A major bottleneck to scaling-up training of self-driving perception systems are the human annotations required for supervision. A promising alternative is to leverage "auto-labelling" offboard perception models that are trained to automatically gene
Externí odkaz:
http://arxiv.org/abs/2311.01444
Autor:
Abdelsalam, Mohamed Ashraf, Rangrej, Samrudhdhi B., Hadji, Isma, Dvornik, Nikita, Derpanis, Konstantinos G., Fazly, Afsaneh
We study the problem of future step anticipation in procedural videos. Given a video of an ongoing procedural activity, we predict a plausible next procedure step described in rich natural language. While most previous work focus on the problem of da
Externí odkaz:
http://arxiv.org/abs/2310.08312
Publikováno v:
2023 International Conference on Robotics and Automation
When humans perform a task with an articulated object, they interact with the object only in a handful of ways, while the space of all possible interactions is nearly endless. This is because humans have prior knowledge about what interactions are li
Externí odkaz:
http://arxiv.org/abs/2305.17565
Autor:
Dvornik, Nikita, Hadji, Isma, Zhang, Ran, Derpanis, Konstantinos G., Garg, Animesh, Wildes, Richard P., Jepson, Allan D.
Instructional videos are an important resource to learn procedural tasks from human demonstrations. However, the instruction steps in such videos are typically short and sparse, with most of the video being irrelevant to the procedure. This motivates
Externí odkaz:
http://arxiv.org/abs/2304.13265
Autor:
Ma, Avery, Dvornik, Nikita, Zhang, Ran, Pishdad, Leila, Derpanis, Konstantinos G., Fazly, Afsaneh
Data augmentation is a key element for training accurate models by reducing overfitting and improving generalization. For image classification, the most popular data augmentation techniques range from simple photometric and geometrical transformation
Externí odkaz:
http://arxiv.org/abs/2211.00113
Understanding dynamics from visual observations is a challenging problem that requires disentangling individual objects from the scene and learning their interactions. While recent object-centric models can successfully decompose a scene into objects
Externí odkaz:
http://arxiv.org/abs/2210.05861
Autor:
Dvornik, Nikita, Hadji, Isma, Pham, Hai, Bhatt, Dhaivat, Martinez, Brais, Fazly, Afsaneh, Jepson, Allan D.
Publikováno v:
ECCV 2022
In this work, we consider the problem of weakly-supervised multi-step localization in instructional videos. An established approach to this problem is to rely on a given list of steps. However, in reality, there is often more than one way to execute
Externí odkaz:
http://arxiv.org/abs/2210.04996
Autor:
Zhao, He, Hadji, Isma, Dvornik, Nikita, Derpanis, Konstantinos G., Wildes, Richard P., Jepson, Allan D.
In this paper, we study the problem of procedure planning in instructional videos. Here, an agent must produce a plausible sequence of actions that can transform the environment from a given start to a desired goal state. When learning procedure plan
Externí odkaz:
http://arxiv.org/abs/2205.02300
In this work, we consider the problem of sequence-to-sequence alignment for signals containing outliers. Assuming the absence of outliers, the standard Dynamic Time Warping (DTW) algorithm efficiently computes the optimal alignment between two (gener
Externí odkaz:
http://arxiv.org/abs/2108.11996
Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples. In this work, we propose a
Externí odkaz:
http://arxiv.org/abs/2003.09338