Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Bear, Daniel M."'
Autor:
Venkatesh, Rahul, Chen, Honglin, Feigelis, Kevin, Bear, Daniel M., Jedoui, Khaled, Kotar, Klemen, Binder, Felix, Lee, Wanhee, Liu, Sherry, Smith, Kevin A., Fan, Judith E., Yamins, Daniel L. K.
The ability to understand physical dynamics is critical for agents to act in the world. Here, we use Counterfactual World Modeling (CWM) to extract vision structures for dynamics understanding. CWM uses a temporally-factored masking policy for masked
Externí odkaz:
http://arxiv.org/abs/2312.06721
Autor:
Bear, Daniel M., Feigelis, Kevin, Chen, Honglin, Lee, Wanhee, Venkatesh, Rahul, Kotar, Klemen, Durango, Alex, Yamins, Daniel L. K.
Leading approaches in machine vision employ different architectures for different tasks, trained on costly task-specific labeled datasets. This complexity has held back progress in areas, such as robotics, where robust task-general perception remains
Externí odkaz:
http://arxiv.org/abs/2306.01828
Autor:
Chen, Honglin, Venkatesh, Rahul, Friedman, Yoni, Wu, Jiajun, Tenenbaum, Joshua B., Yamins, Daniel L. K., Bear, Daniel M.
Self-supervised, category-agnostic segmentation of real-world images is a challenging open problem in computer vision. Here, we show how to learn static grouping priors from motion self-supervision by building on the cognitive science concept of a Sp
Externí odkaz:
http://arxiv.org/abs/2205.08515
Autor:
Bear, Daniel M., Wang, Elias, Mrowca, Damian, Binder, Felix J., Tung, Hsiao-Yu Fish, Pramod, R. T., Holdaway, Cameron, Tao, Sirui, Smith, Kevin, Sun, Fan-Yun, Fei-Fei, Li, Kanwisher, Nancy, Tenenbaum, Joshua B., Yamins, Daniel L. K., Fan, Judith E.
While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments. Here we introduce Physion, a dataset and benchmark for rigorously evaluating the ability to pred
Externí odkaz:
http://arxiv.org/abs/2106.08261
Autor:
Gan, Chuang, Schwartz, Jeremy, Alter, Seth, Mrowca, Damian, Schrimpf, Martin, Traer, James, De Freitas, Julian, Kubilius, Jonas, Bhandwaldar, Abhishek, Haber, Nick, Sano, Megumi, Kim, Kuno, Wang, Elias, Lingelbach, Michael, Curtis, Aidan, Feigelis, Kevin, Bear, Daniel M., Gutfreund, Dan, Cox, David, Torralba, Antonio, DiCarlo, James J., Tenenbaum, Joshua B., McDermott, Josh H., Yamins, Daniel L. K.
We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. Unique properties in
Externí odkaz:
http://arxiv.org/abs/2007.04954
Autor:
Bear, Daniel M., Fan, Chaofei, Mrowca, Damian, Li, Yunzhu, Alter, Seth, Nayebi, Aran, Schwartz, Jeremy, Fei-Fei, Li, Wu, Jiajun, Tenenbaum, Joshua B., Yamins, Daniel L. K.
Convolutional Neural Networks (CNNs) have proved exceptional at learning representations for visual object categorization. However, CNNs do not explicitly encode objects, parts, and their physical properties, which has limited CNNs' success on tasks
Externí odkaz:
http://arxiv.org/abs/2006.12373
Autor:
Li, Yunzhu, Lin, Toru, Yi, Kexin, Bear, Daniel M., Yamins, Daniel L. K., Wu, Jiajun, Tenenbaum, Joshua B., Torralba, Antonio
Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions. The abilities to perform physical reasoning and to adapt to new environments, while intrinsic to humans
Externí odkaz:
http://arxiv.org/abs/2004.13664
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