Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Zhu, Tyler"'
Autor:
Gupta, Ritwik, Li, Shufan, Zhu, Tyler, Malik, Jitendra, Darrell, Trevor, Mangalam, Karttikeya
Modern computer vision pipelines handle large images in one of two sub-optimal ways: down-sampling or cropping. These two methods incur significant losses in the amount of information and context present in an image. There are many downstream applica
Externí odkaz:
http://arxiv.org/abs/2403.01915
Autor:
Zhu, Tyler, Mangalam, Karttikeya
The growing size of datasets and deep learning models has made faster and memory-efficient training crucial. Reversible transformers have recently been introduced as an exciting new method for extremely memory-efficient training, but they come with a
Externí odkaz:
http://arxiv.org/abs/2306.09342
Autor:
Zhu, Luyang, Yang, Dawei, Zhu, Tyler, Reda, Fitsum, Chan, William, Saharia, Chitwan, Norouzi, Mohammad, Kemelmacher-Shlizerman, Ira
Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person. A key challenge is to synthesize a photorealistic detail-preserving visualization of
Externí odkaz:
http://arxiv.org/abs/2306.08276
With a proliferation of generic domain-adaptation approaches, we report a simple yet effective technique for learning difficult per-pixel 2.5D and 3D regression representations of articulated people. We obtained strong sim-to-real domain generalizati
Externí odkaz:
http://arxiv.org/abs/2007.15506
Autor:
Hendrycks, Dan, Basart, Steven, Mu, Norman, Kadavath, Saurav, Wang, Frank, Dorundo, Evan, Desai, Rahul, Zhu, Tyler, Parajuli, Samyak, Guo, Mike, Song, Dawn, Steinhardt, Jacob, Gilmer, Justin
We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. With our new datasets, we take stock of previously proposed methods for improving ou
Externí odkaz:
http://arxiv.org/abs/2006.16241
Autor:
Bazarevsky, Valentin, Grishchenko, Ivan, Raveendran, Karthik, Zhu, Tyler, Zhang, Fan, Grundmann, Matthias
We present BlazePose, a lightweight convolutional neural network architecture for human pose estimation that is tailored for real-time inference on mobile devices. During inference, the network produces 33 body keypoints for a single person and runs
Externí odkaz:
http://arxiv.org/abs/2006.10204
Autor:
Triantafillou, Eleni, Zhu, Tyler, Dumoulin, Vincent, Lamblin, Pascal, Evci, Utku, Xu, Kelvin, Goroshin, Ross, Gelada, Carles, Swersky, Kevin, Manzagol, Pierre-Antoine, Larochelle, Hugo
Publikováno v:
International Conference on Learning Representations (2020)
Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle it, we find the procedure and datasets that are used to assess their progress lacking. To address thi
Externí odkaz:
http://arxiv.org/abs/1903.03096
Autor:
Papandreou, George, Zhu, Tyler, Chen, Liang-Chieh, Gidaris, Spyros, Tompson, Jonathan, Murphy, Kevin
We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model. The proposed PersonLab model tackles both semantic-level reasoning and object-p
Externí odkaz:
http://arxiv.org/abs/1803.08225
Autor:
Papandreou, George, Zhu, Tyler, Kanazawa, Nori, Toshev, Alexander, Tompson, Jonathan, Bregler, Chris, Murphy, Kevin
We propose a method for multi-person detection and 2-D pose estimation that achieves state-of-art results on the challenging COCO keypoints task. It is a simple, yet powerful, top-down approach consisting of two stages. In the first stage, we predict
Externí odkaz:
http://arxiv.org/abs/1701.01779
Autor:
Hendrycks, Dan, Basart, Steven, Mu, Norman, Kadavath, Saurav, Wang, Frank, Dorundo, Evan, Desai, Rahul, Zhu, Tyler, Parajuli, Samyak, Guo, Mike, Song, Dawn, Steinhardt, Jacob, Gilmer, Justin
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. With our new datasets, we take stock of previously proposed methods for improving ou