3.5k citations · 7.6k across the 18 of their papers we have counts for
19 papers
Pinterest Canvas: Large-Scale Image Generation at Pinterest
Yu Wang, Eric Tzeng, Raymond Shiau +3
While recent image generation models demonstrate a remarkable ability to handle a wide variety of image generation tasks, this flexibility makes them hard to control via prompting…
Large-scale Reinforcement Learning for Diffusion Models
Yinan Zhang, Eric Tzeng, Yilun Du +1
Text-to-image diffusion models are a class of deep generative models that have demonstrated an impressive capacity for high-quality image generation. However, these models are susc…
Toward Transformer-Based Object Detection
Josh Beal, Eric Kim, Eric Tzeng +3
Transformers have become the dominant model in natural language processing, owing to their ability to pretrain on massive amounts of data, then transfer to smaller, more specific t…
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
Mohammad Reza Loghmani, Luca Robbiano, Mirco Planamente +3
Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distribution…
Revisiting Few-shot Activity Detection with Class Similarity Control
Huijuan Xu, Ximeng Sun, Eric Tzeng +3
Many interesting events in the real world are rare making preannotated machine learning ready videos a rarity in consequence. Thus, temporal activity detection models that are able…
Semantic Bottleneck Scene Generation
Samaneh Azadi, Michael Tschannen, Eric Tzeng +3
Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models, we propose a semantic bottl…