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5 papers · 2 filters
Channel selection using Gumbel Softmax
Charles Herrmann, Richard Strong Bowen, Ramin Zabih
Important applications such as mobile computing require reducing the computational costs of neural network inference. Ideally, applications would specify their preferred tradeoff b…
Learning to Evaluate Image Captioning
Yin Cui, Guandao Yang, Andreas Veit +2
Evaluation metrics for image captioning face two challenges. Firstly, commonly used metrics such as CIDEr, METEOR, ROUGE and BLEU often do not correlate well with human judgments.…
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
Yin Cui, Yang Song, Chen Sun +2
Transferring the knowledge learned from large scale datasets (e.g., ImageNet) via fine-tuning offers an effective solution for domain-specific fine-grained visual categorization (F…
Learning from Multi-domain Artistic Images for Arbitrary Style Transfer
Zheng Xu, Michael Wilber, Chen Fang +2
We propose a fast feed-forward network for arbitrary style transfer, which can generate stylized image for previously unseen content and style image pairs. Besides the traditional…
Learning Intrinsic Image Decomposition from Watching the World
Zhengqi Li, Noah Snavely
Single-view intrinsic image decomposition is a highly ill-posed problem, and so a promising approach is to learn from large amounts of data. However, it is difficult to collect gro…