Search Results for author: Ming-Jie Sun

Found 6 papers, 4 papers with code

Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach

1 code implementation19 Nov 2019 Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Ming-Jie Sun, Kai-Zhu Huang

Such reliable regions are then directly served as ground-truth labels for the parallel segmentation branch, where a newly designed dense energy loss function is adopted for optimization.

Image Classification Weakly-Supervised Semantic Segmentation

Progressive Sample Mining and Representation Learning for One-Shot Person Re-identification with Adversarial Samples

1 code implementation2 Nov 2019 Hui Li, Jimin Xiao, Ming-Jie Sun, Eng Gee Lim, Yao Zhao

To tackle this problem, we propose to iteratively guess pseudo labels for the unlabeled image samples, which are later used to update the re-identification model together with the labelled samples.

Person Re-Identification Representation Learning

Rethinking the Value of Network Pruning

2 code implementations ICLR 2019 Zhuang Liu, Ming-Jie Sun, Tinghui Zhou, Gao Huang, Trevor Darrell

Our observations are consistent for multiple network architectures, datasets, and tasks, which imply that: 1) training a large, over-parameterized model is often not necessary to obtain an efficient final model, 2) learned "important" weights of the large model are typically not useful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited "important" weights, is more crucial to the efficiency in the final model, which suggests that in some cases pruning can be useful as an architecture search paradigm.

Network Pruning Neural Architecture Search

Adaptive foveated single-pixel imaging with dynamic super-sampling

no code implementations27 Jul 2016 David B. Phillips, Ming-Jie Sun, Jonathan M. Taylor, Matthew P. Edgar, Stephen M. Barnett, Graham G. Gibson, Miles J. Padgett

To mitigate this, a range of compressive sensing techniques have been developed which rely on a priori knowledge of the scene to reconstruct images from an under-sampled set of measurements.

Compressive Sensing Frame

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