14 citations · 26 across the 6 of their papers we have counts for
6 papers
Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles
Zifu Wang, Junyi Zhu, Bo Tang +4
The application of rule-based reinforcement learning (RL) to multimodal large language models (MLLMs) introduces unique challenges and potential deviations from findings in text-on…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union
Zifu Wang, Maxim Berman, Amal Rannen-Triki +6
Semantic segmentation datasets often exhibit two types of imbalance: \textit{class imbalance}, where some classes appear more frequently than others and \textit{size imbalance}, wh…
Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels
Zifu Wang, Teodora Popordanoska, Jeroen Bertels +2
The soft Dice loss (SDL) has taken a pivotal role in numerous automated segmentation pipelines in the medical imaging community. Over the last years, some reasons behind its superi…
Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels
Zifu Wang, Xuefei Ning, Matthew B. Blaschko
Intersection over Union (IoU) losses are surrogates that directly optimize the Jaccard index. Leveraging IoU losses as part of the loss function have demonstrated superior performa…
MRF-UNets: Searching UNet with Markov Random Fields
Zifu Wang, Matthew B. Blaschko
UNet [27] is widely used in semantic segmentation due to its simplicity and effectiveness. However, its manually-designed architecture is applied to a large number of problem setti…