These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
WaferSegClassNet -- A Light-weight Network for Classification and Segmentation of Semiconductor Wafer Defects
Metrics concerned:Accuracy, Mean Intersection over Union (IoU)
Traffic event cognition and reasoning in videos is an important task that has
a wide range of applications in intelligent transportation, assisted driving,
and autonomous vehicles. In this paper, we create a novel dataset,
SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA
based on the collected 10,080 in-the-wild videos and annotated 62,535 QA pairs,
for benchmarking the cognitive capability of causal inference and event
understanding models in complex traffic scenarios. Specifically, we propose 6
challenging reasoning tasks corresponding to various traffic scenarios, so as
to evaluate the reasoning capability over different kinds of complex yet
practical traffic events. Moreover, we propose Eclipse, a novel Efficient
glimpse network via dynamic inference, in order to achieve
computation-efficient and reliable video reasoning. The experiments show that
our method achieves superior performance while reducing the computation cost
significantly. The project page: https://github.com/SUTDCV/SUTD-TrafficQA.
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