3 research outputs found
ContextMix: A context-aware data augmentation method for industrial visual inspection systems
While deep neural networks have achieved remarkable performance, data
augmentation has emerged as a crucial strategy to mitigate overfitting and
enhance network performance. These techniques hold particular significance in
industrial manufacturing contexts. Recently, image mixing-based methods have
been introduced, exhibiting improved performance on public benchmark datasets.
However, their application to industrial tasks remains challenging. The
manufacturing environment generates massive amounts of unlabeled data on a
daily basis, with only a few instances of abnormal data occurrences. This leads
to severe data imbalance. Thus, creating well-balanced datasets is not
straightforward due to the high costs associated with labeling. Nonetheless,
this is a crucial step for enhancing productivity. For this reason, we
introduce ContextMix, a method tailored for industrial applications and
benchmark datasets. ContextMix generates novel data by resizing entire images
and integrating them into other images within the batch. This approach enables
our method to learn discriminative features based on varying sizes from resized
images and train informative secondary features for object recognition using
occluded images. With the minimal additional computation cost of image
resizing, ContextMix enhances performance compared to existing augmentation
techniques. We evaluate its effectiveness across classification, detection, and
segmentation tasks using various network architectures on public benchmark
datasets. Our proposed method demonstrates improved results across a range of
robustness tasks. Its efficacy in real industrial environments is particularly
noteworthy, as demonstrated using the passive component dataset.Comment: Accepted to EAA
NICE 2023 Zero-shot Image Captioning Challenge
In this report, we introduce NICE
project\footnote{\url{https://nice.lgresearch.ai/}} and share the results and
outcomes of NICE challenge 2023. This project is designed to challenge the
computer vision community to develop robust image captioning models that
advance the state-of-the-art both in terms of accuracy and fairness. Through
the challenge, the image captioning models were tested using a new evaluation
dataset that includes a large variety of visual concepts from many domains.
There was no specific training data provided for the challenge, and therefore
the challenge entries were required to adapt to new types of image descriptions
that had not been seen during training. This report includes information on the
newly proposed NICE dataset, evaluation methods, challenge results, and
technical details of top-ranking entries. We expect that the outcomes of the
challenge will contribute to the improvement of AI models on various
vision-language tasks.Comment: Tech report, project page https://nice.lgresearch.ai