Learning with Free Object Segments for Long-Tailed Instance Segmentation
In this paper, we explore the possibility to increase the training examples without laborious data collection and annotation for long-tailed instance segmentation. We find that an abundance of instance segments can potentially be obtained freely from object-centric images, according to two insights: (i) an object-centric image usually contains one salient object in a simple background; (ii) objects from the same class often share similar appearances or similar contrasts to the background. Motivated by these insights, we propose a simple and scalable framework FREESEG for extracting and leveraging these “free” object segments to facilitate model training. Concretely, we investigate the similarity among object-centric images of the same class to propose candidate segments of foreground instances, followed by a novel ranking of segment quality. The resulting high quality object segments can then be used to augment the existing long-tailed datasets, e.g., by copying and pasting the segments onto the original training images. Extensive experiments show that FREESEG yields substantial improvements on top of strong baselines and achieves state-of-the-art accuracy for segmenting rare object categories.
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10338449
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L3D-IVU: Workshop on Learning with Limited Labeled Data for Image and Video Understanding, in conjunction with the IEEE / CVF Computer Vision and Pattern Recognition Conference
5. Label noise in real-world datasets encodes wrong correlation patterns and impairs the generalization of deep neural networks (DNNs). It is critical to find efficient ways to detect corrupted patterns. Current methods primarily focus on designing robust training techniques to prevent DNNs from memorizing corrupted patterns. These approaches often require customized training processes and may overfit corrupted patterns, leading to a performance drop in detection. In this paper, from a more data-centric perspective, we propose a training-free solution to detect corrupted labels. Intuitively, closer'' instances are more likely to share the same clean label. Based on the neighborhood information, we propose two methods: the first one uses local voting" via checking the noisy label consensuses of nearby features. The second one is a ranking-based approach that scores each instance and filters out a guaranteed number of instances that are likely to be corrupted. We theoretically analyze how the quality of features affects the local voting and provide guidelines for tuning neighborhood size. We also prove the worst-case error bound for the ranking-based method. Experiments with both synthetic and real-world label noise demonstrate our training-free solutions consistently and significantly improve most of the training-based baselines.