2 research outputs found
RandMSAugment: A Mixed-Sample Augmentation for Limited-Data Scenarios
The high costs of annotating large datasets suggests a need for effectively
training CNNs with limited data, and data augmentation is a promising
direction. We study foundational augmentation techniques, including Mixed
Sample Data Augmentations (MSDAs) and a no-parameter variant of RandAugment
termed Preset-RandAugment, in the fully supervised scenario. We observe that
Preset-RandAugment excels in limited-data contexts while MSDAs are moderately
effective. We show that low-level feature transforms play a pivotal role in
this performance difference, postulate a new property of augmentations related
to their data efficiency, and propose new ways to measure the diversity and
realism of augmentations. Building on these insights, we introduce a novel
augmentation technique called RandMSAugment that integrates complementary
strengths of existing methods. RandMSAugment significantly outperforms the
competition on CIFAR-100, STL-10, and Tiny-Imagenet. With very small training
sets (4, 25, 100 samples/class), RandMSAugment achieves compelling performance
gains between 4.1% and 6.75%. Even with more training data (500 samples/class)
we improve performance by 1.03% to 2.47%. RandMSAugment does not require
hyperparameter tuning, extra validation data, or cumbersome optimizations
CoMaL Tracking: Tracking Points at the Object Boundaries
Traditional point tracking algorithms such as the KLT use local 2D
information aggregation for feature detection and tracking, due to which their
performance degrades at the object boundaries that separate multiple objects.
Recently, CoMaL Features have been proposed that handle such a case. However,
they proposed a simple tracking framework where the points are re-detected in
each frame and matched. This is inefficient and may also lose many points that
are not re-detected in the next frame. We propose a novel tracking algorithm to
accurately and efficiently track CoMaL points. For this, the level line segment
associated with the CoMaL points is matched to MSER segments in the next frame
using shape-based matching and the matches are further filtered using
texture-based matching. Experiments show improvements over a simple
re-detect-and-match framework as well as KLT in terms of speed/accuracy on
different real-world applications, especially at the object boundaries.Comment: 10 pages, 10 figures, to appear in 1st Joint BMTT-PETS Workshop on
Tracking and Surveillance, CVPR 201