4 research outputs found
Enhancing Contrastive Learning with Efficient Combinatorial Positive Pairing
In the past few years, contrastive learning has played a central role for the
success of visual unsupervised representation learning. Around the same time,
high-performance non-contrastive learning methods have been developed as well.
While most of the works utilize only two views, we carefully review the
existing multi-view methods and propose a general multi-view strategy that can
improve learning speed and performance of any contrastive or non-contrastive
method. We first analyze CMC's full-graph paradigm and empirically show that
the learning speed of -views can be increased by times
for small learning rate and early training. Then, we upgrade CMC's full-graph
by mixing views created by a crop-only augmentation, adopting small-size views
as in SwAV multi-crop, and modifying the negative sampling. The resulting
multi-view strategy is called ECPP (Efficient Combinatorial Positive Pairing).
We investigate the effectiveness of ECPP by applying it to SimCLR and assessing
the linear evaluation performance for CIFAR-10 and ImageNet-100. For each
benchmark, we achieve a state-of-the-art performance. In case of ImageNet-100,
ECPP boosted SimCLR outperforms supervised learning
DR.CPO: Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion
In autonomous driving, data augmentation is commonly used for improving 3D
object detection. The most basic methods include insertion of copied objects
and rotation and scaling of the entire training frame. Numerous variants have
been developed as well. The existing methods, however, are considerably limited
when compared to the variety of the real world possibilities. In this work, we
develop a diversified and realistic augmentation method that can flexibly
construct a whole-body object, freely locate and rotate the object, and apply
self-occlusion and external-occlusion accordingly. To improve the diversity of
the whole-body object construction, we develop an iterative method that
stochastically combines multiple objects observed from the real world into a
single object. Unlike the existing augmentation methods, the constructed
objects can be randomly located and rotated in the training frame because
proper occlusions can be reflected to the whole-body objects in the final step.
Finally, proper self-occlusion at each local object level and
external-occlusion at the global frame level are applied using the Hidden Point
Removal (HPR) algorithm that is computationally efficient. HPR is also used for
adaptively controlling the point density of each object according to the
object's distance from the LiDAR. Experiment results show that the proposed
DR.CPO algorithm is data-efficient and model-agnostic without incurring any
computational overhead. Also, DR.CPO can improve mAP performance by 2.08% when
compared to the best 3D detection result known for KITTI dataset. The code is
available at https://github.com/SNU-DRL/DRCPO.gi
Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion
In autonomous driving, data augmentation is commonly used for improving 3D object detection. The most basic methods include insertion of copied objects and rotation and scaling of the entire training frame. Numerous variants have been developed as well. The existing methods, however, are considerably limited when compared to the variety of the real world possibilities. In this work, we develop a diversified and realistic augmentation method that can flexibly construct a whole-body object, freely locate and rotate the object, and apply self-occlusion and external-occlusion accordingly. To improve the diversity of the whole-body object construction, we develop an iterative method that stochastically combines multiple objects observed from the real world into a single object. Unlike the existing augmentation methods, the constructed objects can be randomly located and rotated in the training frame because proper occlusions can be reflected to the whole-body objects in the final step. Finally, proper self-occlusion at each local object level and external-occlusion at the global frame level are applied using the Hidden Point Removal (HPR) algorithm that is computationally efficient. HPR is also used for adaptively controlling the point density of each object according to the object's distance from the LiDAR. Experiment results show that the proposed DR.CPO algorithm is data-efficient and model-agnostic without incurring any computational overhead. Also, DR.CPO can improve mAP performance by 2.08% when compared to the best 3D detection result known for KITTI dataset
Table_1_Patterns of summer ichthyoplankton distribution, including invasive species, in the Bering and Chukchi Seas.docx
A multidisciplinary survey was carried out in the Pacific Arctic and sub-Arctic regions of the North Pacific Ocean on the Korean icebreaking research vessel Araon. During this survey, ichthyoplankton fishes in the Pacific Arctic and sub-Arctic region ranged from the Bering Sea to the northern Chukchi Shelf in summer. The most dominant species was Gadus chalcogrammus, followed by Pleuronectes quadrituberculatus and Boreogadus saida. Gadus chalcogrammus and P. quadrituberculatus were particularly abundant near the Bering Sea and Bering Strait, whereas B. saida was dominant in the Chukchi Sea. Hierarchical cluster analysis revealed four distinct ichthyoplankton communities in Pacific Arctic and sub-Arctic regions based on geographical regions. However, Eleginus gracilis, which was previously known to be seen between latitudes 66.5°N and 69.5°N, was found above 70°N, suggesting that its distribution extends further north. Furthermore, we noticed that Benthosema glaciale, which is usually found in the Atlantic sector of Arctic Ocean, was observed in the northern Chukchi Sea. In addition to these unusual species distributions, several species that are mainly observed in coastal areas are observed in the Chukchi Sea region. The observed influx of various uncommon fish species into the Chukchi Sea can be attributed to multiple factors, including freshwater inflow from the East Siberian Sea and the intrusion of warm Atlantic and Pacific waters, which are strongly affected by global warming. Consequently, it is imperative to conduct rigorous monitoring of the Pacific Arctic region, with a particular focus on the Chukchi Sea, to better understand the implications of global warming.</p