6 research outputs found
Scalable Auction Algorithms for Bipartite Maximum Matching Problems
In this paper, we give new auction algorithms for maximum weighted bipartite
matching (MWM) and maximum cardinality bipartite -matching (MCbM). Our
algorithms run in and rounds, respectively, in the blackboard distributed
setting. We show that our MWM algorithm can be implemented in the distributed,
interactive setting using and bit messages,
respectively, directly answering the open question posed by Demange, Gale and
Sotomayor [DNO14]. Furthermore, we implement our algorithms in a variety of
other models including the the semi-streaming model, the shared-memory
work-depth model, and the massively parallel computation model. Our
semi-streaming MWM algorithm uses passes in space and our MCbM algorithm runs in
passes using space (where parameters represent
the degree constraints on the -matching and and represent the left
and right side of the bipartite graph, respectively). Both of these algorithms
improves \emph{exponentially} the dependence on in the space
complexity in the semi-streaming model against the best-known algorithms for
these problems, in addition to improvements in round complexity for MCbM.
Finally, our algorithms eliminate the large polylogarithmic dependence on
in depth and number of rounds in the work-depth and massively parallel
computation models, respectively, improving on previous results which have
large polylogarithmic dependence on (and exponential dependence on
in the MPC model).Comment: To appear in APPROX 202
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
For years, the YOLO series has been the de facto industry-level standard for
efficient object detection. The YOLO community has prospered overwhelmingly to
enrich its use in a multitude of hardware platforms and abundant scenarios. In
this technical report, we strive to push its limits to the next level, stepping
forward with an unwavering mindset for industry application.
Considering the diverse requirements for speed and accuracy in the real
environment, we extensively examine the up-to-date object detection
advancements either from industry or academia. Specifically, we heavily
assimilate ideas from recent network design, training strategies, testing
techniques, quantization, and optimization methods. On top of this, we
integrate our thoughts and practice to build a suite of deployment-ready
networks at various scales to accommodate diversified use cases. With the
generous permission of YOLO authors, we name it YOLOv6. We also express our
warm welcome to users and contributors for further enhancement. For a glimpse
of performance, our YOLOv6-N hits 35.9% AP on the COCO dataset at a throughput
of 1234 FPS on an NVIDIA Tesla T4 GPU. YOLOv6-S strikes 43.5% AP at 495 FPS,
outperforming other mainstream detectors at the same scale~(YOLOv5-S, YOLOX-S,
and PPYOLOE-S). Our quantized version of YOLOv6-S even brings a new
state-of-the-art 43.3% AP at 869 FPS. Furthermore, YOLOv6-M/L also achieves
better accuracy performance (i.e., 49.5%/52.3%) than other detectors with a
similar inference speed. We carefully conducted experiments to validate the
effectiveness of each component. Our code is made available at
https://github.com/meituan/YOLOv6.Comment: technical repor