50,149 research outputs found
Instance-Level Salient Object Segmentation
Image saliency detection has recently witnessed rapid progress due to deep
convolutional neural networks. However, none of the existing methods is able to
identify object instances in the detected salient regions. In this paper, we
present a salient instance segmentation method that produces a saliency mask
with distinct object instance labels for an input image. Our method consists of
three steps, estimating saliency map, detecting salient object contours and
identifying salient object instances. For the first two steps, we propose a
multiscale saliency refinement network, which generates high-quality salient
region masks and salient object contours. Once integrated with multiscale
combinatorial grouping and a MAP-based subset optimization framework, our
method can generate very promising salient object instance segmentation
results. To promote further research and evaluation of salient instance
segmentation, we also construct a new database of 1000 images and their
pixelwise salient instance annotations. Experimental results demonstrate that
our proposed method is capable of achieving state-of-the-art performance on all
public benchmarks for salient region detection as well as on our new dataset
for salient instance segmentation.Comment: To appear in CVPR201
LST-Bench: Benchmarking Log-Structured Tables in the Cloud
Log-Structured Tables (LSTs), also commonly referred to as table formats,
have recently emerged to bring consistency and isolation to object stores. With
the separation of compute and storage, object stores have become the go-to for
highly scalable and durable storage. However, this comes with its own set of
challenges, such as the lack of recovery and concurrency management that
traditional database management systems provide. This is where LSTs such as
Delta Lake, Apache Iceberg, and Apache Hudi come into play, providing an
automatic metadata layer that manages tables defined over object stores,
effectively addressing these challenges. A paradigm shift in the design of
these systems necessitates the updating of evaluation methodologies. In this
paper, we examine the characteristics of LSTs and propose extensions to
existing benchmarks, including workload patterns and metrics, to accurately
capture their performance. We introduce our framework, LST-Bench, which enables
users to execute benchmarks tailored for the evaluation of LSTs. Our evaluation
demonstrates how these benchmarks can be utilized to evaluate the performance,
efficiency, and stability of LSTs. The code for LST-Bench is open sourced and
is available at https://github.com/microsoft/lst-bench/
Making an Embedded DBMS JIT-friendly
While database management systems (DBMSs) are highly optimized, interactions
across the boundary between the programming language (PL) and the DBMS are
costly, even for in-process embedded DBMSs. In this paper, we show that
programs that interact with the popular embedded DBMS SQLite can be
significantly optimized - by a factor of 3.4 in our benchmarks - by inlining
across the PL / DBMS boundary. We achieved this speed-up by replacing parts of
SQLite's C interpreter with RPython code and composing the resulting
meta-tracing virtual machine (VM) - called SQPyte - with the PyPy VM. SQPyte
does not compromise stand-alone SQL performance and is 2.2% faster than SQLite
on the widely used TPC-H benchmark suite.Comment: 24 pages, 18 figure
Approaches to Interpreter Composition
In this paper, we compose six different Python and Prolog VMs into 4 pairwise
compositions: one using C interpreters; one running on the JVM; one using
meta-tracing interpreters; and one using a C interpreter and a meta-tracing
interpreter. We show that programs that cross the language barrier frequently
execute faster in a meta-tracing composition, and that meta-tracing imposes a
significantly lower overhead on composed programs relative to mono-language
programs.Comment: 33 pages, 1 figure, 9 table
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