2,188 research outputs found
How good are detection proposals, really?
Current top performing Pascal VOC object detectors employ detection proposals
to guide the search for objects thereby avoiding exhaustive sliding window
search across images. Despite the popularity of detection proposals, it is
unclear which trade-offs are made when using them during object detection. We
provide an in depth analysis of ten object proposal methods along with four
baselines regarding ground truth annotation recall (on Pascal VOC 2007 and
ImageNet 2013), repeatability, and impact on DPM detector performance. Our
findings show common weaknesses of existing methods, and provide insights to
choose the most adequate method for different settings
Learning non-maximum suppression
Object detectors have hugely profited from moving towards an end-to-end
learning paradigm: proposals, features, and the classifier becoming one neural
network improved results two-fold on general object detection. One
indispensable component is non-maximum suppression (NMS), a post-processing
algorithm responsible for merging all detections that belong to the same
object. The de facto standard NMS algorithm is still fully hand-crafted,
suspiciously simple, and -- being based on greedy clustering with a fixed
distance threshold -- forces a trade-off between recall and precision. We
propose a new network architecture designed to perform NMS, using only boxes
and their score. We report experiments for person detection on PETS and for
general object categories on the COCO dataset. Our approach shows promise
providing improved localization and occlusion handling.Comment: Added "Supplementary material" titl
What makes for effective detection proposals?
Current top performing object detectors employ detection proposals to guide
the search for objects, thereby avoiding exhaustive sliding window search
across images. Despite the popularity and widespread use of detection
proposals, it is unclear which trade-offs are made when using them during
object detection. We provide an in-depth analysis of twelve proposal methods
along with four baselines regarding proposal repeatability, ground truth
annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM,
R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object
detection improving proposal localisation accuracy is as important as improving
recall. We introduce a novel metric, the average recall (AR), which rewards
both high recall and good localisation and correlates surprisingly well with
detection performance. Our findings show common strengths and weaknesses of
existing methods, and provide insights and metrics for selecting and tuning
proposal methods.Comment: TPAMI final version, duplicate proposals removed in experiment
Taking a Deeper Look at Pedestrians
In this paper we study the use of convolutional neural networks (convnets)
for the task of pedestrian detection. Despite their recent diverse successes,
convnets historically underperform compared to other pedestrian detectors. We
deliberately omit explicitly modelling the problem into the network (e.g. parts
or occlusion modelling) and show that we can reach competitive performance
without bells and whistles. In a wide range of experiments we analyse small and
big convnets, their architectural choices, parameters, and the influence of
different training data, including pre-training on surrogate tasks.
We present the best convnet detectors on the Caltech and KITTI dataset. On
Caltech our convnets reach top performance both for the Caltech1x and
Caltech10x training setup. Using additional data at training time our strongest
convnet model is competitive even to detectors that use additional data
(optical flow) at test time
The Computational Power of Benenson Automata
The development of autonomous molecular computers capable of making
independent decisions in vivo regarding local drug administration may
revolutionize medical science. Recently Benenson at el (2004) have envisioned
one form such a ``smart drug'' may take by implementing an in vitro scheme, in
which a long DNA state molecule is cut repeatedly by a restriction enzyme in a
manner dependent upon the presence of particular short DNA ``rule molecules.''
To analyze the potential of their scheme in terms of the kinds of computations
it can perform, we study an abstraction assuming that a certain class of
restriction enzymes is available and reactions occur without error. We also
discuss how our molecular algorithms could perform with known restriction
enzymes. By exhibiting a way to simulate arbitrary circuits, we show that these
``Benenson automata'' are capable of computing arbitrary Boolean functions.
Further, we show that they are able to compute efficiently exactly those
functions computable by log-depth circuits. Computationally, we formalize a new
variant of limited width branching programs with a molecular implementation.Comment: 18 page
Person Recognition in Personal Photo Collections
Recognising persons in everyday photos presents major challenges (occluded
faces, different clothing, locations, etc.) for machine vision. We propose a
convnet based person recognition system on which we provide an in-depth
analysis of informativeness of different body cues, impact of training data,
and the common failure modes of the system. In addition, we discuss the
limitations of existing benchmarks and propose more challenging ones. Our
method is simple and is built on open source and open data, yet it improves the
state of the art results on a large dataset of social media photos (PIPA).Comment: Accepted to ICCV 2015, revise
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