148 research outputs found
Instantiating deformable models with a neural net
Deformable models are an attractive approach to recognizing objects which have considerable within-class variability such as handwritten characters. However, there are severe search problems associated with fitting the models to data which could be reduced if a better starting point for the search were available. We show that by training a neural network to predict how a deformable model should be instantiated from an input image, such improved starting points can be obtained. This method has been implemented for a system that recognizes handwritten digits using deformable models, and the results show that the search time can be significantly reduced without compromising recognition performance. © 1997 Academic Press
Query-guided End-to-End Person Search
Person search has recently gained attention as the novel task of finding a
person, provided as a cropped sample, from a gallery of non-cropped images,
whereby several other people are also visible. We believe that i. person
detection and re-identification should be pursued in a joint optimization
framework and that ii. the person search should leverage the query image
extensively (e.g. emphasizing unique query patterns). However, so far, no prior
art realizes this. We introduce a novel query-guided end-to-end person search
network (QEEPS) to address both aspects. We leverage a most recent joint
detector and re-identification work, OIM [37]. We extend this with i. a
query-guided Siamese squeeze-and-excitation network (QSSE-Net) that uses global
context from both the query and gallery images, ii. a query-guided region
proposal network (QRPN) to produce query-relevant proposals, and iii. a
query-guided similarity subnetwork (QSimNet), to learn a query-guided
reidentification score. QEEPS is the first end-to-end query-guided detection
and re-id network. On both the most recent CUHK-SYSU [37] and PRW [46]
datasets, we outperform the previous state-of-the-art by a large margin.Comment: Accepted as poster in CVPR 201
Institutional paraconsciousness and its pathologies
This analysis extends a recent mathematical treatment of the Baars consciousness model to analogous, but far more complicated, phenomena of institutional cognition. Individual consciousness is limited to a single, tunable, giant component of interacting cognitive modules, instantiating a Global Workspace. Human institutions, by contrast, support several, sometimes many, such giant components simultaneously, although their behavior remains constrained to a topology generated by cultural context and by the path-dependence inherent to organizational history. Such highly parallel multitasking - institutional paraconsciousness - while clearly limiting inattentional blindness and the consequences of failures within individual workspaces, does not eliminate them, and introduces new characteristic dysfunctions involving the distortion of information sent between global workspaces. Consequently, organizations (or machines designed along these principles), while highly efficient at certain kinds of tasks, remain subject to canonical and idiosyncratic failure patterns similar to, but more complicated than, those afflicting individuals. Remediation is complicated by the manner in which pathogenic externalities can write images of themselves on both institutional function and therapeutic intervention, in the context of relentless market selection pressures. The approach is broadly consonant with recent work on collective efficacy, collective consciousness, and distributed cognition
Real-Time Siamese Multiple Object Tracker with Enhanced Proposals
Maintaining the identity of multiple objects in real-time video is a
challenging task, as it is not always feasible to run a detector on every
frame. Thus, motion estimation systems are often employed, which either do not
scale well with the number of targets or produce features with limited semantic
information. To solve the aforementioned problems and allow the tracking of
dozens of arbitrary objects in real-time, we propose SiamMOTION. SiamMOTION
includes a novel proposal engine that produces quality features through an
attention mechanism and a region-of-interest extractor fed by an inertia module
and powered by a feature pyramid network. Finally, the extracted tensors enter
a comparison head that efficiently matches pairs of exemplars and search areas,
generating quality predictions via a pairwise depthwise region proposal network
and a multi-object penalization module. SiamMOTION has been validated on five
public benchmarks, achieving leading performance against current
state-of-the-art trackers. Code available at:
https://github.com/lorenzovaquero/SiamMOTIONComment: Accepted at Pattern Recognition. Code available at
https://github.com/lorenzovaquero/SiamMOTIO
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