93,085 research outputs found
Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN
The dominant object detection approaches treat each dataset separately and
fit towards a specific domain, which cannot adapt to other domains without
extensive retraining. In this paper, we address the problem of designing a
universal object detection model that exploits diverse category granularity
from multiple domains and predict all kinds of categories in one system.
Existing works treat this problem by integrating multiple detection branches
upon one shared backbone network. However, this paradigm overlooks the crucial
semantic correlations between multiple domains, such as categories hierarchy,
visual similarity, and linguistic relationship. To address these drawbacks, we
present a novel universal object detector called Universal-RCNN that
incorporates graph transfer learning for propagating relevant semantic
information across multiple datasets to reach semantic coherency. Specifically,
we first generate a global semantic pool by integrating all high-level semantic
representation of all the categories. Then an Intra-Domain Reasoning Module
learns and propagates the sparse graph representation within one dataset guided
by a spatial-aware GCN. Finally, an InterDomain Transfer Module is proposed to
exploit diverse transfer dependencies across all domains and enhance the
regional feature representation by attending and transferring semantic contexts
globally. Extensive experiments demonstrate that the proposed method
significantly outperforms multiple-branch models and achieves the
state-of-the-art results on multiple object detection benchmarks (mAP: 49.1% on
COCO).Comment: Accepted by AAAI2
Revisiting knowledge transfer for training object class detectors
We propose to revisit knowledge transfer for training object detectors on
target classes from weakly supervised training images, helped by a set of
source classes with bounding-box annotations. We present a unified knowledge
transfer framework based on training a single neural network multi-class object
detector over all source classes, organized in a semantic hierarchy. This
generates proposals with scores at multiple levels in the hierarchy, which we
use to explore knowledge transfer over a broad range of generality, ranging
from class-specific (bicycle to motorbike) to class-generic (objectness to any
class). Experiments on the 200 object classes in the ILSVRC 2013 detection
dataset show that our technique: (1) leads to much better performance on the
target classes (70.3% CorLoc, 36.9% mAP) than a weakly supervised baseline
which uses manually engineered objectness [11] (50.5% CorLoc, 25.4% mAP). (2)
delivers target object detectors reaching 80% of the mAP of their fully
supervised counterparts. (3) outperforms the best reported transfer learning
results on this dataset (+41% CorLoc and +3% mAP over [18, 46], +16.2% mAP over
[32]). Moreover, we also carry out several across-dataset knowledge transfer
experiments [27, 24, 35] and find that (4) our technique outperforms the weakly
supervised baseline in all dataset pairs by 1.5x-1.9x, establishing its general
applicability.Comment: CVPR 1
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
We introduce ScanComplete, a novel data-driven approach for taking an
incomplete 3D scan of a scene as input and predicting a complete 3D model along
with per-voxel semantic labels. The key contribution of our method is its
ability to handle large scenes with varying spatial extent, managing the cubic
growth in data size as scene size increases. To this end, we devise a
fully-convolutional generative 3D CNN model whose filter kernels are invariant
to the overall scene size. The model can be trained on scene subvolumes but
deployed on arbitrarily large scenes at test time. In addition, we propose a
coarse-to-fine inference strategy in order to produce high-resolution output
while also leveraging large input context sizes. In an extensive series of
experiments, we carefully evaluate different model design choices, considering
both deterministic and probabilistic models for completion and semantic
inference. Our results show that we outperform other methods not only in the
size of the environments handled and processing efficiency, but also with
regard to completion quality and semantic segmentation performance by a
significant margin.Comment: Video: https://youtu.be/5s5s8iH0NF
Processing Metonymy: a Domain-Model Heuristic Graph Traversal Approach
We address here the treatment of metonymic expressions from a knowledge
representation perspective, that is, in the context of a text understanding
system which aims to build a conceptual representation from texts according to
a domain model expressed in a knowledge representation formalism.
We focus in this paper on the part of the semantic analyser which deals with
semantic composition. We explain how we use the domain model to handle metonymy
dynamically, and more generally, to underlie semantic composition, using the
knowledge descriptions attached to each concept of our ontology as a kind of
concept-level, multiple-role qualia structure.
We rely for this on a heuristic path search algorithm that exploits the
graphic aspects of the conceptual graphs formalism. The methods described have
been implemented and applied on French texts in the medical domain.Comment: 6 pages, LaTeX, one encapsulated PostScript figure, uses colap.sty
(included) and epsf.sty (available from the cmp-lg macro library). To appear
in Coling-9
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