1 research outputs found
Hierarchical Context Embedding for Region-based Object Detection
State-of-the-art two-stage object detectors apply a classifier to a sparse
set of object proposals, relying on region-wise features extracted by RoIPool
or RoIAlign as inputs. The region-wise features, in spite of aligning well with
the proposal locations, may still lack the crucial context information which is
necessary for filtering out noisy background detections, as well as recognizing
objects possessing no distinctive appearances. To address this issue, we
present a simple but effective Hierarchical Context Embedding (HCE) framework,
which can be applied as a plug-and-play component, to facilitate the
classification ability of a series of region-based detectors by mining
contextual cues. Specifically, to advance the recognition of context-dependent
object categories, we propose an image-level categorical embedding module which
leverages the holistic image-level context to learn object-level concepts.
Then, novel RoI features are generated by exploiting hierarchically embedded
context information beneath both whole images and interested regions, which are
also complementary to conventional RoI features. Moreover, to make full use of
our hierarchical contextual RoI features, we propose the early-and-late fusion
strategies (i.e., feature fusion and confidence fusion), which can be combined
to boost the classification accuracy of region-based detectors. Comprehensive
experiments demonstrate that our HCE framework is flexible and generalizable,
leading to significant and consistent improvements upon various region-based
detectors, including FPN, Cascade R-CNN and Mask R-CNN.Comment: Accepted by ECCV 202