3 research outputs found
Object-Aware Instance Labeling for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD), where a detector is trained with
only image-level annotations, is attracting more and more attention. As a
method to obtain a well-performing detector, the detector and the instance
labels are updated iteratively. In this study, for more efficient iterative
updating, we focus on the instance labeling problem, a problem of which label
should be annotated to each region based on the last localization result.
Instead of simply labeling the top-scoring region and its highly overlapping
regions as positive and others as negative, we propose more effective instance
labeling methods as follows. First, to solve the problem that regions covering
only some parts of the object tend to be labeled as positive, we find regions
covering the whole object focusing on the context classification loss. Second,
considering the situation where the other objects contained in the image can be
labeled as negative, we impose a spatial restriction on regions labeled as
negative. Using these instance labeling methods, we train the detector on the
PASCAL VOC 2007 and 2012 and obtain significantly improved results compared
with other state-of-the-art approaches.Comment: Accepted to ICCV 2019 (oral
Cross-Supervised Object Detection
After learning a new object category from image-level annotations (with no
object bounding boxes), humans are remarkably good at precisely localizing
those objects. However, building good object localizers (i.e., detectors)
currently requires expensive instance-level annotations. While some work has
been done on learning detectors from weakly labeled samples (with only class
labels), these detectors do poorly at localization. In this work, we show how
to build better object detectors from weakly labeled images of new categories
by leveraging knowledge learned from fully labeled base categories. We call
this novel learning paradigm cross-supervised object detection. We propose a
unified framework that combines a detection head trained from instance-level
annotations and a recognition head learned from image-level annotations,
together with a spatial correlation module that bridges the gap between
detection and recognition. These contributions enable us to better detect novel
objects with image-level annotations in complex multi-object scenes such as the
COCO dataset
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection
Based on the framework of multiple instance learning (MIL), tremendous works
have promoted the advances of weakly supervised object detection (WSOD).
However, most MIL-based methods tend to localize instances to their
discriminative parts instead of the whole content. In this paper, we propose a
spatial likelihood voting (SLV) module to converge the proposal localizing
process without any bounding box annotations. Specifically, all region
proposals in a given image play the role of voters every iteration during
training, voting for the likelihood of each category in spatial dimensions.
After dilating alignment on the area with large likelihood values, the voting
results are regularized as bounding boxes, being used for the final
classification and localization. Based on SLV, we further propose an end-to-end
training framework for multi-task learning. The classification and localization
tasks promote each other, which further improves the detection performance.
Extensive experiments on the PASCAL VOC 2007 and 2012 datasets demonstrate the
superior performance of SLV.Comment: Accepted by CVPR202