107 research outputs found
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
How do we learn an object detector that is invariant to occlusions and
deformations? Our current solution is to use a data-driven strategy -- collect
large-scale datasets which have object instances under different conditions.
The hope is that the final classifier can use these examples to learn
invariances. But is it really possible to see all the occlusions in a dataset?
We argue that like categories, occlusions and object deformations also follow a
long-tail. Some occlusions and deformations are so rare that they hardly
happen; yet we want to learn a model invariant to such occurrences. In this
paper, we propose an alternative solution. We propose to learn an adversarial
network that generates examples with occlusions and deformations. The goal of
the adversary is to generate examples that are difficult for the object
detector to classify. In our framework both the original detector and adversary
are learned in a joint manner. Our experimental results indicate a 2.3% mAP
boost on VOC07 and a 2.6% mAP boost on VOC2012 object detection challenge
compared to the Fast-RCNN pipeline. We also release the code for this paper.Comment: CVPR 2017 Camera Read
Cross-stitch Networks for Multi-task Learning
Multi-task learning in Convolutional Networks has displayed remarkable
success in the field of recognition. This success can be largely attributed to
learning shared representations from multiple supervisory tasks. However,
existing multi-task approaches rely on enumerating multiple network
architectures specific to the tasks at hand, that do not generalize. In this
paper, we propose a principled approach to learn shared representations in
ConvNets using multi-task learning. Specifically, we propose a new sharing
unit: "cross-stitch" unit. These units combine the activations from multiple
networks and can be trained end-to-end. A network with cross-stitch units can
learn an optimal combination of shared and task-specific representations. Our
proposed method generalizes across multiple tasks and shows dramatically
improved performance over baseline methods for categories with few training
examples.Comment: To appear in CVPR 2016 (Spotlight
Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos
We present a semi-supervised approach that localizes multiple unknown object
instances in long videos. We start with a handful of labeled boxes and
iteratively learn and label hundreds of thousands of object instances. We
propose criteria for reliable object detection and tracking for constraining
the semi-supervised learning process and minimizing semantic drift. Our
approach does not assume exhaustive labeling of each object instance in any
single frame, or any explicit annotation of negative data. Working in such a
generic setting allow us to tackle multiple object instances in video, many of
which are static. In contrast, existing approaches either do not consider
multiple object instances per video, or rely heavily on the motion of the
objects present. The experiments demonstrate the effectiveness of our approach
by evaluating the automatically labeled data on a variety of metrics like
quality, coverage (recall), diversity, and relevance to training an object
detector.Comment: To appear in CVPR 201
Detecting Human-Object Interactions via Functional Generalization
We present an approach for detecting human-object interactions (HOIs) in
images, based on the idea that humans interact with functionally similar
objects in a similar manner. The proposed model is simple and efficiently uses
the data, visual features of the human, relative spatial orientation of the
human and the object, and the knowledge that functionally similar objects take
part in similar interactions with humans. We provide extensive experimental
validation for our approach and demonstrate state-of-the-art results for HOI
detection. On the HICO-Det dataset our method achieves a gain of over 2.5%
absolute points in mean average precision (mAP) over state-of-the-art. We also
show that our approach leads to significant performance gains for zero-shot HOI
detection in the seen object setting. We further demonstrate that using a
generic object detector, our model can generalize to interactions involving
previously unseen objects.Comment: AAAI 202
Management of hepatic hydatidosis by open versus laparoscopic surgery
Background:Aim of current study was to compare between laparoscopic versus open management of the hydatid cyst of liver regarding complication rate, post-operative recovery course with different modality of treatment and hospital stay. This study shows our results of surgical treatment of liver hydatid cysts during a 2.5 years period.Methods:A prospective study of 30 patients operated on in a 2.5 year period (April 2011 to October 2013) in department of general surgery of J.N. medical college, Sawangi (Meghe), Wardha, Maharashtra, with hepatic hydatid cyst. All patients were preoperatively treated with albendazole. 15 patients were tackled by laparoscopic technique (using Palanivelu hydatid system) and rest 15 underwent Open procedure as surgical approach.Results:Patients operated by laparoscopic surgery shown a better post-operative recovery course, required less analgesia, mobilized and started on oral feed early, intra-abdominal drain was removed at a much earlier period as compared to open group patient, this not only reduced morbidity but also because of this patient could be discharged earlier.Conclusion:Minimal invasive management, using Palanivelu hydatid system for aspiration and laparoscopic intervention, is an alternative to open surgery because of its ability to prevent spillage and thus minimize recurrences. It is better and safe to use laparoscopy in treatment of hydatid liver with less morbidity, mortality and recurrence rate in comparison with open technique.
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