65,502 research outputs found
Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes
Current 3D segmentation methods heavily rely on large-scale point-cloud
datasets, which are notoriously laborious to annotate. Few attempts have been
made to circumvent the need for dense per-point annotations. In this work, we
look at weakly-supervised 3D semantic instance segmentation. The key idea is to
leverage 3D bounding box labels which are easier and faster to annotate.
Indeed, we show that it is possible to train dense segmentation models using
only bounding box labels. At the core of our method, \name{}, lies a deep
model, inspired by classical Hough voting, that directly votes for bounding box
parameters, and a clustering method specifically tailored to bounding box
votes. This goes beyond commonly used center votes, which would not fully
exploit the bounding box annotations. On ScanNet test, our weakly supervised
model attains leading performance among other weakly supervised approaches (+18
mAP@50). Remarkably, it also achieves 97% of the mAP@50 score of current fully
supervised models. To further illustrate the practicality of our work, we train
Box2Mask on the recently released ARKitScenes dataset which is annotated with
3D bounding boxes only, and show, for the first time, compelling 3D instance
segmentation masks.Comment: Project page: https://virtualhumans.mpi-inf.mpg.de/box2mask
Crime Pattern Detection Using Data Mining
Can crimes be modeled as data mining problems? We will try to answer this question in this paper. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. Here we look at use of clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving crime. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. We will apply these techniques to real crime data from a sheriff’s office and validate our results. We also use semi-supervised learning technique here for knowledge discovery from the crime records and to help increase the predictive accuracy. We also developed a weighting scheme for attributes here to deal with limitations of various out of the box clustering tools and techniques. This easy to implement machine learning framework works with the geo-spatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. It can also be applied for counter terrorism for homeland security
Multi-learner based recursive supervised training
In this paper, we propose the Multi-Learner Based Recursive Supervised Training (MLRT) algorithm which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of the dataset, and utilize it to train the data accurately and efficiently. We observed that empirically, MLRT performs considerably well as compared to RPHP and other systems on benchmark data with 11% improvement in accuracy on the SPAM dataset and comparable performances on the VOWEL and the TWO-SPIRAL problems. In addition, for most datasets, the time taken by MLRT is considerably lower than the other systems with comparable accuracy. Two heuristic versions, MLRT-2 and MLRT-3 are also introduced to improve the efficiency in the system, and to make it more scalable for future updates. The performance in these versions is similar to the original MLRT system
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
- …