Constructing 3D structures from serial section data is a long standing problem in microscopy. The structure of a fiber reinforced composite material can be reconstructed using a tracking-by-detection model. Tracking-by-detection algorithms rely heavily on detection accuracy, especially the recall performance. The state-of-the-art fiber detection algorithms perform well under ideal conditions, but are not accurate where there are local degradations of image quality, due to contaminants on the material surface and or defocus blur. Convolutional Neural Networks (CNN) could be used for this problem, but would require a large number of manual annotated fibers, which are not available. We propose an unsupervised learning method to accurately detect fibers on the large scale, that is robust against local degradations of image quality. The proposed method does not require manual annotations, but uses fiber shape size priors and spatio-temporal consistency in tracking to simulate the supervision in the training of the CNN. Experiments show significant improvements over state-of-the-art fiber detection algorithms together with advanced tracking performance.
Spatio-temporal consistency, as an important property in video processing, has many vision applications such as video object proposals @cite_34 , object instance search in videos @cite_11 , human segmentation @cite_28 , etc. Assuming tracked patches have similar visual representation in deep feature space, unsupervised learning of visual representations is accomplished @cite_17 . As described in @cite_25 , tracking and detection can be jointly carried out in a supervised CNN based framework with manual annotations. Inspired by these researches, we expect that fiber tracking and fiber detection could work collaboratively to achieve better performance in an unsupervised manner. Without any manual annotations, we combine fiber shape size priors and spatio-temporal consistency in tracking to simulate the human-like supervision in training a CNN based object detector, providing effective fiber detection and tracking simultaneously.
@cite_11 @cite_28 @cite_34 @cite_25 @cite_17
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Given a specific object as query, object instance search aims to not only retrieve the images or frames that contain the query, but also locate all its occurrences. In this work, we explore the use of spatio-temporal cues to improve the quality of object instance search from videos. To this end, we formulate this problem as the spatio-temporal trajectory search problem, where a trajectory is a sequence of bounding boxes that locate the object instance in each frame. The goal is to find the top- @math trajectories that are likely to contain the target object. Despite the large number of trajectory candidates, we build on a recent spatio- temporal search algorithm for event detection to efficiently find the optimal spatio- temporal trajectories in large video volumes , with complexity linear to the video volume size. We solve the key bottleneck in applying this approach to object instance search by leveraging a randomized approach to enable fast scoring of any bounding boxes in the video volume. In addition , we present a new dataset for video object instance search. Experimental results on a 73-hour video dataset demonstrate that our approach improves the performance of video object instance search and localization over the state-of-the-art search and tracking methods. An intuition on human segmentation is that when a human is moving in a video, the video-context (e.g., appearance and motion clues) may potentially infer reasonable mask information for the whole human body. Inspired by this, based on popular deep convolutional neural networks (CNN), we explore a very-weakly supervised learning framework for human segmentation task, where only an imperfect human detector is available along with massive weakly-labeled YouTube videos. In our solution, the video-context guided human mask inference and CNN based segmentation network learning iterate to mutually enhance each other until no further improvement gains. In the first step, each video is decomposed into supervoxels by the unsupervised video segmentation. The superpixels within the supervoxels are then classified as human or non-human by graph optimization with unary energies from the imperfect human detection results and the predicted confidence maps by the CNN trained in the previous iteration. In the second step, the video-context derived human masks are used as direct labels to train CNN. Extensive experiments on the challenging PASCAL VOC 2012 semantic segmentation benchmark demonstrate that the proposed framework has already achieved superior results than all previous weakly-supervised methods with object class or bounding box annotations. In addition, by augmenting with the annotated masks from PASCAL VOC 2012, our method reaches a new state-of-the-art performance on the human segmentation task. Recent approaches for high accuracy detection and tracking of object categories in video consist of complex multistage solutions that become more cumbersome each year. In this paper we propose a ConvNet architecture that jointly performs detection and tracking, solving the task in a simple and effective way. Our contributions are threefold: (i) we set up a ConvNet architecture for simultaneous detection and tracking, using a multi-task objective for frame-based object detection and across-frame track regression; (ii) we introduce correlation features that represent object co-occurrences across time to aid the ConvNet during tracking; and (iii) we link the frame level detections based on our across-frame tracklets to produce high accuracy detections at the video level. Our ConvNet architecture for spatiotemporal object detection is evaluated on the large-scale ImageNet VID dataset where it achieves state-of-the-art results. Our approach provides better single model performance than the winning method of the last ImageNet challenge while being conceptually much simpler. Finally, we show that by increasing the temporal stride we can dramatically increase the tracker speed. Is strong supervision necessary for learning a good visual representation? Do we really need millions of semantically-labeled images to train a Convolutional Neural Network (CNN)? In this paper, we present a simple yet surprisingly powerful approach for unsupervised learning of CNN. Specifically, we use hundreds of thousands of unlabeled videos from the web to learn visual representations. Our key idea is that visual tracking provides the supervision. That is, two patches connected by a track should have similar visual representation in deep feature space since they probably belong to same object or object part. We design a Siamese-triplet network with a ranking loss function to train this CNN representation. Without using a single image from ImageNet, just using 100K unlabeled videos and the VOC 2012 dataset, we train an ensemble of unsupervised networks that achieves 52 mAP (no bounding box regression). This performance comes tantalizingly close to its ImageNet-supervised counterpart, an ensemble which achieves a mAP of 54.4 . We also show that our unsupervised network can perform competitively in other tasks such as surface-normal estimation.