Transfer Learning for Cross-Dataset Recognition: A Survey <s> Self Labelling. <s> Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund & Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model. <s> BIB001 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Self Labelling. <s> In recent years, many research works have been carried out to recognize human actions from video clips. To learn an effective action classifier, most of the previous approaches rely on enough training labels. When being required to recognize the action in a different dataset, these approaches have to re-train the model using new labels. However, labeling video sequences is a very tedious and time-consuming task, especially when detailed spatial locations and time durations are required. In this paper, we propose an adaptive action detection approach which reduces the requirement of training labels and is able to handle the task of cross-dataset action detection with few or no extra training labels. Our approach combines model adaptation and action detection into a Maximum a Posterior (MAP) estimation framework, which explores the spatial-temporal coherence of actions and makes good use of the prior information which can be obtained without supervision. Our approach obtains state-of-the-art results on KTH action dataset using only 50% of the training labels in tradition approaches. Furthermore, we show that our approach is effective for the cross-dataset detection which adapts the model trained on KTH to two other challenging datasets1. <s> BIB002 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Self Labelling. <s> Many classifiers are trained with massive training sets only to be applied at test time on data from a different distribution. How can we rapidly and simply adapt a classifier to a new test distribution, even when we do not have access to the original training data? We present an on-line approach for rapidly adapting a “black box” classifier to a new test data set without retraining the classifier or examining the original optimization criterion. Assuming the original classifier outputs a continuous number for which a threshold gives the class, we reclassify points near the original boundary using a Gaussian process regression scheme. We show how this general procedure can be used in the context of a classifier cascade, demonstrating performance that far exceeds state-of-the-art results in face detection on a standard data set. We also draw connections to work in semi-supervised learning, domain adaptation, and information regularization. <s> BIB003 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Self Labelling. <s> Abstract : Typical object detectors trained on images perform poorly on video, as there is a clear distinction in domain between the two types of data. In this paper, we tackle the problem of adapting object detectors learned from images to work well on videos. We treat the problem as one of unsupervised domain adaptation, in which we are given labeled data from the source domain (image), but only unlabeled data from the target domain (video). Our approach, self-paced domain adaptation, seeks to iteratively adapt the detector by re-training the detector with automatically discovered target domain examples, starting with the easiest first. At each iteration, the algorithm adapts by considering an increased number of target domain examples, and a decreased number of source domain examples. To discover target domain examples from the vast amount of video data, we introduce a simple, robust approach that scores trajectory tracks instead of bounding boxes. We also show how rich and expressive features specific to the target domain can be incorporated under the same framework. We show promising results on the 2011 TRECVID Multimedia Event Detection and LabelMe Video datasets that illustrate the benefit of our approach to adapt object detectors to video. <s> BIB004 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Self Labelling. <s> We propose an online domain adaptation method for the deformable part-based model (DPM). The online domain adaptation is based on a two-level hierarchical adaptation tree, which consists of instance models in the leaf nodes and a category model at the root node. Moreover, combined with a multiple object tracking procedure (MOT), our proposal neither requires target-domain annotated data nor revisiting the source-domain data for performing the source-to-target domain adaptation of the DPM. From a practical point of view this means that, given a source-domain DPM and new video for training on a new domain without object annotations, our procedure outputs a new DPM adapted to the domain represented by the video. As proof-of-concept we apply our proposal to the challenging task of pedestrian detection. In this case, each instance model is an exemplar classifier trained online with only one pedestrian per frame. The pedestrian instances are collected by MOT and the hierarchical model is constructed dynamically according to the pedestrian trajectories. Our experimental results show that the adapted model achieves the accuracy of recent supervised domain adaptation methods (i.e., requiring manually annotated target-domain data), and improves the source model more than 10 percentage points. <s> BIB005
Hybrid Knowledge Transfer. The instance weights and classifier parameters are jointly exploited by BIB001 ]. They propose a transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms by adding a mechanism to decrease the weights of the instances that are the most dissimilar to the target distribution in order to weaken their impacts. The target labelled samples are used to help vote on the usefulness of each of the source domain data instance. Classifier-based Transfer. BIB003 address the online adaptation in the face detection task by adapting pre-trained classifiers using a Gaussian process regression scheme. The intuition is that the "easy-to-detect" faces can help the detection of "hard-to-detect" faces by normalizing the co-occurring "hard-to-detect" faces and thus reducing their difficulty of detection. Differently, BIB002 address the cross-dataset action detection problem by proposing a Maximum a Posterior (MAP) estimation framework, which explores the spatial-temporal coherence of actions and makes use of the prior information. BIB005 propose an online domain adaptation model for multiple object tracking based on a two-level hierarchical adaptation tree, which consists of instance detectors in the leaf nodes and a category detector at the root node. The adaptation is executed in a progressive manner. Instance-based Transfer. BIB004 propose a self-paced method for adapting object detectors from image to video, hence the data modalities between domains are completely different. They iteratively adapt the detector by automatically discovering examples from target video data, starting from the most confident ones. In each iteration, the number of target examples is increased while the source examples are decreased.