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This paper considers the active recognition scenario, where the agent is empowered to intelligently acquire observations for better recognition. The agents usually compose two modules, i.e., the policy and the recognizer, to select actions and predict the category. While using ground-truth class labels to supervise the recognizer, the policy is typically updated with rewards determined by the current in-training recognizer, like whether achieving correct predictions. However, this joint learning process could lead to unintended solutions, like a collapsed policy that only visits views that the recognizer is already sufficiently trained to obtain rewards, which harms the generalization ability. We call this phenomenon lingering to depict the agent being reluctant to explore challenging views during training. Existing approaches to tackle the exploration-exploitation trade-off could be ineffective as they usually assume reliable feedback during exploration to update the estimate of rarely-visited states. This assumption is invalid here as the reward from the recognizer could be insufficiently trained.To this end, our approach integrates another adversarial policy to constantly disturb the recognition agent during training, forming a competing game to promote active explorations and avoid lingering. The reinforced adversary, rewarded when the recognition fails, contests the recognition agent by turning the camera to challenging observations. Extensive experiments across two datasets validate the effectiveness of the proposed approach regarding its recognition performances, learning efficiencies, and especially robustness in managing environmental noises. 

Weakly-supervised Temporal Action Localization (WTAL) aims to classify and localize action instances in untrimmed videos with only video-level labels. Existing methods typically use snippet-level RGB and optical flow features extracted from pre-trained extractors directly. Because of two limitations: the short temporal span of snippets and the inappropriate initial features, these WTAL methods suffer from the lack of effective use of temporal information and have limited performance. In this paper, we propose the Temporal Feature Enhancement Dilated Convolution Network (TFE-DCN) to address these two limitations. The proposed TFE-DCN has an enlarged receptive field that covers a long temporal span to observe the full dynamics of action instances, which makes it powerful to capture temporal dependencies between snippets. Furthermore, we propose the Modality Enhancement Module that can enhance RGB features with the help of enhanced optical flow features, making the overall features appropriate for the WTAL task. Experiments conducted on THUMOS’14 and ActivityNet v1.3 datasets show that our proposed approach far outperforms state-of-the-art WTAL methods. 

The subpopulation shifting challenge, known as some subpopulations of a category that are not seen during training, severely limits the classification performance of the state-of-the-art convolutional neural networks. Thus, to mitigate this practical issue, we explore incremental subpopulation learning (ISL) to adapt the original model via incrementally learning the unseen subpopulations without retaining the seen population data. However, striking a great balance between subpopulation learning and seen population forgetting is the main challenge in ISL but is not well studied by existing approaches. These incremental learners simply use a pre-defined and fixed hyperparameter to balance the learning objective and forgetting regularization, but their learning is usually biased towards either side in the long run. In this paper, we propose a novel two-stage learning scheme to explicitly disentangle the acquisition and forgetting for achieving a better balance between subpopulation learning and seen population forgetting: in the first “gain-acquisition” stage, we progressively learn a new classifier based on the margin-enforce loss, which enforces the hard samples and population to have a larger weight for classifier updating and avoid uniformly updating all the population; in the second “counter-forgetting” stage, we search for the proper combination of the new and old classifiers by optimizing a novel objective based on proxies of forgetting and acquisition. We benchmark the representative and state-of-the-art non-exemplar-based incremental learning methods on a large-scale subpopulation shifting dataset for the first time. Under almost all the challenging ISL protocols, we significantly outperform other methods by a large margin, demonstrating our superiority to alleviate the subpopulation shifting problem (Code is released in https://github.com/wuyujack/ISL). 

Existing solutions to instance-level visual identification usually aim to learn faithful and discriminative feature extractors from offline training data and directly use them for the unseen online testing data. However, their performance is largely limited due to the severe distribution shifting issue between training and testing samples. Therefore, we propose a novel online group-metric adaptation model to adapt the offline learned identification models for the online data by learning a series of metrics for all sharing-subsets. Each sharing-subset is obtained from the proposed novel frequent sharing-subset mining module and contains a group of testing samples that share strong visual similarity relationships to each other. Furthermore, to handle potentially large-scale testing samples, we introduce self-paced learning (SPL) to gradually include samples into adaptation from easy to difficult which elaborately simulates the learning principle of humans. Unlike existing online visual identification methods, our model simultaneously takes both the sample-specific discriminant and the set-based visual similarity among testing samples into consideration. Our method is generally suitable to any off-the-shelf offline learned visual identification baselines for online performance improvement which can be verified by extensive experiments on several widely-used visual identification benchmarks. 

Supervised dimensionality reduction for sequence data learns a transformation that maps the observations in sequences onto a low-dimensional subspace by maximizing the separability of sequences in different classes. It is typically more challenging than conventional dimensionality reduction for static data, because measuring the separability of sequences involves non-linear procedures to manipulate the temporal structures. In this paper, we propose a linear method, called Order-preserving Wasserstein Discriminant Analysis (OWDA), and its deep extension, namely DeepOWDA, to learn linear and non-linear discriminative subspace for sequence data, respectively. We construct novel separability measures between sequence classes based on the order-preserving Wasserstein (OPW) distance to capture the essential differences among their temporal structures. Specifically, for each class, we extract the OPW barycenter and construct the intra-class scatter as the dispersion of the training sequences around the barycenter. The inter-class distance is measured as the OPW distance between the corresponding barycenters. We learn the linear and non-linear transformations by maximizing the inter-class distance and minimizing the intra-class scatter. In this way, the proposed OWDA and DeepOWDA are able to concentrate on the distinctive differences among classes by lifting the geometric relations with temporal constraints. Experiments on four 3D action recognition datasets show the effectiveness of OWDA and DeepOWDA.