Search

The focus of few shot learning research has been on the development of meta-learning recently, where a meta-learner is trained on a variety of tasks in hopes of being generalizable to new tasks. Tasks in meta training and meta test are usually assumed to be from the same domain, which would not necessarily hold in real world scenarios. In this paper, we propose variational hyperparameter inference for few-shot learning across domains. Based on an especially successful algorithm named model agnostic meta learning, the proposed variational hyperparameter inference integrates meta learning and variational inference into the optimization of hyperparameters, which enables the meta-learner with adaptivity for generalization across domains. In particular, we choose to learn adaptive hyperparameters including the learning rate and weight decay to avoid the failure in the face of few labeled examples across domain. Moreover, we model hyperparameters as distributions instead of fixed values, which will further enhance the generalization ability by capturing the uncertainty. Extensive experiments are conducted on two benchmark datasets including few shot learning dataset within-domain and across-domain. The results demonstrate that our methods outperforms previous approaches consistently, and comprehensive ablation studies further validate its effectiveness on few shot learning both within domains and across domains. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation crowd counting (UDACC) has been studied with practical research utility by getting rid of the labeling burden on large-scale dense crowds in the target domain. Current methods generalize well within the specific domain gap by directly aligning domain distributions or translating synthetic data to realistic images. However, it is difficult to define domain gaps among complex real-world datasets, in which the images vary greatly in style, density level and/or content. To tackle this problem, in this paper, we propose a Cross-Domain Attention Network (CDANet), which can effectively generalize the model to the unlabeled domain on both unsupervised synthetic-to-realistic and realistic-to-realistic crowd counting. Specifically, we propose a Cross-Domain Attention Module (CDAM) to learn domain-related information between the source and target domain, which extracts relations in cross-domain attentive information, thus enhancing crowd-informative features. Moreover, to make our CDAM invariant to domain shifts, we introduce a consistency penalty to ensure that the attention maps are consistent before and after the domain shifting. Thus our CDANet can pay attention to the shared counting information across domains, while remaining its invariant ability during domain adaptation. Extensive experiments on several common benchmarks for UDACC demonstrate that our CDANet gets competitive results on both unsupervised synthetic-to-realistic and realistic-to-realistic UDACC tasks. [ABSTRACT FROM AUTHOR]

Few-shot learning has recently generated increasing popularity in machine learning, which addresses the fundamental yet challenging problem of learning to adapt to new tasks with the limited data. In this paper, we propose a new probabilistic framework that learns to fast adapt with external memory. We model the classifier parameters as distributions that are inferred from the support set and directly applied to the query set for prediction. The model is optimized by formulating as a variational inference problem. The probabilistic modeling enables better handling prediction uncertainty due to the limited data. We impose a discriminative constraint on the feature representations by exploring the class structure, which can improve the classification performance. We further introduce a memory unit to store task-specific information extracted from the support set and used for the query set to achieve explicit adaption to individual tasks. By episodic training, the model learns to acquire the capability of adapting to specific tasks, which guarantees its performance on new related tasks. We conduct extensive experiments on widely-used benchmarks for few-shot recognition. Our method achieves new state-of-the-art performance and largely surpassing previous methods by large margins. The ablation study further demonstrates the effectiveness of the proposed discriminative learning and memory unit. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new multi-layer learning architecture, the calibrated multivariate regression network (CMRN). Compared to previous multivariate models, the CMRN is able to simultaneously handle major challenges in multivariate regression including highly nonlinear input-output relationships, underlying inter-output correlations and calibration of multiple outputs within one single framework. The CMRN is comprised of a nonlinear module with cosine activations and a linear module with the low-rank expansion, which establishes a compact multivariate regression network. By seamlessly working with the $\ell _{2,1}$ loss, the CMRN automatically calibrates multiple outputs with distinct noise levels to achieve improved performance. Being succinctly formulated but theoretically well-founded, the CMRN offers a compact multi-layer learning architecture that can be efficiently trained to scale up with massive datasets. We conduct extensive experimental evaluation on two representative large multivariate regression tasks for both machine learning and computer vision. The proposed CMRN can produce high performance on all tasks, which is better or competitive to state-of-the-art models. Extensive ablation studies offer deep insights into the effectiveness of the proposed CMRN. [ABSTRACT FROM AUTHOR]

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. In this paper, we propose a new representation learning method, named Structure Transfer Machine (STM), which enables feature learning process to converge at the representation expectation in a probabilistic way. We theoretically show that such an expected value of the representation (mean) is achievable if the manifold structure can be transferred from the data space to the feature space. The resulting structure regularization term, named manifold loss, is incorporated into the loss function of the typical deep learning pipeline. The STM architecture is constructed to enforce the learned deep representation to satisfy the intrinsic manifold structure from the data, which results in robust features that suit various application scenarios, such as digit recognition, image classification and object tracking. Compared with state-of-the-art CNN architectures, we achieve better results on several commonly used public benchmarks. [ABSTRACT FROM AUTHOR]

The power line is one of the most hazardous obstacles for low-altitude aircrafts. As aircrafts usually encounter scenes like never before during the flight, cross-scene power line detection is the key for their flight safety. However, compared to regular object detection tasks, cross-scene power line detection is extremely challenging due to its weak visual appearance and widespread existence. In this letter, we propose a cross-scene power line detection method based on attentional information fusion networks. Specifically, we construct a fully convolutional network with attention and information fusion mechanism for cross-scene detection. The two main modules make full use of the semantic and location information, which enables the model to focus more on power lines rather than the unexpected scenes. To the best of author knowledge, our method establishes the first end-to-end convolutional architecture for pixelwise power line detection. Experimental results have shown that our method outperforms previous methods by large margins for cross-scene power line detection. [ABSTRACT FROM AUTHOR]

Dynamic scene classification has been extensively studied in computer vision due to its widespread applications. The key to dynamic scene classification lies in jointly characterizing spatial appearance and temporal dynamics to achieve informative representation, which remains an outstanding task in the literature. In this paper, we propose a unified framework to extract spatial and temporal features for dynamic scene representation. More specifically, we deploy two variants of deep convolutional neural networks to encode spatial appearance and short-term dynamics into short-term deep features (STDF). Based on STDF, we propose using the autoregressive moving average model to extract long-term frequency features (LTFF). By combining STDF and LTFF, we establish the long–short-term feature (LSTF) representations of dynamic scenes. The LSTF characterizes both spatial and temporal patterns of dynamic scenes for comprehensive and information representation that enables more accurate classification. Extensive experiments on three-dynamic scene classification benchmarks have shown that the proposed LSTF achieves high performance and substantially surpasses the state-of-the-art methods. [ABSTRACT FROM AUTHOR]