Domain adaptation is an important research topic in the field of computer vision, where the goal is to solve the difference of data distribution between different scenarios of the same task. In recent times, adversarial learning method becomes a mainstream approach to generate complicated images across diverse domains through optimizing deep networks, and it can also improve the recognition accuracy rate of deep networks despite existing domain shift or dataset bias. However, there are few effective efforts of domain adaptation for the disease diagnosis on fundus images. Fundus images are normally captured on different medical devices with different rules. When diagnosing glaucoma, there is a serious homogeneous domain shift, which means feature spaces between target domain and source domain images have a distribution shift although they are very similar. We propose a unified framework to solve this problem. Previous studies have shown that glaucoma can be monitored by analyzing the optic disc/cup and its surroundings. So we exploit a novel reconstruction loss which not only leverages unsupervised data to bring the source and target distributions closer but also keeps original target domain images label unchanged. The experimental results on several public and private datasets demonstrate that our method could increase the classification accuracy of glaucoma diagnosis.

This paper contributes to cross-lingual image annotation and retrieval in terms of data and baseline methods. We propose COCO-CN, a novel dataset enriching MS-COCO with manually written Chinese sentences and tags. For effective annotation acquisition, we develop a recommendation-assisted collective annotation system, automatically providing an annotator with several tags and sentences deemed to be relevant with respect to the pictorial content. Having 20 342 images annotated with 27 218 Chinese sentences and 70 993 tags, COCO-CN is currently the largest Chinese-English dataset that provides a unified and challenging platform for cross-lingual image tagging, captioning, and retrieval. We develop conceptually simple yet effective methods per task for learning from cross-lingual resources. Extensive experiments on the three tasks justify the viability of the proposed dataset and methods. Data and code are publicly available at https://github.com/li-xirong/coco-cn.

Generalized zero-shot learning (GZSL) aims to recognize any test instance coming either from a known class or from a novel class that has no training instance. To synthesize training instances for novel classes and thus resolving GZSL as a common classification problem, we propose a Dissimilarity Representation Learning (DSS) method. Dissimilarity representation is to represent a specific instance in terms of its (dis)similarity to other instances in a visual or attribute based feature space. In the dissimilarity space, instances of the novel classes are synthesized by an end-to-end optimized neural network. The neural network realizes two-level feature mappings and domain adaptions in the dissimilarity space and the attribute based feature space. Experimental results on five benchmark datasets, i.e., AWA, AWA$_2$, SUN, CUB, and aPY, show that the proposed method improves the state-of-the-art with a large margin, approximately 10% gain in terms of the harmonic mean of the top-1 accuracy. Consequently, this paper establishes a new baseline for GZSL.

This paper attacks the challenging problem of violence detection in videos. Different from existing works focusing on combining multi-modal features, we go one step further by adding and exploiting subclasses visually related to violence. We enrich the MediaEval 2015 violence dataset by manually labeling violence videos with respect to the subclasses. Such fine-grained annotations not only help understand what have impeded previous efforts on learning to fuse the multi-modal features, but also enhance the generalization ability of the learned fusion to novel test data. The new subclass based solution, with AP of 0.303 and P100 of 0.55 on the MediaEval 2015 test set, outperforms the state-of-the-art. Notice that our solution does not require fine-grained annotations on the test set, so it can be directly applied on novel and fully unlabeled videos. Interestingly, our study shows that motion related features (MBH, HOG and HOF), though being essential part in previous systems, are seemingly dispensable. Data is available at http://lixirong.net/datasets/mm2016vsd

With the increasing use of audio sensors in user generated content collection, how to detect semantic concepts using audio streams has become an important research problem. In this paper, we present a semantic concept annotation system using soundtracks/ audio of the video. We investigate three different acoustic feature representations for audio semantic concept annotation and explore fusion of audio annotation with visual annotation systems. We test our system on the data collection from HUAWEI Accurate and Fast Mobile Video Annotation Grand Challenge 2014. The experimental results show that our audio-only concept annotation system can detect semantic concepts significantly better than random guess. It can also provide significant complementary information to the visual-based concept annotation system for performance boost. Further detailed analysis shows that for interpreting a semantic concept both visually and acoustically, it is better to train concept models for the visual system and audio system using visual-driven and audio-driven ground truth separately.

With the increasing use of audio sensors in user generated content (UGC) collections, semantic concept annotation from video soundtracks has become an important research problem. In this paper, we investigate reducing the semantic gap of the traditional data-driven bag-of-audio-words based audio annotation approach by utilizing the large-amount of wild audio data and their rich user tags, from which we propose a new feature representation based on semantic class model distance. We conduct experiments on the data collection from HUAWEI Accurate and Fast Mobile Video Annotation Grand Challenge 2014. We also fuse the audio-only annotation system with a visual-only system. The experimental results show that our audio-only concept annotation system can detect semantic concepts significantly better than does random guessing. The new feature representation achieves comparable annotation performance with the bag-of-audio-words feature. In addition, it can provide more semantic interpretation in the output. The experimental results also prove that the audio-only system can provide significant complementary information to the visual-only concept annotation system for performance boost and for better interpretation of semantic concepts both visually and acoustically.

This paper proposed a framework to assist highlighting and annotating music utilization situation automatically within videos, further to supervise and protect music copyrights. Nowadays, music copyrighters pay attention to their rights increasingly, thus music embedded in TV channel videos should be validated to avoid infringing. Our framework supports Music Copyrighter Society of China(MCSC) to do statistic works to protect the copyright owners. In our framework, through AV separation, feature extractor, classification and assemblage functions, music positioning could be confirmed effectively. Then applying music fingerprint retrieving, music could be annotated automatically with high accuracy. Moreover, our framework is a closed loop self-adaptation system as it can be re-trained regularly to expand annotation database and enhance classifier's efficiency. The system based on our framework has been implemented in MCSC and its effectiveness has been evaluated in a real-life scenario. The results, on experiments of the real-life TV stations and comparisons of former works, show that the music positioning and annotation completed automatically by our system have significant improvement about over 30 times enhancement on the working efficiency.

How to estimate cross-media relevance between a given query and an unlabeled image is a key question in the MSR-Bing Image Retrieval Challenge. We answer the question by proposing cross-media relevance fusion, a conceptually simple framework that exploits the power of individual methods for cross-media relevance estimation. Four base cross-media relevance functions are investigated, and later combined by weights optimized on the development set. With DCG25 of 0.5200 on the test dataset, the proposed image retrieval system secures the first place in the evaluation.

In this paper, we propose an artificial bee group colony algorithm for numerical function optimization, based on the thoughts of group competition and similar property characteristic. Our algorithm contains three optimization strategies including grouping strategy, similar property strategy and competition strategy, which could not only ensure the algorithm finds better solutions stably, but also induce the algorithm to maintain solution diversification. Moreover, the similar property strategy could produce efficient exploring to find better solutions with skipping optimization. We evaluated the performance of our proposed algorithm on some standard numerical benchmark functions. The results demonstrate that our algorithm is able to yield higher quality solutions with faster convergence than either the original ABC or some other authoritative swarm intelligent algorithms.

Despite the impressive progress in music emotion recognition, it remains unclear what aspect of a song, i.e., singing voice and accompanied music, carries more emotional information. As an initial attempt to answer the question, we introduce source separation into a standard music emotion recognition system. This allows us to compare systems with and without source separation, and consequently reveal the influence of singing voice and accompanied music on emotion recognition. Classification experiments on a set of 267 songs with last.fm annotations verify the new finding that source separation improves song music emotion recognition.

In this paper, a novel hybrid evolutionary algorithm combining a Hopfield net and a local search strategy is proposed to solve maximum clique problem. The algorithm makes full use of powerful searching capability of Hopfield net and probabilistic statistic feature of estimation of distribution algorithm to produce wider search in global solution domain. In particular, a possible extension way correlated with local search optimization is introduced to affect the mutation probability thus to produce guided evolution. Experiments on the popular DIMACS benchmark demonstrate that the hybrid evolutionary algorithm produces comparable and better results than other compared algorithms, including EA/G which is a state-of-the-art algorithm in the field of evolutionary computation.

With the expansion of the Internet services, providing personalized product recommendations has become one of the most important ways to attract customers. Especially, collaborative recommender systems have achieved widespread success on the web. Information of products is recommended to the users based on their nearest "neighbors" who have similar interests. It is widely known that there is a sparsity problem in such systems. However, according to our research, there are other problems: one is that the typical collaborative algorithm loses some important parameter when it predicts the ratings, because there might be a strong similarity between the users who give very different ratings. Another is that the classification information of resources is not used. To solve these problems, we have proposed a recommendation algorithm combining the user-based classified regression and the item-based filtering. The experiment results show that performance is improved after applying the new algorithm.