Direction-Oriented Visual-semantic Embedding Model for Remote Sensing Image-text Retrieval

Image-text retrieval has developed rapidly in recent years. However, it is still a challenge in remote sensing due to visual-semantic imbalance, which leads to incorrect matching of non-semantic visual and textual features. To solve this problem, we propose a novel Direction-Oriented Visual-semantic Embedding Model (DOVE) to mine the relationship between vision and language. Concretely, a Regional-Oriented Attention Module (ROAM) adaptively adjusts the distance between the final visual and textual embeddings in the latent semantic space, oriented by regional visual features. Meanwhile, a lightweight Digging Text Genome Assistant (DTGA) is designed to expand the range of tractable textual representation and enhance global word-level semantic connections using less attention operations. Ultimately, we exploit a global visual-semantic constraint to reduce single visual dependency and serve as an external constraint for the final visual and textual representations. The effectiveness and superiority of our method are verified by extensive experiments including parameter evaluation, quantitative comparison, ablation studies and visual analysis, on two benchmark datasets, RSICD and RSITMD.

Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution

To solve the ill-posed problem of hyperspectral image super-resolution (HSISR), an usually method is to use the prior information of the hyperspectral images (HSIs) as a regularization term to constrain the objective function. Model-based methods using hand-crafted priors cannot fully characterize the properties of HSIs. Learning-based methods usually use a convolutional neural network (CNN) to learn the implicit priors of HSIs. However, the learning ability of CNN is limited, it only considers the spatial characteristics of the HSIs and ignores the spectral characteristics, and convolution is not effective for long-range dependency modeling. There is still a lot of room for improvement. In this paper, we propose a novel HSISR method that uses Transformer instead of CNN to learn the prior of HSIs. Specifically, we first use the proximal gradient algorithm to solve the HSISR model, and then use an unfolding network to simulate the iterative solution processes. The self-attention layer of Transformer makes it have the ability of spatial global interaction. In addition, we add 3D-CNN behind the Transformer layers to better explore the spatio-spectral correlation of HSIs. Both quantitative and visual results on two widely used HSI datasets and the real-world dataset demonstrate that the proposed method achieves a considerable gain compared to all the mainstream algorithms including the most competitive conventional methods and the recently proposed deep learning-based methods.

A Frequency Domain Constraint for Synthetic X-ray Image Super Resolution

Synthetic X-ray images can be helpful for image guiding systems and VR simulations. However, it is difficult to produce high-quality arbitrary view synthetic X-ray images in real-time due to limited CT scanning resolution, high computation resource demand or algorithm complexity. Our goal is to generate high-resolution synthetic X-ray images in real-time by upsampling low-resolution im-ages. Reference-based Super Resolution (RefSR) has been well studied in recent years and has been proven to be more powerful than traditional Single Image Su-per-Resolution (SISR). RefSR can produce fine details by utilizing the reference image but it still inevitably generates some artifacts and noise. In this paper, we propose texture transformer super-resolution with frequency domain (TTSR-FD). We introduce frequency domain loss as a constraint to further improve the quality of the RefSR results with fine details and without obvious artifacts. This makes a real-time synthetic X-ray image-guided procedure VR simulation system possible. To the best of our knowledge, this is the first paper utilizing the frequency domain as part of the loss functions in the field of super-resolution. We evaluated TTSR-FD on our synthetic X-ray image dataset and achieved state-of-the-art results.

Part of Speech Tagging in Thai Language Using Support Vector Machine

The elastic-input neuro tagger and hybrid tagger, combined with a neural network and Brill's error-driven learning, have already been proposed for the purpose of constructing a practical tagger using as little training data as possible. When a small Thai corpus is used for training, these taggers have tagging accuracies of 94.4% and 95.5% (accounting only for the ambiguous words in terms of the part of speech), respectively. In this study, in order to construct more accurate taggers we developed new tagging methods using three machine learning methods: the decision-list, maximum entropy, and support vector machine methods. We then performed tagging experiments by using these methods. Our results showed that the support vector machine method has the best precision (96.1%), and that it is capable of improving the accuracy of tagging in the Thai language. Finally, we theoretically examined all these methods and discussed how the improvements were achived.

Using a Support-Vector Machine for Japanese-to-English Translation of Tense, Aspect, and Modality

This paper describes experiments carried out using a variety of machine-learning methods, including the k-nearest neighborhood method that was used in a previous study, for the translation of tense, aspect, and modality. It was found that the support-vector machine method was the most precise of all the methods tested.

Correction of Errors in a Modality Corpus Used for Machine Translation by Using Machine-learning Method

We performed corpus correction on a modality corpus for machine translation by using such machine-learning methods as the maximum-entropy method. We thus constructed a high-quality modality corpus based on corpus correction. We compared several kinds of methods for corpus correction in our experiments and developed a good method for corpus correction.

CRL at Ntcir2

We have developed systems of two types for NTCIR2. One is an enhenced version of the system we developed for NTCIR1 and IREX. It submitted retrieval results for JJ and CC tasks. A variety of parameters were tried with the system. It used such characteristics of newspapers as locational information in the CC tasks. The system got good results for both of the tasks. The other system is a portable system which avoids free parameters as much as possible. The system submitted retrieval results for JJ, JE, EE, EJ, and CC tasks. The system automatically determined the number of top documents and the weight of the original query used in automatic-feedback retrieval. It also determined relevant terms quite robustly. For EJ and JE tasks, it used document expansion to augment the initial queries. It achieved good results, except on the CC tasks.

Meaning Sort - Three examples: dictionary construction, tagged corpus construction, and information presentation system

It is often useful to sort words into an order that reflects relations among their meanings as obtained by using a thesaurus. In this paper, we introduce a method of arranging words semantically by using several types of `{\sf is-a}' thesauri and a multi-dimensional thesaurus. We also describe three major applications where a meaning sort is useful and show the effectiveness of a meaning sort. Since there is no doubt that a word list in meaning-order is easier to use than a word list in some random order, a meaning sort, which can easily produce a word list in meaning-order, must be useful and effective.

A Machine-Learning Approach to Estimating the Referential Properties of Japanese Noun Phrases

The referential properties of noun phrases in the Japanese language, which has no articles, are useful for article generation in Japanese-English machine translation and for anaphora resolution in Japanese noun phrases. They are generally classified as generic noun phrases, definite noun phrases, and indefinite noun phrases. In the previous work, referential properties were estimated by developing rules that used clue words. If two or more rules were in conflict with each other, the category having the maximum total score given by the rules was selected as the desired category. The score given by each rule was established by hand, so the manpower cost was high. In this work, we automatically adjusted these scores by using a machine-learning method and succeeded in reducing the amount of manpower needed to adjust these scores.

Magical Number Seven Plus or Minus Two: Syntactic Structure Recognition in Japanese and English Sentences

George A. Miller said that human beings have only seven chunks in short-term memory, plus or minus two. We counted the number of bunsetsus (phrases) whose modifiees are undetermined in each step of an analysis of the dependency structure of Japanese sentences, and which therefore must be stored in short-term memory. The number was roughly less than nine, the upper bound of seven plus or minus two. We also obtained similar results with English sentences under the assumption that human beings recognize a series of words, such as a noun phrase (NP), as a unit. This indicates that if we assume that the human cognitive units in Japanese and English are bunsetsu and NP respectively, analysis will support Miller's $7 \pm 2$ theory.