nils-herrmann/clean_scirepeval huggingface dataset:
When Correlation Filters Meet Convolutional Neural Networks for Visual Tracking

Correlation filters have been widely applied to visual tracking in recent years as adaptive correlation filters with short-term memory are robust to large appearance changes. However, tracking methods relying on correlation filters are prone to drifting due to noisy updates. Moreover, these methods are unable to recover from tracking failures caused by temporary or persistent heavy occlusions. In this paper, we interpret correlation filters as the counterparts of convolution filters in deep neural networks. Correlation filters encode the holistic template of target appearance, while convolution filters with smaller size encode the part-based template. In the light of this idea, we propose to exploit deep convolutional networks that directly learn mapping as a spatial correlation between two consecutive frames for visual tracking. We show that these deeply learned networks are effective in maintaining the long-term memory of target appearance for handling heavy occlusion or out-of-view. We further take the response maps both from the deep networks and conventional correlation filters into account for precisely locating the target. Experimental results on large-scale benchmark sequences show that the proposed algorithm performs favorably against the state-of-the-art methods.
Spatial Error Concealment Technique using Verge Points

This paper presents a spatial error concealment technique using "verge points" which are the high-curvature points on image surfaces. Associated with curvature information, the verge points provide important characteristics in reconstructing broken edges. In our method, only a single layer of verge points surrounding the missing area are extracted. The broken edges are reconstructed by pairing these verge points with respect to both the curvature information and their neighboring pixel values. Simulations show that with our pairing criterion, most of the object edges are recovered and our concealment technique yields satisfactory results especially in terms of subjective perception.
Re-ranking image search results by multiscale visual saliency model

The paper focuses on two mechanisms, multiscale relevance and visual saliency, in web image search. First, in most current web image search engines, such as Google Image Search, Yahoo Image Search and so on, people judge the relevance of search results by the thumbnails and then click through the thumbnails to check if the corresponding image is really relevant. Basically the thumbnail and the corresponding image give the multiscale representations of the image. The second is that from visual point of view, it is obvious that salient images would be easier to catch users' eyes and more likely to be clicked than cluttered ones in low-level vision. In this paper, we build a multiscale saliency model and apply it to re-rank the results from web image search engines. Experimental results show that the model can achieve an average precision (AP)  of as high as 97%, and it improves the results of Google image search significantly.
H∞-optimal traffic predictor for VBR video

An H∞-optimal video traffic predicting algorithm is proposed, which not only minimizes the worst possible effect in the estimation error but also does not require a priori knowledge of the noise source statistics. The proposed method has been applied to modeling and predicting MPEG4 variable bit rate (VBR) video traffic. The low computational complexity and good prediction performance of the H∞ predictor are encouraging and reflect on the practicability of the proposed approach. Simulation results show that the proposed model is more robust compared with the Kalman predicting model and is able to predict the original traffic more accurately, thus more suitable for real time video traffic prediction and resource reservation.
A Convolutional Neural Network-Based Chinese Text Detection Algorithm via Text Structure Modeling

Text detection in a natural environment plays an important role in many computer vision applications. While existing text detection methods are focused on English characters, there are strong application demands on text detection in other languages, such as Chinese. In this paper, we present a novel text detection algorithm for Chinese characters based on a specific designed convolutional neural network (CNN). The CNN contains a text structure component detector layer, a spatial pyramid layer, and a multi-input-layer deep belief network (DBN). The CNN is pre-trained via a convolutional sparse auto-encoder, specifically designed for extracting complex features from Chinese characters. In particular, the text structure component detectors enhance the accuracy and uniqueness of feature descriptors by extracting multiple text structure components in various ways. The spatial pyramid layer enhances the scale invariability of the CNN for detecting texts in multiple scales. Finally, the multi-input-layer DBN replaces the fully connected layers in the CNN to ensure features from multiple scales are comparable. A multilingual text detection dataset, in which texts in Chinese, English, and digits are labeled separately, is set up to evaluate the proposed text detection algorithm. The proposed algorithm shows a significant performance improvement over the baseline CNN algorithms. In addition the proposed algorithm is evaluated over a public multilingual benchmark and achieves state-of-the-art result under multiple languages. Furthermore, a simplified version of the proposed algorithm with only general components is evaluated on the ICDAR 2011 and 2013 datasets, showing comparable detection performance to the existing general text detection algorithms.
Fast coding unit depth decision for HEVC

High Efficiency Video Coding (HEVC) achieves high efficiency by introducing a new coding structure in adoption of coding unit (CU), prediction unit (PU) and transform unit (TU). However, it also imposes great computation burden on the mode decision of encoders. In this paper, we propose a fast CU depth decision scheme to reduce the encoder complexity for HEVC. Firstly, the relationship between rate-distortion (R-D) cost and CU depth is explored carefully with Mean Squared Error (MSE) and Number of Encoded Bits (NEB) metrics. Then CU splitting is modeled as a binary classification problem and resolved by an offline trained Support Vector Machine (SVM) model. The experimental results show that the proposed algorithm achieves up to 59% running-time reduction with negligible loss in terms of PSNR and bit rate.
Recurrent Modeling of Interaction Context for Collective Activity Recognition

Modeling of high order interactional context, e.g., group interaction, lies in the central of collective/group activity recognition. However, most of the previous activity recognition methods do not offer a flexible and scalable scheme to handle the high order context modeling problem. To explicitly address this fundamental bottleneck, we propose a recurrent interactional context modeling scheme based on LSTM network. By utilizing the information propagation/aggregation capability of LSTM, the proposed scheme unifies the interactional feature modeling process for single person dynamics, intra-group (e.g., persons within a group) and inter-group(e.g., group to group)interactions. The proposed high order context modeling scheme produces more discriminative/descriptive interactional features. It is very flexible to handle a varying number of input instances (e.g., different number of persons in a group or different number of groups) and linearly scalable to high order context modeling problem. Extensive experiments on two benchmark collective/group activity datasets demonstrate the effectiveness of the proposed method.
A Matrix Decomposition Perspective to Multiple Graph Matching

Graph matching has a wide spectrum of real-world applications and in general is known NP-hard. In many vision tasks, one realistic problem arises for finding the global node mappings across a batch of corrupted weighted graphs. This paper is an attempt to connect graph matching, especially multi-graph matching to the matrix decomposition model and its relevant on-the-shelf convex optimization algorithms. Our method aims to extract the common inliers and their synchronized permutations from disordered weighted graphs in the presence of deformation and outliers. Under the proposed framework, several variants can be derived in the hope of accommodating to other types of noises. Experimental results on both synthetic data and real images empirically show that the proposed paradigm exhibits several interesting behaviors and in many cases performs competitively with the state-of-the-arts.
Interpolating fine texturess with fields of experts prior

Traditional image interpolation methods assume that the local spatial structure of the low-resolution (LR) and high-resolution (HR) images are approximately the same, and use edge information of the LR image to estimate the missing pixels. This assumption, however, no longer holds for natural images with fine and dense textures. Consequently, those methods cannot restore dense textures well and tend to generate over-fitting visual effects. In this paper, a learned HR image prior is exploited to overcome the problems. In particular, we use Fields of Experts (FoE) with student's t-distribution experts to model the prior, taking advantage of its representative ability of non-Gaussian natures in images. Then Maximum a Posterior (MAP) estimation incorporating FoE prior is used to estimate the missing pixels. Experimental results compared with traditional interpolation methods demonstrate that our method not only can recover fine details and produce superior PSNR values, but also avoid the visual over-fitting problems.
Learning object classes from image thumbnails through deep neural networks

We propose a new approach for recognizing object classes which is based on the intuitive idea that human beings are able to perform the task well given only thumbnails (coarse scale version) of images. Unlike previous work which uses local image features at fine scales, our approach uses thumbnails directly, and captures their high-order correlations at coarse scales through deep multi-layer neural networks based on restricted Boltzmann machines. Specifically, the pretraining stage of such networks takes on the role of feature extraction. Experimental results show that the proposed approach is comparable to other state-of-the-art recognition methods in terms of accuracy. The merits of the proposed approach come from the simplicity of the workflow and the parallelizability of the implementation structure.
Action Recognition with Actons

With the improved accessibility to an exploding amount of video data and growing demands in a wide range of video analysis applications, video-based action recognition/classification becomes an increasingly important task in computer vision. In this paper, we propose a two-layer structure for action recognition to automatically exploit a mid-level ``acton'' representation. The actons are learned via a new max-margin multi-channel multiple instance learning framework. The learned actons (with no requirement for detailed manual annotations) thus observe a property of being compact, informative, discriminative, and easy to scale. This is different from the standard unsupervised (e.g. k-means) or supervised (e.g. random forests) coding strategies in action recognition. Applying the learned actons in our two-layer structure yields the state-of-the-art classification performance on Youtube and HMDB51 datasets.
Transform domain transcoding from MPEG-2 to H.264 with interpolation error drift compensation

A complete transform domain solution to transcode MPEG-2 to H.264 is proposed. Firstly, we propose an efficient transcoding architecture for intra frames by taking advantage of the fact that the intra prediction of horizontal, vertical, and DC modes can be efficiently obtained in transform domain with very low computational complexity. Then, for the transcoding of inter frames, we analyze two major kinds of drifting error resulting from the mismatch of motion compensation: interpolation error and quantization error. The former is caused by the difference between the interpolation filters adopted in these two standards, thus very unique to the transform domain transcoding from MPEG-2 to H.264. As a major contribution of this paper, the close form of interpolation error is derived from transform domain. Experimental results show that the proposed transform domain transcoding scheme achieves very promising performance in terms of low computational complexity and high transcoded video quality, most importantly its PSNR is very close to the cascaded transcoding architecture with decoding and recoding process in pixel domain.
Progress in the development of the 170 GHz coaxial cavity gyrotron

The fabrication phase of an industrial prototype of a 2 MW, CW 170 GHz coaxial cavity gyrotron started recently. To verify experimentally the design of critical components, the previously used 165 GHz short pulse coaxial tube has been redesigned for operation at 170 GHz. After starting the gyrotron operation strong parasitic low frequency (LF) oscillations mainly at 260 and 330 MHz have been excited. Recently the LF oscillations have been suppressed and microwave measurements started.
Bit Allocation for Fine-Granular SNR Scalability Coding with Hierarchical B Pictures

Hierarchical B pictures are devised to achieve temporal scalability in the scalable extension of H.264/AVC (SVC) which is under standardization. The fine-granular SNR scalability (FGS) can be provided by progressive refinement (PR) slices in SVC. In this paper, we firstly investigate error propagation in the case of discarding PR slices and obtain a rate difference distortion optimization criterion to improve coding efficiency of base layer. Then we consider the full rate case and propose a rate distortion slope criterion to enhance FGS coding efficiency at high rate. Finally the criterion to boost coding efficiency in the whole range of FGS rate is derived by combing the criterions derived previously. The proposed method is compared to the approach in SVC test model and up to 0.3dB coding gains are achieved.
Quaternion Phase Congruency model for edge saliency map extraction and image blur measurement

Image edge detection is one of the most important foundations of image analysis and comprehension. Traditional edge detectors are not robust enough to illumination variations. In last decade, detector based on Phase Congruency (PC) model was proposed and invariant to illumination variations. In this paper, we introduce the quaternion Wavelet Transform to PC model and propose a new edge metric of quaternion Phase Congruency (QPC). QPC model overcomes one of the disadvantages of PC model. It is reliable to distinguish the significant skeleton structures from the relatively trivial texture structures. Experimental results show that our new model provides a more consistent edge saliency metric with human perception.
A novel text structure feature extractor for Chinese scene text detection and recognition

Scene text information extraction plays an important role in many computer vision applications. Unlike most existing text extraction algorithms for English texts, in this paper, we focus on Chinese texts, which are more complex in stroke and structure. To tackle this challenging problem, we propose a novel convolutional neural network (CNN) based text structure feature extractor for Chinese texts. Each Chinese character contains its specific types and combination of text structure components, which is rarely seen in backgrounds. Thus, different from the features only applicable to one text extraction stage (text detection or text recognition), the text structure component feature is suitable for both Chinese text detection and recognition. A text structure component detector (TSCD) layer is designed to detect the large amount of component types, which is the most challenging part of extracting text structure component features. Through statistical classification various types of text structure component are detected by their specially designed convolutional units in the TSCD layer. With the TSCD layer, the CNN has improvements in the accuracy and uniqueness of text feature description. In the evaluation, both text detection and recognition algorithms based on the proposed text structure feature extractor achieve state-of-the-art results in two datasets.
A Rate-distortion Based Quantization Level Adjustment Algorithm in Block-based Video Compression

In this paper, a rate-distortion based quantization level adjustment (RDQLA) algorithm is presented. Based on rate-distortion criterion, the quantization level adjustment algorithm effectively improves coding efficiency by adaptively optimizing quantization levels of the signals near the boundaries of quantization cells and adjusting quantization levels per block. The proposed algorithm can be applied in any block based image and video coding method. In addition, it has no overhead and is fully compatible with the existing compression standards. The algorithm has been verified on the platform of H.263 and H.264. Experimental results show that the proposed algorithm improves the performance substantially. It is shown that the proposed algorithm has a gain of 1 dB comparing with the newest H.264 reference code and more than 1 dB comparing with H.263 standard for high bit rates.
Evaluation of beyond-HEVC entropy coding methods for DCT transform coefficients

Entropy coding, which acts as one of the most important compression tools in video coding standard, had been improved step by step for HEVC. There are also several advanced methods which provide better performance than current solutions of HEVC proposed during the standardization of HEVC. However, these methods are all tested in different conditions. Comprehensive evaluation of these advanced methods under a common scenario is desired to indicate where the potential improvement of entropy coding may come from for next generation video codec. In this paper, we first introduce several advanced entropy coding methods for DCT transform coefficients, which aim to improve CABAC performance from two aspects - context modeling and probability updating. Then some modifications based on these original ones are presented. Comprehensive comparison of these methods is conducted under common test conditions. Besides, some combined methods of these two aspects are also tested. Experimental results show that all individual approaches can achieve coding gain and two new combined methods can reduce the BD-Rate up to 1.7%, 1.2% and 1.0% on common test sequences and 1.4%, 1.0% and 1.1% on 4K sequences under all intra, random access and low delay configurations, respectively.
Learning Semantic-Aligned Action Representation

A fundamental bottleneck for achieving highly discriminative action representation is that local motion/appearance features are usually not semantic aligned. Namely, a local feature, such as a motion vector or motion trajectory, does not possess any attribute that indicates which moving body part or operated object it is associated with. This mostly leads to global feature pooling/representation learning methods that are often too coarse. Inspired by the recent success of end-to-end (pixel-to-pixel) deep convolutional neural networks (DCNNs), in this paper, we first propose a DCNN architecture, which maps a human centric image region onto human body part response maps. Based on these response maps, we propose a second DCNN, which achieves semantic-aligned feature representation learning. Prior knowledge that only a few parts are responsible for a certain action is also utilized by introducing a group (part) sparseness prior during feature learning. The learned semantic-aligned feature not only boosts the discriminative capability of action representation, but also possesses the good nature of robustness to pose variations and occlusions. Finally, an iterative mining method is employed for learning discriminative action primitive detectors. Extensive experiments on action recognition benchmarks demonstrate a superior recognition performance of the proposed framework.
New bounds on image denoising: Viewpoint of sparse representation and non-local averaging

Image denoising plays a fundamental role in many image processing applications. Utilizing sparse representation and nonlocal averaging together is such a successful framework that leads to considerable progress in denoising. Almost all the newly proposed denoising algorithms are built base on it, different in detailed implementation, and the denoising performance seems converging. What is the denoising bound of this framework turns into a key question. In this paper, we assume all the possible algorithms under the framework can be approximated by a fixed two steps denoising process with different parameters. Step one cluster geometric similar image patches into groups so that patches within each group could be sparse represented under the basis of the group. Step two use the atoms of the group basis and radiometric similar patches of each patch for non-local averaging. The parameters of the process are the cluster number, the atoms and the number of radiometric similar patches for estimating each patch. Finally, the bound is derived as the minimum denoising error of all the possible parameters. Comparing with previous bounds, the new one is image specific and more practical. Experiment results show that there still exists room to improve the denoising performance for natural images.
Learning Mixtures of Markov Chains from Aggregate Data with Structural Constraints

Statistical models based on Markov chains, especially mixtures of Markov chains, have recently been studied and demonstrated to be effective in various data mining applications such as tourist flow analysis, animal migration modeling, and transportation administration. Nevertheless, the research so far has mainly focused on analyzing data at individual levels. Due to security and privacy reasons, however, the observations in practice usually consist of coarse-grained statistics of individual data, a.k.a. aggregate data, rendering learning mixtures of Markov chains an even more challenging problem. In this work, we show that this challenging problem, although intractable in its original form, can be solved approximately by posing structural constraints on the transition matrices. The proposed structural constraints include specifying active state sets corresponding to the chains and adding a pairwise sparse regularization term on transition matrices. Based on these two structural constraints, we propose a constrained least-squares method to learn mixtures of Markov chains. We further develop a novel iterative algorithm that decomposes the overall problem into a set of convex subproblems and solves each subproblem efficiently, making it possible to effectively learn mixtures of Markov chains from aggregate data. We propose a framework for generating synthetic data and analyze the complexity of our algorithm. Additionally, the empirical results of the convergence and the robustness of our algorithm are also presented. These results demonstrate the effectiveness and efficiency of the proposed algorithm, comparing with traditional methods. Experimental results on real-world data sets further validate that our algorithm can be used to solve practical problems.
165-GHz coaxial cavity gyrotron

The basic investigations on a coaxial cavity gyrotron operating at 165 GHz have been accomplished and the feasibility of manufacturing a 2-MW continuous-wave coaxial cavity gyrotron at 170 GHz has been demonstrated. In the last measurements, the data required for fabrication of an industrial tube have been completed. The influence of misalignment of the insert on gyrotron operation has been investigated. The limitation of the high-voltage performance of the electron gun due to buildup of a Penning discharge has been suppressed and the leakage current to the insert has been measured as a function of the retarding collector voltage.