nils-herrmann/clean_scirepeval huggingface dataset:
MeshStereo: A Global Stereo Model with Mesh Alignment Regularization for View Interpolation

We present a novel global stereo model designed for view interpolation. Unlike existing stereo models which only output a disparity map, our model is able to output a 3D triangular mesh, which can be directly used for view interpolation. To this aim, we partition the input stereo images into 2D triangles with shared vertices. Lifting the 2D triangulation to 3D naturally generates a corresponding mesh. A technical difficulty is to properly split vertices to multiple copies when they appear at depth discontinuous boundaries. To deal with this problem, we formulate our objective as a two-layer MRF, with the upper layer modeling the splitting properties of the vertices and the lower layer optimizing a region-based stereo matching. Experiments on the Middlebury and the Herodion datasets demonstrate that our model is able to synthesize visually coherent new view angles with high PSNR, as well as outputting high quality disparity maps which rank at the first place on the new challenging high resolution Middlebury 3.0 benchmark.
Video sequence learning and recognition via dynamic SOM

Information contained in video sequences is crucial for an autonomous robot or a computer to learn and respond to its surrounding environment. In the past, robot vision mainly concentrated on still image processing and small "image cube" processing. Continuous video sequence learning and recognition is rarely addressed in the literature due to its high requirement of dynamic processing. In this paper, we propose a novel neural network structure called dynamic self-organizing map (DSOM) for video sequence processing. The proposed technique has been tested on simulation data sets, and the results validate its learning/recognition ability.
Scalable Content-Aware Collaborative Filtering for Location Recommendation

Location recommendation plays an essential role in helping people find attractive places. Though recent research has studied how to recommend locations with social and geographical information, few of them addressed the cold-start problem of new users. Because mobility records are often shared on social networks, semantic information can be leveraged to tackle this challenge. A typical method is to feed them into explicit-feedback-based content-aware collaborative filtering, but they require drawing negative samples for better learning performance, as users’ negative preference is not observable in human mobility. However, prior studies have empirically shown sampling-based methods do not perform well. To this end, we propose a scalable Implicit-feedback-based Content-aware Collaborative Filtering (ICCF) framework to incorporate semantic content and to steer clear of negative sampling. We then develop an efficient optimization algorithm, scaling linearly with data size and feature size, and quadratically with the dimension of latent space. We further establish its relationship with graph Laplacian regularized matrix factorization. Finally, we evaluate ICCF with a large-scale LBSN dataset in which users have profiles and textual content. The results show that ICCF outperforms several competing baselines, and that user information is not only effective for improving recommendations but also coping with cold-start scenarios.
Building Hierarchical Representations for Oracle Character and Sketch Recognition

In this paper, we study oracle character recognition and general sketch recognition. First, a data set of oracle characters, which are the oldest hieroglyphs in China yet remain a part of modern Chinese characters, is collected for analysis. Second, typical visual representations in shape- and sketch-related works are evaluated. We analyze the problems suffered when addressing these representations and determine several representation design criteria. Based on the analysis, we propose a novel hierarchical representation that combines a Gabor-related low-level representation and a sparse-encoder-related mid-level representation. Extensive experiments show the effectiveness of the proposed representation in both oracle character recognition and general sketch recognition. The proposed representation is also complementary to convolutional neural network (CNN)-based models. We introduce a solution to combine the proposed representation with CNN-based models, and achieve better performances over both approaches. This solution has beaten humans at recognizing general sketches.
Webdove: a Web-Based Collaboration System for Physical Tasks

While many systems are available for audio-visual people collaboration and data collaboration, systems for collaboration on physical objects are few. In this paper, we present WebDOVE, a system designed to address the needs of collaborative physical tasks. WebDOVE supports both live video streams and pen-based gesture recognition in multi-party bidirectional communication via inexpensive Web cameras. WebDOVE allows distributed collaborators to draw over video streams to produce and interpret pointing and representational gestures as readily as they do in face-to-face settings. To accommodate potential diverse platform requirements from different participants, WebDOVE is designed to be a Web-based platform-independent and browser-independent collaboration solution. We show via experiments that despite WebDOVE's platform independency, it requires moderate network bandwidth and CPU load, which make WebDOVE a practical solution for real-world applications
Learning to Rank Using User Clicks and Visual Features for Image Retrieval

The inconsistency between textual features and visual contents can cause poor image search results. To solve this problem, click features, which are more reliable than textual information in justifying the relevance between a query and clicked images, are adopted in image ranking model. However, the existing ranking model cannot integrate visual features, which are efficient in refining the click-based search results. In this paper, we propose a novel ranking model based on the learning to rank framework. Visual features and click features are simultaneously utilized to obtain the ranking model. Specifically, the proposed approach is based on large margin structured output learning and the visual consistency is integrated with the click features through a hypergraph regularizer term. In accordance with the fast alternating linearization method, we design a novel algorithm to optimize the objective function. This algorithm alternately minimizes two different approximations of the original objective function by keeping one function unchanged and linearizing the other. We conduct experiments on a large-scale dataset collected from the Microsoft Bing image search engine, and the results demonstrate that the proposed learning to rank models based on visual features and user clicks outperforms state-of-the-art algorithms.
Recognizing Faces in Recorded Meetings via MRC-Boosting

Person-based indices and timelines can enable fast and non-linear access to recorded meetings. This paper focuses on how to automatically construct those indices and timelines by using face recognition techniques. While there exist extensive research in generic face recognition, recognizing faces in recorded meetings is still an understudied area. Real-world meeting videos impose several interesting and unique challenges including complex lighting, low imaging quality, and large variations in head pose and size. In this paper, a promising approach based on MRC-Boosting is presented to address these challenges, which achieves encouraging performance on real-world meeting videos and shows superior accuracy and robustness compared to two popular existing approaches
Click Prediction for Web Image Reranking Using Multimodal Sparse Coding

Image reranking is effective for improving the performance of a text-based image search. However, existing reranking algorithms are limited for two main reasons: 1) the textual meta-data associated with images is often mismatched with their actual visual content and 2) the extracted visual features do not accurately describe the semantic similarities between images. Recently, user click information has been used in image reranking, because clicks have been shown to more accurately describe the relevance of retrieved images to search queries. However, a critical problem for click-based methods is the lack of click data, since only a small number of web images have actually been clicked on by users. Therefore, we aim to solve this problem by predicting image clicks. We propose a multimodal hypergraph learning-based sparse coding method for image click prediction, and apply the obtained click data to the reranking of images. We adopt a hypergraph to build a group of manifolds, which explore the complementarity of different features through a group of weights. Unlike a graph that has an edge between two vertices, a hyperedge in a hypergraph connects a set of vertices, and helps preserve the local smoothness of the constructed sparse codes. An alternating optimization procedure is then performed, and the weights of different modalities and the sparse codes are simultaneously obtained. Finally, a voting strategy is used to describe the predicted click as a binary event (click or no click), from the images' corresponding sparse codes. Thorough empirical studies on a large-scale database including nearly 330 K images demonstrate the effectiveness of our approach for click prediction when compared with several other methods. Additional image reranking experiments on real-world data show the use of click prediction is beneficial to improving the performance of prominent graph-based image reranking algorithms.
study of Engine Intelligent Control Based on Multi-Agent

The article studied the application of multi-agent in engine control. The outstanding feature of multi-agent control is to no need building precise mathematical model for control object, it may simulate people's thinking to simplify complex problem and choose different method to control multi mode complex nonlinear system. The system is programmed by C language, assemble language and MATLAB language, the technology of modularity and real time multitasking control mechanism are used. Simulation and test prove that multi-agent control system for engine has good stability and fast dynamic response and stronger anti jamming ability
Two-Dimensional Multilabel Active Learning with an Efficient Online Adaptation Model for Image Classification

Conventional active learning dynamically constructs the training set only along the sample dimension. While this is the right strategy in binary classification, it is suboptimal for multilabel image classification. We argue that for each selected sample, only some effective labels need to be annotated while others can be inferred by exploring the label correlations. The reason is that the contributions of different labels to minimizing the classification error are different due to the inherent label correlations. To this end, we propose to select sample-label pairs, rather than only samples, to minimize a multilabel Bayesian classification error bound. We call it two-dimensional active learning because it considers both the sample dimension and the label dimension. Furthermore, as the number of training samples increases rapidly over time due to active learning, it becomes intractable for the offline learner to retrain a new model on the whole training set. So we develop an efficient online learner to adapt the existing model with the new one by minimizing their model distance under a set of multilabel constraints. The effectiveness and efficiency of the proposed method are evaluated on two benchmark data sets and a realistic image collection from a real-world image sharing Web site-Corbis.
Optimal adaptive learning for image retrieval

Learning-enhanced relevance feedback is one of the most promising and active research directions in content-based image retrieval. However, the existing approaches either require prior knowledge of the data or entail high computation costs, making them less practical. To overcome these difficulties and motivated by the successful history of optimal adaptive filters, we present a new approach to interactive image retrieval. Specifically, we cast the image retrieval problem in the optimal filtering framework, which does not require prior knowledge of the data, supports incremental learning, is simple to implement and achieves better performance than state-of-the-art approaches. To evaluate the effectiveness and robustness of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images. We report promising results on a wide variety of queries.
Content-Aware Collaborative Filtering for Location Recommendation Based on Human Mobility Data

Location recommendation plays an essential role in helping people find places they are likely to enjoy. Though some recent research has studied how to recommend locations with the presence of social network and geographical information, few of them addressed the cold-start problem, specifically, recommending locations for new users. Because the visits to locations are often shared on social networks, rich semantics (e.g., tweets) that reveal a person's interests can be leveraged to tackle this challenge. A typical way is to feed them into traditional explicit-feedback content-aware recommendation methods (e.g., LibFM). As a user's negative preferences are not explicitly observable in most human mobility data, these methods need draw negative samples for better learning performance. However, prior studies have empirically shown that sampling-based methods don't perform as well as a method that considers all unvisited locations as negative but assigns them a lower confidence. To this end, we propose an Implicit-feedback based Content-aware Collaborative Filtering (ICCF) framework to incorporate semantic content and steer clear of negative sampling. For efficient parameter learning, we develop a scalable optimization algorithm, scaling linearly with the data size and the feature size. Furthermore, we offer a good explanation to ICCF, such that the semantic content is actually used to refine user similarity based on mobility. Finally, we evaluate ICCF with a large-scale LBSN dataset where users have profiles and text content. The results show that ICCF outperforms LibFM of the best configuration, and that user profiles and text content are not only effective at improving recommendation but also helpful for coping with the cold-start problem.
Analysis on spatial-temporal characteristics of interannual variation of maximum daily precipitation in Jiangxi Province

On the basis of the daily precipitation data statistics of 17 meteorological stations from 1953 to 2010 in Jiangxi Province, this paper studied the interannual variation characteristics of maximum daily precipitation and its spatial-temporal characteristics in Jiangxi Province with the application of GIS technology and Mann-Kendall (M-K) statistic method. The results showed that there was a slightly constant rising tendency of monthly average maximum daily precipitation in flood duration in Jiangxi from 1953 to 2010, especially in the Southern Jiangxi with the confidence of 90%; For the whole province, the abrupt change of the average monthly maximum daily precipitation in flood duration took place in 1992, with the confidence of 95%; moreover, the confidence of Eastern, North Central and Southern were 95%,95% and 90%.
Browsing and retrieving video content in a unified framework

In this paper, we first review the recent research progress in video analysis, representation, browsing, and retrieval. Motivated by the mechanism used to access a book's content, we then present novel techniques for constructing video table-of-contents and index to facilitate accessing video's content. We further explore the relationship between video browsing and retrieval and propose a unified framework to incorporate both entities in a seamless way. Preliminary research results justify our proposed framework for providing access to videos based on their content.
Water-filling: a novel way for image structural feature extraction

The performance of a content based image retrieval (CBIR) system is inherently constrained by the features adopted to represent the images in the database. In this paper, a new approach is proposed for image feature extraction based on edge maps. The feature vector with multiple feature components is computed through a “water-filling algorithm” applied on the edge map of the original image. The idea of this algorithm is to obtain measures of the edge length and complexity by graph traverse. The new feature is move generally applicable than texture or shape features. We call this structure feature. Experiments show that the new feature is capable of catching salient edge/structure information in the images. An experimental retrieval system utilizing the proposed new features yields better results in retrieving city/building images than some global texture features (wavelet moments). The new feature is ideal for images with clear edge structure. After combining the new features with other features in a relevance feedback framework, satisfactory retrieval results are observed.
JPDAF based HMM for real-time contour tracking

Tracking objects using multiple cues yields more robust results. The well-known hidden Markov model (HMM) provides a powerful framework to incorporate multiple cues by expanding its observation. However, a plain HMM does not capture the inter-correlation between measurements of neighboring states when computing the transition probabilities. This can seriously damage the tracking performance. To overcome this difficulty, we propose a novel HMM framework targeted at contour-based object tracking. A joint probability data association filter (JPDAF) is used to compute the HMM's transition probabilities, taking into account the intercorrelated neighboring measurements. To ensure real-time performance, we have further developed an efficient method to calculate the data association probability via dynamic programming, which allows the proposed JPDAF-HMM to run comfortably at 30 frames/sec. This new tracking framework can easily incorporate various image cues (e.g., edge intensity, foreground region color and background region color), and also offers an online learning process to adapt to changes in the scene. To evaluate its tracking performance, we have applied the proposed JPDAF-HMM in various real-world video sequences. We report promising tracking results in complex environments.
Mode-based multi-hypothesis head tracking using parametric contours

The paper describes a probabilistic mode-based multi-hypothesis tracking (MHT) algorithm. The modes are the local maximums refined from initial samples in a parametric state space. Because the modes are highly representative, this technique allows us to use a small number of hypotheses to effectively model nonlinear probabilistic distributions. To ensure real-time tracking performance, we propose a novel parametric causal contour model and an efficient dynamic programming scheme to refine the initial contours to nearby modes. Furthermore, to overcome the common drawback of conventional MHT techniques, i.e., producing only the maximum likelihood estimates instead of the desired posterior, we introduce the highly effective importance sampling framework into MHT, and develop a novel procedure to estimate the posterior from the importance function. Experiments on a challenging real-world video sequence demonstrate that the proposed tracking technique is both robust in complex environment (e.g., clutter background and partial occlusion) and efficient in computation.
A joint appearance-spatial distance for kernel-based image categorization

The goal of image categorization is to classify a collection of unlabeled images into a set of predefined classes to support semantic-level image retrieval. The distance measures used in most existing approaches either ignored the spatial structures or used them in a separate step. As a result, these distance measures achieved only limited success. To address these difficulties, in this paper, we propose a new distance measure that integrates joint appearance-spatial image features. Such a distance measure is computed as an upper bound of an information-theoretic discrimination, and can be computed efficiently in a recursive formulation that scales well to image size. In addition, the upper bound approximation can be further tightened via adaption learning from a universal reference model. Extensive experiments on two widely-used data sets show that the proposed approach significantly outperforms the state-of-the-art approaches.
Time delay estimation in the presence of correlated noise and reverberation

We propose a new two-stage framework for time delay estimation in the presence of correlated noise and reverberation. The new framework allows us to develop a set of new approaches as well as to unify existing ones. We further develop the maximum likelihood estimation when reverberation is present. The corresponding weighting function is a more accurate form of the weighting function proposed by H. Wang and P. Chu (Proc. ICASSP, 1997), one of the best existing techniques. We compare our new algorithms with the existing ones and report superior performance.
Real-time speaker tracking using particle filter sensor fusion

Sensor fusion for object tracking has become an active research direction during the past few years. But how to do it in a robust and principled way is still an open problem. In this paper, we propose a new fusion framework that combines both the bottom-up and top-down approaches to probabilistically fuse multiple sensing modalities. At the lower level, individual vision and audio trackers are designed to generate effective proposals for the fuser. At the higher level, the fuser performs reliable tracking by verifying hypotheses over multiple likelihood models from multiple cues. Unlike traditional fusion algorithms, the proposed framework is a closed-loop system where the fuser and trackers coordinate their tracking information. Furthermore, to handle nonstationary situations, the proposed framework evaluates the performance of the individual trackers and dynamically updates their object states. We present a real-time speaker tracking system based on the proposed framework by fusing object contour, color and sound source location. We report robust tracking results.
PING: a Group-to-Individual Distributed Meeting System

Group-to-individual (G2I) distributed meeting is an important but understudied area. Because of the asymmetry between different parties in G2I meetings, it has two unique challenges: l)the remote participant tends to be ignored by the local participants; and 2) the remote participant has inferior audio, video, and data experience than the local participants. To address these issues, in this paper we present PING, a system explicitly designed for G2I distributed meetings that combines recent advances in both hardware, e.g., microphone arrays, remote person stand-in devices, and software, e.g., audio-video processing, to improve users' G2I meeting experience. We report how PING addresses the above two challenges and its system design and implementation
Efficient access to video content in a unified framework

Book contents' browsing and retrieval have been greatly facilitated by its Table-of-Contents (ToC) and Index, respectively. Unfortunately, today's video lacks such powerful mechanisms. In this paper, we explore and present novel techniques for constructing video ToC and Index. Furthermore, we explore the relationship between video browsing and retrieval and propose a unified framework: to incorporate both entities in a seamless way. Experimental results on real-world video clips justify our proposed framework for providing efficient access to video content.