nils-herrmann/clean_scirepeval huggingface dataset:
Robust Feature Matching with Alternate Hough and Inverted Hough Transforms

We present an algorithm that carries out alternate Hough transform and inverted Hough transform to establish feature correspondences, and enhances the quality of matching in both precision and recall. Inspired by the fact that nearby features on the same object share coherent homographies in matching, we cast the task of feature matching as a density estimation problem in the Hough space spanned by the hypotheses of homographies. Specifically, we project all the correspondences into the Hough space, and determine the correctness of the correspondences by their respective densities. In this way, mutual verification of relevant correspondences is activated, and the precision of matching is boosted. On the other hand, we infer the concerted homographies propagated from the locally grouped features, and enrich the correspondence candidates for each feature. The recall is hence increased. The two processes are tightly coupled. Through iterative optimization, plausible enrichments are gradually revealed while more correct correspondences are detected. Promising experimental results on three benchmark datasets manifest the effectiveness of the proposed approach.
Multiple Structured-Instance Learning for Semantic Segmentation with Uncertain Training Data

We present an approach MSIL-CRF that incorporates multiple instance learning (MIL) into conditional random fields (CRFs). It can generalize CRFs to work on training data with uncertain labels by the principle of MIL. In this work, it is applied to saving manual efforts on annotating training data for semantic segmentation. Specifically, we consider the setting in which the training dataset for semantic segmentation is a mixture of a few object segments and an abundant set of objects' bounding boxes. Our goal is to infer the unknown object segments enclosed by the bounding boxes so that they can serve as training data for semantic segmentation. To this end, we generate multiple segment hypotheses for each bounding box with the assumption that at least one hypothesis is close to the ground truth. By treating a bounding box as a bag with its segment hypotheses as structured instances, MSIL-CRF selects the most likely segment hypotheses by leveraging the knowledge derived from both the labeled and uncertain training data. The experimental results on the Pascal VOC segmentation task demonstrate that MSIL-CRF can provide effective alternatives to manually labeled segments for semantic segmentation.
Multi-view face detection in videos with online adaptation

Most learning-based approaches to face detection suffer from the problem of performance degradation on faces that are not covered by training data. However, including all variations of faces in training is practically infeasible due to the scalability restriction of machine learning algorithms and expensive manual labeling. In this work, we focus on face detection in videos, and alleviate this problem by exploiting strong correlation among video frames. We augment a pre-trained multiview face detection with an incrementally derived Gaussian process regressor. The regressor can extract and propagate visual knowledge across frames, and adapts the detector to handle unseen faces. Testing on two datasets, the promising results manifest the effectiveness of the proposed approach.
Shape recognition using fast boosted filtering

We address the problem of recognizing 2-D shapes in images via multi-class classifications. Our approach has three key elements. First, a signed distance transform is introduced to represent a shape more informatively. Second, a filter bank is generated such that its filters can capture multiple-scale local and global features between two shapes of different classes. We then apply boosting to combine useful filters to construct discriminant classifiers. Third, in implementing our system, a new classification architecture is developed to accomplish multi-class recognition. To examine the claimed efficiencies, we consider an example of document recognition by pinpointing the strengths of our method through experimental results and comparisons.
USEAQ: Ultra-Fast Superpixel Extraction via Adaptive Sampling From Quantized Regions

We present a novel and highly efficient superpixel extraction method called ultra-fast superpixel extraction via adaptive sampling from quantized regions (USEAQ) to generate regular and compact superpixels in an image. To reduce the computational cost of iterative optimization procedures adopted in most recent approaches, the proposed USEAQ for superpixel generation works in a one-pass fashion. It first performs joint spatial and color quantizations and groups pixels into regions. It then takes into account the variations between regions, and adaptively samples one or a few superpixel candidates for each region. It finally employs maximum a posteriori estimation to assign pixels to the most spatially consistent and perceptually similar superpixels. It turns out that the proposed USEAQ is quite efficient, and the extracted superpixels can precisely adhere to boundaries of objects. Experimental results show that USEAQ achieves better or equivalent performance compared with the state-of-the-art superpixel extraction approaches in terms of boundary recall, undersegmentation error, achievable segmentation accuracy, the average miss rate, average undersegmentation error, and average unexplained variation, and it is significantly faster than these approaches. The source code of USEAQ is available at https://github.com/nchucvml/USEAQ.
Learning Discriminatively Reconstructed Source Data for Object Recognition With Few Examples

We aim at improving the object recognition with few training data in the target domain by leveraging abundant auxiliary data in the source domain. The major issue obstructing knowledge transfer from source to target is the limited correlation between the two domains. Transferring irrelevant information from the source domain usually leads to performance degradation in the target domain. To address this issue, we propose a transfer learning framework with the two key components, such as discriminative source data reconstruction and dual-domain boosting. The former correlates the two domains via reconstructing source data by target data in a discriminative manner. The latter discovers and delivers only knowledge shared by the target data and the reconstructed source data. Hence, it facilitates recognition in the target. The promising experimental results on three benchmarks of object recognition demonstrate the effectiveness of our approach.
Structural characteristics, process and effectiveness of cross-functional teams consisted of specialists and technicians in the healthcare industry

The healthcare industry is served by various specialists and technicians, such as doctors, nurses, pharmacists, medical technicians, radiologists, etc. They form the major workforce in hospitals. In order to provide good healthcare services, teamwork among them is of great importance, and the management of cross-functional teams consisting of specialists and technicians is a significant issue. This study is mainly based on the Input-Process-Output team theoretical model from McGrath and explores some essential variables of teams, including team structural characteristics, team process and team effectiveness. Team structural characteristics are discussed in terms of the size, diversity, norm and cohesion of teams. Team process was divided into two factors: members' cooperation and bottom-up communication. Team effectiveness was measured in terms of performance and satisfaction. Samples of this study were taken from 20 hospitals in Taiwan. A total of 109 valid questionnaires were used in this survey. The results are as follows: (1) On team structural characteristics, only team norm affects team effectiveness. (2) Team process affects team effectiveness. (3) Team norm and team cohesion affect team process. (4) Team process exerts full mediating effects between team structural characteristics and satisfaction; while team process mediates partially between team structural characteristics and performance.
Multimodal kernel learning for image retrieval

We propose a semi-supervised learning technique to address the problem of fusing multimodal information sources for CBIR. In our approach, user's preferences in the form of reference feedback are treated as labeled data, and the key idea is to devise an on-line scheme to effectively transform the abstract semantics into useful training data for improving the query performance. Specifically, our method can be characterized with the following three advantages: 1) Kernel matrices are used to encode each modality of information so that the fusion can be conveniently carried out via boosting; 2) The base kernel matrices are derived from eigendecomposing the graph Laplacian, and further refined to satisfy a pivotal monotone property that ensures intrinsic structure will be reasonably maintained for each modality; 3) The adopted optimization criterion in boosting is to align with a target kernel matrix accounting for relevance feedback, and the learned multimodal kernel matrix can be used for training, and then for testing with those unlabeled ones in the database. To demonstrate the efficiency of the proposed framework, experimental results on CBIR are provided to illustrate several practical considerations.
Per-Cluster Ensemble Kernel Learning for Multi-Modal Image Clustering With Group-Dependent Feature Selection

In this paper, we present a clustering approach, MK-SOM, that carries out cluster-dependent feature selection, and partitions images with multiple feature representations into clusters. This work is motivated by the observations that human visual systems (HVS) can receive various kinds of visual cues for interpreting the world. Images identified by HVS as the same category are typically coherent to each other in certain crucial visual cues, but the crucial cues vary from category to category. To account for this observation and bridge the semantic gap, the proposed MK-SOM integrates multiple kernel learning (MKL) into the training process of self-organizing map (SOM), and associates each cluster with a learnable, ensemble kernel. Hence, it can leverage information captured by various image descriptors, and discoveries the cluster-specific characteristics via learning the per-cluster ensemble kernels. Through the optimization iterations, cluster structures are gradually revealed via the features specified by the learned ensemble kernels, while the quality of these ensemble kernels is progressively improved owing to the coherent clusters by enforcing SOM. Besides, MK-SOM allows the introduction of side information to improve performance, and it hence provides a new perspective of applying MKL to address both unsupervised and semi-supervised clustering tasks. Our approach is comprehensively evaluated in the two applications. The superior and promising results manifest its effectiveness.
Augmented multiple instance regression for inferring object contours in bounding boxes.

In this paper, we address the problem of the high annotation cost of acquiring training data for semantic segmentation. Most modern approaches to semantic segmentation are based upon graphical models, such as the conditional random fields, and rely on sufficient training data in form of object contours. To reduce the manual effort on pixel-wise annotating contours, we consider the setting in which the training data set for semantic segmentation is a mixture of a few object contours and an abundant set of bounding boxes of objects. Our idea is to borrow the knowledge derived from the object contours to infer the unknown object contours enclosed by the bounding boxes. The inferred contours can then serve as training data for semantic segmentation. To this end, we generate multiple contour hypotheses for each bounding box with the assumption that at least one hypothesis is close to the ground truth. This paper proposes an approach, called augmented multiple instance regression (AMIR), that formulates the task of hypothesis selection as the problem of multiple instance regression (MIR), and augments information derived from the object contours to guide and regularize the training process of MIR. In this way, a bounding box is treated as a bag with its contour hypotheses as instances, and the positive instances refer to the hypotheses close to the ground truth. The proposed approach has been evaluated on the Pascal VOC segmentation task. The promising results demonstrate that AMIR can precisely infer the object contours in the bounding boxes, and hence provide effective alternatives to manually labeled contours for semantic segmentation.
Precise player segmentation in team sports videos using contrast-aware co-segmentation

Player segmentation in team sports videos is challenging but crucial to video semantic understanding, such as player interaction identification and tactic analysis. We leverage the appearance similarity among players of the same team, and cast this task as a co-segmentation problem. In this way, the extra knowledge shared across players significantly reduces unfavorable uncertainty in segmenting individual players. We are also aware that the performance of co-segmentation highly depends on the used features, and further propose a contrast-based approach to estimate the discriminant power of each feature in an unsupervised manner. It turns out that our approach can properly fuse features by assigning higher weights to discriminant ones, and result in remarkable performance gains. The promising results on segmenting basketball players manifest the effectiveness of our approach.
The relationship between supervisory support and work-family conflict: The guanxi, LMX, and emotional intelligence perspectives

Previous studies of manufacturing management have ignored a critical theme: the relationship between supervisory support and work-family conflict. In this study, we explore the link between interpersonal relationships, guanxi, leader-member exchange theory, emotional intelligence, supervisory support, and work-family conflict. We gathered 244 valid questionnaires from workers in traditional industries in Taiwan and China. In the rapidly changing society of Taiwan, we found that supervisory support for work-family conflict had faded in traditional industries. Second, we found that leaders with a higher level of leader-member exchange and expressive ties to their subordinates tend to offer a higher level of supervisory support, but that leaders with higher level of instrumental ties to their subordinates tend to offer lower levels of support. Finally, the survey results also showed that a leaderpsilas level of emotional intelligence is not related to supervisory support. We discuss our findings in terms of their implications for management practices and future research.
Action recognition using instance-specific and class-consistent cues

We aim to resolve the difficulties of action recognition arising from the large intra-class variations. These unfavorable variations make it infeasible to represent one action instance by other ones of the same action. We hence propose to extract both instance-specific and class-consistent features to facilitate action recognition. Specifically, the instance-specific features explore the self-similarities among frames of each video instance, while class-consistent features summarize within-class similarities. We introduce a generative formulation to combine the two diverse types of features. The experimental results demonstrate the effectiveness of our approach.
Recognizing offensive tactics in broadcast basketball videos via key player detection

We address offensive tactic recognition in broadcast basketball videos. As a crucial component towards basketball video content understanding, tactic recognition is quite challenging because it involves multiple independent players, each of which has respective spatial and temporal variations. Motivated by the observation that most intra-class variations are caused by non-key players, we present an approach that integrates key player detection into tactic recognition. To save the annotation cost, our approach can work on training data with only video-level tactic annotation, instead of key players labeling. Specifically, this task is formulated as an MIL (multiple instance learning) problem where a video is treated as a bag with its instances corresponding to subsets of the five players. We also propose a representation to encode the spatio-temporal interaction among multiple players. It turns out that our approach not only effectively recognizes the tactics but also precisely detects the key players.
Cross camera people counting with perspective estimation and occlusion handling

We introduce a novel approach to cross camera people counting that can adapt itself to a new environment without the need of manual inspection. The proposed counting model is composed of a pair of collaborative Gaussian processes (GP), which are respectively designed to count people by taking the visible and occluded parts into account. While the first GP exploits multiple visual features to result in better accuracy, the second GP instead investigates the conflicts among these features to recover the underestimate caused by occlusions. Our contributions are threefold. First, we establish a cross camera people counting system that can facilitate forensics investigation and security preservation. Second, a principled way is proposed to estimate the degree of occlusions. Third, our system is comprehensively evaluated on two benchmark datasets. The promising performance demonstrates the effectiveness of our system.
Robust feature matching via multiple descriptor fusion

We present a novel approach to boost image matching performance by fusing multiple local descriptors in the homography space. Traditional matching methods find correspondences based on a single descriptor and the performance becomes unstable due to the goodness of the chosen descriptor To address this problem, our method uses multiple descriptors and select a good descriptor for matching each feature point. Specifically, we project every correspondence into the homography space, where correct correspondences tend to gather together due to the similarity of their homographies. Then kernel density estimation is applied to measure the density in the homography space and verify the correctness of correspondences. The proposed approach is comprehensively compared with the state-of-the-art methods and the promising results manifest its effectiveness.
DeepCD: Learning Deep Complementary Descriptors for Patch Representations

This paper presents the DeepCD framework which learns a pair of complementary descriptors jointly for image patch representation by employing deep learning techniques. It can be achieved by taking any descriptor learning architecture for learning a leading descriptor and augmenting the architecture with an additional network stream for learning a complementary descriptor. To enforce the complementary property, a new network layer, called data-dependent modulation (DDM) layer, is introduced for adaptively learning the augmented network stream with the emphasis on the training data that are not well handled by the leading stream. By optimizing the proposed joint loss function with late fusion, the obtained descriptors are complementary to each other and their fusion improves performance. Experiments on several problems and datasets show that the proposed method1 is simple yet effective, outperforming state-of-the-art methods.
Robust face detection with multi-class boosting

With the aim to design a general learning framework for detecting faces of various poses or under different lighting conditions, we are motivated to formulate the task as a classification problem over data of multiple classes. Specifically, our approach focuses on a new multi-class boosting algorithm, called MBHboost, and its integration with a cascade structure for effectively performing face detection. There are three main advantages of using MBHboost: 1) each MBH weak learner is derived by sharing a good projection direction such that each class of data has its own decision boundary; 2) the proposed boosting algorithm is established based on an optimal criterion for multi-class classification; and 3) since MBHboost is flexible with respect to the number of classes, it turns out that it is possible to use only one single boosted cascade for the multi-class detection. All these properties give rise to a robust system to detect faces efficiently and accurately.
Video Saliency Map Detection by Dominant Camera Motion Removal

We present a trajectory-based approach to detect salient regions in videos by dominant camera motion removal. Our approach is designed in a general way so that it can be applied to videos taken by either stationary or moving cameras without any prior information. Moreover, multiple salient regions of different temporal lengths can also be detected. To this end, we extract a set of spatially and temporally coherent trajectories of keypoints in a video. Then, velocity and acceleration entropies are proposed to represent the trajectories. In this way, long-term object motions are exploited to filter out short-term noises, and object motions of various temporal lengths can be represented in the same way. On the other hand, we are inspired by the observation that the trajectories in backgrounds, i.e., the nonsalient trajectories, are usually consistent with the dominant camera motion no matter whether the camera is stationary or not. We make use of this property to develop a unified approach to saliency generation for both stationary and moving cameras. Specifically, one-class SVM is employed to remove the consistent trajectories in motion. It follows that the salient regions could be highlighted by applying a diffusion process to the remaining trajectories. In addition, we create a set of manually annotated ground truth on the collected videos. The annotated videos are then used for performance evaluation and comparison. The promising results on various types of videos demonstrate the effectiveness and great applicability of our approach.
Deep Co-occurrence Feature Learning for Visual Object Recognition

This paper addresses three issues in integrating part-based representations into convolutional neural networks (CNNs) for object recognition. First, most part-based models rely on a few pre-specified object parts. However, the optimal object parts for recognition often vary from category to category. Second, acquiring training data with part-level annotation is labor-intensive. Third, modeling spatial relationships between parts in CNNs often involves an exhaustive search of part templates over multiple network streams. We tackle the three issues by introducing a new network layer, called co-occurrence layer. It can extend a convolutional layer to encode the co-occurrence between the visual parts detected by the numerous neurons, instead of a few pre-specified parts. To this end, the feature maps serve as both filters and images, and mutual correlation filtering is conducted between them. The co-occurrence layer is end-to-end trainable. The resultant co-occurrence features are rotation-and translation-invariant, and are robust to object deformation. By applying this new layer to the VGG-16 and ResNet-152, we achieve the recognition rates of 83.6% and 85.8% on the Caltech-UCSD bird benchmark, respectively. The source code is available at https://github.com/yafangshih/Deep-COOC.