nils-herrmann/clean_scirepeval huggingface dataset:
Connecting the dots without clues: Unsupervised domain adaptation for cross-domain visual classification

Many real-world visual classification tasks require one to recognize test data in a particular domain of interest, while the training data can only be collected from a different domain. This can be viewed as the problem of unsupervised domain adaptation, in which the domain difference and the lack of cross-domain label/correspondence information make the recognition task very difficult. In this paper, we propose to exploit the cross-domain data correspondence using both observed data similarity and labels transferred from the source domain. This allows us to perform distribution matching for cross-domain data with recognition guarantees. Our experiments on three different cross-domain visual classification tasks would confirm the effectiveness of our method, which is shown to perform favorably against state-of-the-art unsupervised domain adaptation approaches.
Anomaly Detection via Online Oversampling Principal Component Analysis

Anomaly detection has been an important research topic in data mining and machine learning. Many real-world applications such as intrusion or credit card fraud detection require an effective and efficient framework to identify deviated data instances. However, most anomaly detection methods are typically implemented in batch mode, and thus cannot be easily extended to large-scale problems without sacrificing computation and memory requirements. In this paper, we propose an online oversampling principal component analysis (osPCA) algorithm to address this problem, and we aim at detecting the presence of outliers from a large amount of data via an online updating technique. Unlike prior principal component analysis (PCA)-based approaches, we do not store the entire data matrix or covariance matrix, and thus our approach is especially of interest in online or large-scale problems. By oversampling the target instance and extracting the principal direction of the data, the proposed osPCA allows us to determine the anomaly of the target instance according to the variation of the resulting dominant eigenvector. Since our osPCA need not perform eigen analysis explicitly, the proposed framework is favored for online applications which have computation or memory limitations. Compared with the well-known power method for PCA and other popular anomaly detection algorithms, our experimental results verify the feasibility of our proposed method in terms of both accuracy and efficiency.
Background Recovery in Railroad Crossing Videos via Incremental Low-Rank Matrix Decomposition

Inspired by the recent success of low-rank matrix recovery, we propose a novel incremental learning algorithm based on low-rank matrix decomposition. Our proposed algorithm can be applied for solving background removal problems from static yet time-varying scenes. And, in this paper, we particularly consider background modeling for railroad crossing videos. The success of an adaptive background modeling/removal approach like ours will allow users to automatically perform foreground (or intruder) detection on such scenes, which would prevent possible vehicle-train collisions and thus significantly reduce the fatality or injury rates. The challenges of background modeling in railroad crossing videos not only involve environmental variations like lighting or weather changes, headlight reflection on rails caused by nearby vehicles and foreground objects with very different velocities (e.g., vehicle, bikes, or pedestrian) also make background removal of such real-world scenes extremely difficult. We will verify that our proposed algorithm exhibits sufficient effectiveness and robustness in solving this problem. Our experiments on real-world video data would confirm that, while our approach outperforms baseline or state-of-the-art background modeling methods, our computation cost is significantly lower than that of standard low-rank based algorithm.
Edge-Preserving Depth Map Upsampling by Joint Trilateral Filter

Compared to the color images, their associated depth images captured by the RGB-D sensors are typically with lower resolution. The task of depth map super-resolution (SR) aims at increasing the resolution of the range data by utilizing the high-resolution (HR) color image, while the details of the depth information are to be properly preserved. In this paper, we present a joint trilateral filtering (JTF) algorithm for depth image SR. The proposed JTF first observes context information from the HR color image. In addition to the extracted spatial and range information of local pixels, our JTF further integrates local gradient information of the depth image, which allows the prediction and refinement of HR depth image outputs without artifacts like textural copies or edge discontinuities. Quantitative and qualitative experimental results demonstrate the effectiveness and robustness of our approach over prior depth map upsampling works.
Occlusion-aware face inpainting via generative adversarial networks

Face inpainting aims to restore the corrupted regions of face images due to extreme lighting variations, occlusion, or even disguise. This task becomes especially challenging, when the face images are taken in an unconstrained environment (i.e., with pose, illumination, and expression variations) and the type of corruption is not known in advance. In this paper, we propose a deep-learning based approach of occlusion-aware generative adversarial networks (GAN) for solving this problem. By utilizing GAN pre-trained on occlusion-free images, we are able to detect corrupted image regions automatically with the associated image pixels properly recovered. We produce promising performances on images from the benchmark dataset of LFW, and show that recognition of such face images would be benefited from our proposed approach.
Learning guided convolutional neural networks for cross-resolution face recognition

Cross-resolution face recognition tackles the problem of matching face images with different resolutions. Although state-of-the-art convolutional neural network (CNN) based methods have reported promising performances on standard face recognition problems, such models cannot sufficiently describe images with resolution different from those seen during training, and thus cannot solve the above task accordingly. In this paper, we propose Guided Convolutional Neural Network (Guided-CNN), which is a novel CNN architecture with parallel sub-CNN models as guide and learners. Unique loss functions are introduced, which would serve as joint supervision for images within and across resolutions. Our experiments not only verify the use of our model for cross-resolution recognition, but also its applicability of recognizing face images with different degrees of occlusion.
A discriminative domain adaptation model for cross-domain image classification

Techniques of domain adaptation have been applied to address cross-domain recognition problems. In particular, such techniques favor the scenarios in which labeled data can be obtained at the source domain, but only few labeled target domain data are available during the training stage. In this paper, we propose a domain adaptation approach which is able to transfer source domain labeled data to the target domain, so that one can collect a sufficient amount of training data at that domain for recognition purposes. By advancing low-rank matrix decomposition for obtaining representative cross-domain data, our proposed model aims at transferring source domain labeled data to the target domain while preserving class label information. This introduces additional discriminating ability into our model, and thus improved recognition can be expected. Empirical results on cross-domain image datasets confirm the use of our proposed model for solving cross-domain recognition problems.
Undersampled face recognition with one-pass dictionary learning

Undersampled face recognition deals with the problem in which, for each subject to be recognized, only one or few images are available in the gallery (training) set. Thus, it is very difficult to handle large intra-class variations for face images. In this paper, we propose a one-pass dictionary learning algorithm to derive an auxiliary dictionary from external data, which consists of image variants of the subjects not of interest (not to be recognized). The proposed algorithm not only allows us to efficiently model intra-class variations such as illumination and expression changes, it also exhibits excellent abilities in recognizing corrupted images due to occlusion. In our experiments, we will show that our method would perform favorably against existing sparse representation or dictionary learning based approaches. Moreover, our computation time is remarkably less than that of recent dictionary learning based face recognition methods. Therefore, the effectiveness and efficiency of our proposed algorithm can be successfully verified.
A support vector hierarchical method for multi-class classification and rejection

We address both recognition of true classes and rejection of unseen false classes inputs, as occurs in many realistic pattern recognition problems. we advance a hierarchical binary-decision classifier and produce analog outputs at each node, with yields a new soft-decision hierarchical is designed by our new support vector clustering method, which selects the classes to be separated at each node in the hierarchy. Use of our SVRDM (support vector representation and discrimination machine) classifiers at each node provides generalization and rejection ability. The soft-decision SVRDM output allows use of the confidence score for each class at each node; this is shown to improve classification (for true classes) and rejection (for false classes) performance. New aspects of this paper are that we provide remarks on our hierarchical design method, including our hierarchical clustering rule, and discuss the meaning and the use of probabilities in our soft-decision hierarchical SVRDM classifiers. We also provide initial tests results on a new database (COIL) that allows large class problem to be addressed. No prior work considered rejection of false classes on this database.
A sparse-coding based approach to clothing image retrieval

In this paper, we present a sparse-coding based clothing image retrieval method. Our proposed method utilizes multiple types of low and high-level features such as clothing type, color, and appearance to describe an input clothing image. Based on the recent success of sparse representation, we advance a locality-sensitive sparse coding framework on the derived features for retrieving relevant instances from a clothing image collection. Compared with prior image retrieval or recommendation methods which either aimed at determining a proper similarity measure or required the knowledge or preference of prior users, our sparse-coding based approach is able to identify the most similar data instances based on its content information. From our experimental results on a real-world commercial clothing image dataset, we not only verify the effectiveness of our proposed framework, we also confirm that our approach outperforms baseline and state-of-the-art clothing image retrieval methods.
Coupled Dictionary and Feature Space Learning with Applications to Cross-Domain Image Synthesis and Recognition

Cross-domain image synthesis and recognition are typically considered as two distinct tasks in the areas of computer vision and pattern recognition. Therefore, it is not clear whether approaches addressing one task can be easily generalized or extended for solving the other. In this paper, we propose a unified model for coupled dictionary and feature space learning. The proposed learning model not only observes a common feature space for associating cross-domain image data for recognition purposes, the derived feature space is able to jointly update the dictionaries in each image domain for improved representation. This is why our method can be applied to both cross-domain image synthesis and recognition problems. Experiments on a variety of synthesis and recognition tasks such as single image super-resolution, cross-view action recognition, and sketch-to-photo face recognition would verify the effectiveness of our proposed learning model.
Domain-Adaptive generative adversarial networks for sketch-to-photo inversion

Generating photo-realistic images from multiple style sketches is one of challenging tasks in image synthesis with important applications such as facial composite for suspects. While machine learning techniques have been applied for solving this problem, the requirement of collecting sketch and face photo image pairs would limit the use of the learned model for rendering sketches of different styles. In this paper, we propose a novel deep learning model of Domain-adaptive Generative Adversarial Networks (DA-GAN). The design of DA-GAN performs cross-style sketch-to-photo inversion, which mitigates the difference across input sketch styles without the need to collect a large number of sketch and face image pairs for training purposes. In experiments, we show that our method is able to produce satisfactory results as well as performing favorably against state-of-the-art approaches.
Style-centric image summarization from photographic views of a city

Visual summarization addresses the task of selecting images from an image collection, so that the sampled images would contain representative information which sufficiently highlights the collected visual data. In this paper, we solve the problem of style-centric visual summarization using photographic landmark images of a city. Different from existing works which typically retrieve landmark images based on salient visual appearances, our proposed method is able to produce different sets of summarized images, while each set corresponds to a particular image style. This is achieved by performing unsupervised clustering on images within and across landmark categories, which discovers the common photographic styles from the input image collection. Our experiments will confirm that, compared to standard clustering algorithms, our approach is able to achieve satisfactory summarization outputs with style consistency.
Learning Dense Optical-Flow Trajectory Patterns for Video Object Extraction

We proposes an unsupervised method to address videoobject extraction (VOE) in uncontrolled videos, i.e. videoscaptured by low-resolution and freely moving cameras. Weadvocate the use of dense optical-flow trajectories (DOTs),which are obtained by propagating the optical flow informationat the pixel level. Therefore, no interest point extractionis required in our framework. To integrate colorand and shape information of moving objects, we groupthe DOTs at the super-pixel level to extract co-motion regions,and use the associated pyramid histogram of orientedgradients (PHOG) descriptors to extract objects of interestacross video frames. Our approach for VOE is easy to implement,and the use of DOTs for both motion segmentationand object tracking is more robust than existing trajectorybasedmethods. Experiments on several video sequencesexhibit the feasibility of our proposed VOE framework.
Robust propagated filtering with applications to image texture filtering and beyond

Extracting meaningful structures from an image is an important task and benefits a wide range of image application tasks. However, it is typically very challenging to distinguish between noisy or textural patterns from image structures, especially when such patterns do not exhibit regularity (e.g., irregular textural patterns or those with varying scales). While existing edge-preserving image filters like bilateral, guided, or propagation filters aim at observing strong image edges, they cannot be easily applied to solve the above texture filtering tasks. In this paper, we propose robust propagated filter, which is an extension to propagation filters while exhibiting excellent ability in eliminating the aforementioned textural patterns when performing filtering. We show in our experimental results that our filter provides promising results on image filtering. Additional experiments on inverse image half toning and detail enhancement further verify the effectiveness of our proposed method.
An unsupervised domain adaptation approach for cross-domain visual classification

For cross-view action recognition and many real-world visual classification problems, one needs to recognize test data at a particular target domain of interest, while training data are collected at a different source domain. Without eliminating such domain differences, recognition of test data using classifiers trained in the source domain will not be expected to produce satisfactory performance. In this paper, we propose a novel domain adaptation approach, which is able to learn a common feature space relating cross-domain data. In particular, we not only aim at matching cross-domain data marginal distributions during adaptation, we also exploit the structure of target domain data and update class-conditional distributions accordingly. Experiments on various cross-domain visual classification tasks would verify the effectiveness and robustness of our proposed method.
RedEye: Preventing Collisions Caused by Red-Light Running Scooters With Smartphones

In this paper, we present a scooter collision avoidance system that can identify red-light runners (RLRs) at intersections. When the RLR behavior is detected, the system would advise the RLR to slow down immediately and warn nearby vehicles on the intersecting road in real time. In particular, we do not consider infrastructure-based solutions such as those utilizing a radar or a camera. This is because, in addition to high implementation costs, collisions can be only avoided at intersections where such infrastructure configurations are deployed. Instead, we advance an on-scooter solution using smartphones carried by scooter riders. Smartphones provide a useful platform that has a high penetration rate, more than sufficient computational power, inertial sensors to reflect the driving behavior, and the communication capability to transmit or receive information from other vehicles. In our system, we utilize a support vector machine and design an RLR classifier for learning and predicting RLR behaviors. The evaluation results show that our system is able to achieve over 70% recognition rates when distinguishing between RLR and non-RLR cases, as compared with approximately 80% recognition rates of the infrastructure-based (and higher cost) solution using a laser range finder (LADAR).
Unsupervised Domain Adaptation with Imbalanced Cross-Domain Data

We address a challenging unsupervised domain adaptation problem with imbalanced cross-domain data. For standard unsupervised domain adaptation, one typically obtains labeled data in the source domain and only observes unlabeled data in the target domain. However, most existing works do not consider the scenarios in which either the label numbers across domains are different, or the data in the source and/or target domains might be collected from multiple datasets. To address the aforementioned settings of imbalanced cross-domain data, we propose Closest Common Space Learning (CCSL) for associating such data with the capability of preserving label and structural information within and across domains. Experiments on multiple cross-domain visual classification tasks confirm that our method performs favorably against state-of-the-art approaches, especially when imbalanced cross-domain data are presented.
Automatic target recognition using new support vector machine

A hierarchical classifier using a new SVRDM (support vector representation and discrimination machine) is proposed for automatic target recognition. An accuracy and distance-based method is used to design a hierarchical classifier. Our SVRDM hierarchical classifier has the ability to reject unseen non-object classes and clutter inputs. Uses of both iconic and spatial frequency domain features are considered. Initial recognition and rejection test results on infrared (IR) data are excellent.
Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation

While domain adaptation (DA) aims to associate the learning tasks across data domains, heterogeneous domain adaptation (HDA) particularly deals with learning from cross-domain data which are of different types of features. In other words, for HDA, data from source and target domains are observed in separate feature spaces and thus exhibit distinct distributions. In this paper, we propose a novel learning algorithm of Cross-Domain Landmark Selection (CDLS) for solving the above task. With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation. In addition, the adaptation capabilities of such cross-domain landmarks can be determined accordingly. This is the reason why our CDLS is able to achieve promising HDA performance when comparing to state-of-the-art HDA methods. We conduct classification experiments using data across different features, domains, and modalities. The effectiveness of our proposed method can be successfully verified.
Recognition at a long distance: Very low resolution face recognition and hallucination

In real-world video surveillance applications, one often needs to recognize face images from a very long distance. Such recognition tasks are very challenging, since such images are typically with very low resolution (VLR). However, if one simply downsamples high-resolution (HR) training images for recognizing the VLR test inputs, or if one directly upsamples the VLR inputs for matching the HR training data, the resulting recognition performance would not be satisfactory. In this paper, we propose a joint face hallucination and recognition approach based on sparse representation. Given a VLR input image, our method is able to synthesize its person-specific HR version with recognition guarantees. In our experiments, we consider two different face image datasets. Empirical results will support the use of our approach for both VLR face recognition. In addition, compared to state-of-the-art super-resolution (SR) methods, we will also show that our method results in improved quality for the recovered HR face images.