nils-herrmann/clean_scirepeval huggingface dataset:
Personalized Age Progression with Bi-Level Aging Dictionary Learning

Age progression is defined as aesthetically re-rendering the aging face at any future age for an individual face. In this work, we aim to automatically render aging faces in a personalized way. Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e.g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each person may have extra personalized facial characteristics, e.g., mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular person, yet much easier and more practical to get face pairs from neighboring age groups. To this end, we propose a novel Bi-level Dictionary Learning based Personalized Age Progression (BDL-PAP) method. Here, bi-level dictionary learning is formulated to learn the aging dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of the proposed BDL-PAP over other state-of-the-arts in term of personalized age progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.
Personalized Age Progression with Aging Dictionary

In this paper, we aim to automatically render aging faces in a personalized way. Basically, a set of age-group specific dictionaries are learned, where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups, and a linear combination of these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each subject may have extra personalized facial characteristics, e.g. mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular subject, yet much easier and more practical to get face pairs from neighboring age groups. Thus a personality-aware coupled reconstruction loss is utilized to learn the dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of our proposed solution over other state-of-the-arts in term of personalized aging progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.
Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm.

The nuclear norm is widely used as a convex surrogate of the rank function in compressive sensing for low rank matrix recovery with its applications in image recovery and signal processing. However, solving the nuclear norm based relaxed convex problem usually leads to a suboptimal solution of the original rank minimization problem. In this paper, we propose to use a family of nonconvex surrogates of L0-norm on the singular values of a matrix to approximate the rank function. This leads to a nonconvex nonsmooth minimization problem. Then we propose to solve the problem by Iteratively Reweighted Nuclear Norm (IRNN) algorithm. IRNN iteratively solves a Weighted Singular Value Thresholding (WSVT) problem, which has a closed form solution due to the special properties of the nonconvex surrogate functions. We also extend IRNN to solve the nonconvex problem with two or more blocks of variables. In theory, we prove that IRNN decreases the objective function value monotonically, and any limit point is a stationary point. Extensive experiments on both synthesized data and real images demonstrate that IRNN enhances the low rank matrix recovery compared with state-ofthe- art convex algorithms.
Weakly Supervised Multimodal Hashing for Scalable Social Image Retrieval

Recent years have witnessed a dramatic increase in the number of community-contributed images. Hashing-based similarity searches for social images have been attracting considerable interest from computer vision and multimedia communities due to their computational and memory efficiency. In this paper, we propose a novel weakly supervised hashing method named weakly supervised multimodal hashing, for scalable social image retrieval. Semantic-aware hash functions are learned by jointly leveraging the weakly supervised tag information and visual information. Specifically, because user-provided tags associated with social images can describe the semantic information, the hash functions are learned by exploring the semantic structure. Unfortunately, the user-provided tags are imperfect. To avoid overfitting the weakly supervised tags, the local discriminative structure and the geometric structure in the visual space are explored. Besides, to learn compact and non-redundant hash codes, the hash functions are constrained to be orthogonal and an information theoretic regularization based on the maximum entropy principle is introduced to maximize the information provided by each hash code. The learned hash functions are orthogonal, which can avoid redundancy in the learned hash codes as much as possible. The proposed hashing learning problem is formulated as the eigenvalue problem, which can be solved efficiently. Extensive experiments are conducted on two widely used social image data sets and the encouraging performance compared with the state-of-the-art hashing techniques demonstrates the effectiveness of the proposed method.
Improving Bottom-up Saliency Detection by Looking into Neighbors

Bottom-up saliency detection aims to detect salient areas within natural images usually without learning from labeled images. Typically, the saliency map of an image is inferred by only using the information within this image (referred to as the “current image”). While efficient, such single-image-based methods may fail to obtain reliable results, because the information within a single image may be insufficient for defining saliency. In this paper, we investigate how saliency detection can benefit from the nearest neighbor structure in the image space. First, we show that existing methods can be improved by extending them to include the visual neighborhood information. This verifies the significance of the neighbors. Next, a solution of multitask sparsity pursuit is proposed to integrate the current image and its neighbors to collaboratively detect saliency. The integration is done by first representing each image as a feature matrix, and then seeking the consistently sparse elements from the joint decompositions of multiple matrices into pairs of low-rank and sparse matrices. The computational procedure is formulated as a constrained nuclear norm and ℓ2,1-norm minimization problem, which is convex and can be solved efficiently with the augmented Lagrange multiplier method. Besides the nearest neighbor structure in the visual feature space, the proposed model can also be generalized to handle multiple visual features. Extensive experiments have clearly validated its superiority over other state-of-the-art methods.
Beyond Accuracy: Typicality Ranking for Video Annotation

In this paper, we address the issue of typicality ranking for video annotation and propose to use a novel criterion, average typicality precision (ATP), to replace the frequently used one, average precision (AP), for evaluating the performance of video annotation algorithms. General annotation methods just care the number of true-positive samples at the top of the ranked list; they actually do not care the order of these samples. We argue that it is more reasonable to rank "typical" true-positive samples higher than non-typical ones, which can be evaluated by our proposed ATP. However, generally the labels of the training data only differentiate true from false; that is to say, typical or non-typical training samples have the same contribution to the learning process. Therefore, the labels of the unlabeled data learned from these training data can not well measure the typicality. In this paper, we relax the labels of the training data to real-valued typicality scores by a pre-processing stage, which is accomplished by three approaches, including density estimation, user feedback and active learning. Then the typicality scores of the training data are propagated to unlabeled data using manifold-ranking. Experiments conducted on the TRECVID data set demonstrate that this typicality ranking scheme is more consistent with human perception than normal accuracy based ranking schemes.
Multi-objective optimization design for active damping LCL grid-connected

LCL filters, as the interface between the grid-connected inverters and the grid, overcome some of drawbacks of L or LC filters, especially have great performance used in medium and high power range situation. Methods of introducing passive damping and active damping are used to solving the stability problem around the resonance frequency. This paper purposed an active damping method based on introducing capacitor current feedback which can effectively control current resonance without changing the resonance frequency of LCL filter and avoid increasing wastage and reducing system efficiency. On the basis of above research, synthetically considering current attenuation ratio, transfer function amplitude and cost of the filter, the multi-objective optimization model of active damping LCL filter and optimization algorithm based on NSGA-Ċ are presented. Effectiveness of the optimization method is verified by simulations on PSCAD.
Semi-Supervised Image-to-Video Adaptation for Video Action Recognition

Human action recognition has been well explored in applications of computer vision. Many successful action recognition methods have shown that action knowledge can be effectively learned from motion videos or still images. For the same action, the appropriate action knowledge learned from different types of media, e.g., videos or images, may be related. However, less effort has been made to improve the performance of action recognition in videos by adapting the action knowledge conveyed from images to videos. Most of the existing video action recognition methods suffer from the problem of lacking sufficient labeled training videos. In such cases, over-fitting would be a potential problem and the performance of action recognition is restrained. In this paper, we propose an adaptation method to enhance action recognition in videos by adapting knowledge from images. The adapted knowledge is utilized to learn the correlated action semantics by exploring the common components of both labeled videos and images. Meanwhile, we extend the adaptation method to a semi-supervised framework which can leverage both labeled and unlabeled videos. Thus, the over-fitting can be alleviated and the performance of action recognition is improved. Experiments on public benchmark datasets and real-world datasets show that our method outperforms several other state-of-the-art action recognition methods.
CAD: Scale Invariant Framework for Real-Time Object Detection

Real-time detection frameworks that typically utilize end-to-end networks to scan the entire vision range, have shown potential effectiveness in object detection. However, compared to more accurate but time-consuming frameworks, detection accuracy of existing real-time networks are still left far behind. Towards this end, this work proposes a novel CAD framework to improve detection accuracy while preserving the real-time speed. Moreover, to enhance the generalization ability of the proposed framework, we introduce maxout  to approximate the correlation between image pixels and network predictions. In addition, the non-maximum weighted (NMW)  is employed to eliminate the redundant bounding boxes that are considered as repetitive detections for the same objects. Extensive experiments are conducted on two detection benchmarks to demonstrate that the proposed framework achieves state-of-the-art performance.
A new class of B/W and color halftoning algorithms

A new class of dithering algorithms for black and white (B/W) and color images is presented. The basic idea behind the technique is to divide the image into small blocks and minimize the distortion between the original continuous tone image and its low-pass filtered halftone. This corresponds to a quadratic programming problem with linear constraints which is solved via the branch and bound algorithm. Examples of B/W and color dither images using the technique are shown and compared to halftones obtained via existing dithering algorithms.<<ETX>>
Matching pursuits multiple description coding for wireless video

Multiple description coding (MDC) is an error resilient source coding scheme that creates multiple bitstreams of approximately equal importance. We develop a 2 description video coding scheme based on the 3 loop structure originally studied in Reibman et al. (1999). We modify the discrete cosine transform structure to the matching pursuits framework and evaluate performance gain using maximum likelihood (ML) enhancement when both descriptions are available. We find that ML enhancement works best for low motion sequences. Performance comparison is made between our MDC scheme and single description coding (SDC) schemes over two-state Markov channels and Rayleigh fading channels. We find that MDC outperforms SDC in bursty slowly varying environments. In the case of Rayleigh fading channels, interleaving helps SDC close the gap and even outperform MDC depending on the amount of interleaving performed, at the expense of additional delay.
Sharp geometry reconstruction of building facades using range data

In this paper we describe a method for detailed geometry reconstruction of building façades in an urban environment, given a 3D point-cloud of LiDAR range data. Our approach separates planar faces and interpolates their shape with Moving Least-Squares (MLS) sampling. A method is then proposed to reconstruct occluded areas of the building whereby gaps in the building surface are modeled with axis-aligned planes fit to the gap boundary vertices. This approach reconstructs unsampled areas of building surfaces under the assumption that buildings have 3D rectilinear, axis-aligned features. We demonstrate the effectiveness of our approach on a number of building façades.
New properties of sigma-delta modulators with DC inputs

New properties of single- and double-loop sigma-delta modulators with constant inputs are derived by exploiting the inherent structure os the output sequences or codewords that the modulators are capable of producing. Upper bounds are derived on the number of N-bit codewords for the single- and double-loop modulators. Analytical lower bounds on the mean squared error (MSE) obtainable by any decoder, linear or nonlinear, in approximating the constant input are also derived. Optimal nonlinear decoders for constant inputs based on a table lookup approach which operates directly on the nonuniform quantization intervals are considered. Using simulations is is found that the optimal nonlinear decoders perform better than linear decoders, by about 3 and 20 dB for the single- and double-loop modulators, respectively. A cascade structure specifically for constant inputs is introduced, and its corresponding decoding algorithm is derived. It is shown that for a fixed latency, the MSE performance of the cascade structure is 12 dB superior, and its throughput is twice that of the conventional two-stage MASH modulator.
Effective bandwidth based scheduling for streaming multimedia

We propose a class of packet scheduling algorithms for streaming media. The importance level of a video packet is determined by its relative position within its group of pictures, taking into account the motion-texture discrimination and temporal scalability. We generate a number of nested substreams, with more important streams embedding less important ones in a progressive manner. We model the streaming system as a queueing system, compute the run-time decoding failure probability of a frame in each substream based on effective bandwidth, and determine the optimum substream to be sent at any moment in time. The data within optimum substream is sent based on earliest-deadline-first scheduling, until the next channel report arrives, at which time the optimum substream is recomputed. From experiments with real video data, we show that our proposed scheduling scheme outperforms the conventional sequential sending scheme.
Orientation adaptive subband coding of images

In the subband coding of images, directionality of image features has thus far been exploited very little. The proposed subband coding scheme utilizes orientation of local image features to avoid the highly objectionable Gibbs-like phenomena observed at reconstructed image edges with conventional subband schemes at low bit rates, At comparable bit rates, the subjective image quality obtained by our orientation adaptive scheme is considerably enhanced over a conventional separable subband coding scheme, as well as other separable approaches such as the JPEG compression standard.
A new nonuniform frequency sampling technique for multidimensional FIR filter design

A nonuniform frequency sampling method for designing multidimensional finite impulse response (FIR) filters has been derived. The derivation makes use of the fact that a multidimensional polynomial is uniquely reconstructible from its samples on irreducible curves, provided there are enough samples on each curve. The technique entails choosing an appropriate number of samples of the desired frequency response on an appropriate number of straight lines with different rational slopes in such a way that the resulting filter design problem has a unique solution. The major advantage of the technique over a window and transformation methods is its simplicity, and its advantage over existing frequency sampling methods is its increased flexibility. Efficient algorithms for determining the filter coefficients have been developed.<<ETX>>
Reconstruction of oversampled band-limited signals from Sigma Delta encoded binary sequences

The authors consider the application of Sigma Delta modulators to analog-to-digital conversion. They have previously shown that for constant input signals, optimal nonlinear decoding can achieve large gains in signal-to-noise ratio (SNR) over linear decoding. A similar result is shown for band-limited input signals. The new nonlinear decoding algorithm is based on projections onto convex sets (POCS) and alternates between a band limitation and a time-domain operation to find a signal invariant under both. The band limitations can be based on singular value decomposition of a certain matrix. The time-domain operation results in a quadratic programming problem. Simulation results are shown for the SNR performance of a POCS-based decoder and a linear decoder for the single-loop Sigma Delta modulator and for a specific fourth-order interpolative modulator. Improvements in SNR of up to 20 dB can be achieved for the single-loop modulator depending on the oversampling ratio, and up to 10 dB for the fourth-order modulator.<<ETX>>
Automated texture mapping of 3D city models with oblique aerial imagery

This work describes an approach to texture mapping a 3D city model obtained from aerial and ground-based laser scans with oblique aerial imagery. First, the images are automatically registered by matching 2D image lines with projections of 3D lines from the city model. Then, for each triangle in the model, the optimal image is selected by taking into account occlusion, image resolution, surface normal orientation, and coherence with neighboring triangles. Finally, the utilized texture patches from all images are combined into one texture atlas for compact representation and efficient rendering. We evaluate our approach on a data set of downtown Berkeley.
User-Driven Geolocation of Untagged Desert Imagery Using Digital Elevation Models

We propose a system for user-aided visual localization of desert imagery without the use of any metadata such as GPS readings, camera focal length, or field-of-view. The system makes use only of publicly available digital elevation models (DEMs) to rapidly and accurately locate photographs in non-urban environments such as deserts. Our system generates synthetic skyline views from a DEM and extracts stable concavity-based features from these skylines to form a database. To localize queries, a user manually traces the skyline on an input photograph. The skyline is automatically refined based on this estimate, and the same concavity-based features are extracted. We then apply a variety of geometrically constrained matching techniques to efficiently and accurately match the query skyline to a database skyline, thereby localizing the query image. We evaluate our system using a test set of 44 ground-truthed images over a 10, 000 km2 region of interest in a desert and show that in many cases, queries can be localized with precision as fine as 100 m2.
A lower bound on the MSE of the single loop sigma delta modulator

W e derive a lower bound far the Mean Square Error (MSE) of singie loop Sigma Delta modulators. This is done by exploiting the inherent structure of the codewords the modulator is capable of producing as a S O U ~ E ~ coder. Spedfically, we find a unique ordering of the codewords in order to derive an upper bound on the size of the collection of the codewords. This upper bound is then used to find a lower bound for MSE which is shown to be independent of the decoder configuration, and inversely proportionalto M', where M is the oversampling ratio. Numerical simulations are used to show that the actual number of codewords the modulator is capable of producing is proportional to M2. It is also shown that the sigma delta modulator can be thought of as a nonuniform quantizer in which the ratio between largest and smallest quantization intervals is approximately equal to the oversampling ratio. Numerical values of the quantization intervals are used to show that the MSE of a nonlinear decoder based on "lwk up" table approach is inversely proportional to M3.
Cooperative inchworm localization with a low cost team

In this paper we address the problem of multi-robot localization with a heterogeneous team of low-cost mobile robots. The team consists of a single centralized observer with an inertial measurement unit (IMU) and monocular camera, and multiple picket robots with only IMUs and Red Green Blue (RGB) light emitting diodes (LED). This team cooperatively navigates a visually featureless environment while localizing all robots. A combination of camera imagery captured by the observer and IMU measurements from the pickets and observer are fused to estimate motion of the team. A team movement strategy, referred to as inchworm, is formulated as follows: Pickets move ahead of the observer and then act as temporary landmarks for the observer to follow. This cooperative approach employs a single Extended Kalman Filter (EKF) to localize the entire heterogeneous multi-robot team, using a formulation of the measurement Jacobian to relate the pose of the observer to the poses of the pickets with respect to the global reference frame. An initial experiment with the inchworm strategy has shown localization within 0.14 m position error and 2.18° orientation error over a path-length of 5 meters in an environment with irregular ground, partial occlusions, and a ramp. This demonstrates improvement over a camera-only localization technique that was adapted to our team dynamic which produced 0.18m position error and 3.12° orientation error over the same dataset. In addition, we demonstrate improvement in localization accuracy with an increasing number of picket robots.