nils-herrmann/clean_scirepeval huggingface dataset:
A Structured Learning-Based Graph Matching Method for Tracking Dynamic Multiple Objects

Detecting multiple targets and obtaining a record of trajectories of identical targets that interact mutually infer countless applications in a large number of fields. However, it presents a significant challenge to the technology of object tracking. This paper describes a novel structured learning-based graph matching approach to track a variable number of interacting objects in complicated environments. Different from previous approaches, the proposed method takes full advantage of neighboring relationships as the edge feature in a structured graph, which performs better than using the node feature only. Therefore, a structured graph matching model is established, and the problem is regarded as structured node and edge matching between graphs generated from successive frames. In essence, it is formulated as the maximum weighted bipartite matching problem to be solved using the dynamic Hungarian algorithm, which is applicable to optimally solving the assignment problem in situations with changing edge costs or weights. In the proposed graph matching model, the parameters of the structured graph matching model are determined in a stochastic learning process. In order to improve the tracking performance, bilateral tracking is also used. Finally, extensive experimental results on Dynamic Cell, Football, and Car sequences demonstrate that the new approach effectively deals with complicated target interactions.
Structured Sparse Representation With Union of Data-Driven Linear and Multilinear Subspaces Model for Compressive Video Sampling

The standard compressive sensing (CS) theory can be improved for robust recovery with fewer measurements under the assumption that signals lie on a union of subspaces (UoS). However, the UoS model is restricted to specific types of signal regularities with predetermined topology for subspaces. This paper proposes a generalized model which adaptively decomposes signals into a union of data-driven subspaces (UoDS) for structured sparse representation. The proposed UoDS model leverages subspace clustering to derive the optimal structures and bases for the subspaces conditioned on the sample signals. For multidimensional signals with various statistics, it supports linear and multilinear subspace learning for compressive sampling. As an improvement for generic CS model, the basis which represents the sparsity of sample signals is adaptively generated via linear subspace learning method. Furthermore, a generalized model with multilinear subspace learning is considered for CS to avoid vectorization of high-dimensional signals. In comparison to UoS, the UoDS model requires fewer degrees of freedom for a desirable recovery quality. Experimental results demonstrate that the proposed model for video sampling is promising and applicable.
Generic video coding with abstraction and detail completion

This paper presents a generic video coding framework with the texture abstraction and completion, inspired by a strong grouping bias of local elements in Gestalt psychology. Abstracting imagery by grouping perceptual salience from anisotropic diffusion, it decomposes video images into two layers composing of semantic components and residual detail. The similarity between textures of abstraction layer is motivated to infer the restoration of missing detail, under the spatio-temporal variation regularity. Through a motion and spatial context of moton, hence, a group of pictures (GOP) is divided into key frames and abstracted frames to form the final compressed data. An abstraction refinement is tuned to improve matching of detail restoration based on bilateral filtering. The proposed approach is more generic without incurring any specific side information, and achieves up to 20% bit saving versus standard H.264 at similar visual quality levels.
Embedded Transform Coding Based Lossless Compression in Compressive Spectral Imaging with Coded Aperture

The multi-shot Coded Aperture Snapshot Spectral Imaging system (CASSI) is an imaging architecture that senses the spectral imaging information of a 3D cube using a 2D focal plane array snapshot. Modeled as the summation of coded and shifted versions of different spectral voxels, the compressive CASSI measurements are difficult to be further compressed due to little correlation and redundancy. This paper is dedicated to retaining the correlations in the original data cube by an embedded transform coding on the compressive measurements. Through statistical modeling, the transformed measurements prove to be more approximate to the distribution of original spectral images than the compressive measurements. Meanwhile, bit-plane coding for the transformed measurements is applied by taking advantage of the known code aperture. The significant performance of this embedded coder on random compressive measurements is evaluated for different number of snapshots.
Design and implementation of multiplexing rate control in broadband access network TV transmission system

This paper addresses the implementation architecture and protocol procedure of the dedicated broadband access network TV transmission system. According to application-oriented quality of service (QoS), two alternative rate control schemes for multiple DTV programs transmission are proposed collaborating with MPEG-2 system strategy and IP-base network multiplexing mechanism. As to the network bandwidth utilization, a flexible RTF-based synchronization resilience scheme for VBR transport stream (TS) transmission is realized to ensure the performance requirement for MPEG-2 standard. As to the MPEG-2 system strategy, moreover, a parallel VBR transcoding/encoding rate control algorithm for CBR TS transmission could achieve a consistent picture quality for multiple DTV programs under a constrained fixed-rate network bandwidth. For adapting the variable access network bandwidth, the system main control module can flexibly schedule the available rate control scheme for multiple slave service multiplexers. Experimental results demonstrate that the proposed schemes not only improve the traditional TV broadcast adaptation to Internet, but also achieve a more ideal program picture quality constrained by multi-program parallel multiplexing.
Bi-Directional Context Modeling with Combinatorial Structuring for Genome Sequence Compression

This paper proposes a bi-directional context modeling (BCM) technique for reference-free genome sequence compression, which constructs its contexts by combining arbitrary predicted symbols in two directions corresponding to approximate repeats and non-repeat regions. Thus, BCM can sequentially predict DNA sequences with weighted conditional probabilities that simultaneously exploit the correlations among matched approximate repeats and fit the variable-order statistics in non-repeat regions. Moreover, BCM eliminates the overhead of pointer information for specifying approximate repeats, as it is synchronized in both encoder and decoder. In theory, we show that upper bounds of excess model redundancy led by BCM vanish with the growth of sequence size. Experimental results show that BCM outperforms the state-of-the-art reference-free compressors like FCM and CTW+LZ.
Union of Data-Driven Subspaces via Subspace Clustering for Compressive Video Sampling

The standard Compressive Sensing (CS) theory indicates that robust signals recovery can be obtained from just a few collection of incoherent projections. To further decrease the necessary measurements, an alternative to the generic CS framework assumes that signals lie on a union of subspaces (UoS). However, UoS model is limited to the specific type of signal regularity. This paper considers a more general and adaptive model which presumes that signals lie on a union of data-driven subspaces (UoDS). The UoDS model inherits the merit from UoS that signals have structural sparse representation. Meanwhile, it allows to recover signals using fewer degrees of freedom for a desirable recovery quality than UoS. To construct the UoDS model, a subspace clustering method is utilized to form an adaptive group set. The corresponding adaptive basis is learned by applying a linear subspace learning (LSL) method to each group. A corresponding recovery algorithm with provable performance is also given. Experiment results demonstrate that the proposed model for video sampling is valid and applicable.
Compressive Tensor Sampling with Structured Sparsity

Conventional Compressive Sensing (CS) obscures the intrinsic structures of multidimensional signals with the vectorized representation. Although tensor-based CS methods can preserve the intrinsic multidimensional structures with reduced computational complexity, their sampling efficiency and recovery performance are degraded with the assumption of standard/simple sparsity. This paper proposes a general and adaptive model that incorporates structured sparsity into tensor representation to fit the varying nonstationary statistics of multidimensional signals. To guarantee the block sparsity, subspace clustering is adopted to adaptively generate the union of tensor subspaces with its basis of each tensor subspace learned for optimized representation. For sampled tensors, the stable recovery algorithm is developed to achieve desirable recovery performance using fewer degrees of freedom. Moreover, the proposed model inherits the merit from tensor-based CS to alleviate the computational and storage burden in sampling and recovery. Experimental results demonstrate that the proposed model can achieve better recovery performance in video sampling in comparison to the state-of-the-art tensor-based method.
Sparse spatio-temporal representation with adaptive regularized dictionaries for super-resolution based video coding

In this paper, we propose a sparse representation learning with adaptive regularized dictionaries and develop a low bit-rate video coding scheme. In a reversed-complexity manner, it select a subset of key frames to encode at original resolution, while the rest are down-sampled and super-resolution reconstructed by a sparse super-resolution estimations using key frames as training set. Since primitive patches are of low dimensionality and can be well learned from the primitive patches across different images, video frame is divided into three layers: a primitive layer, a non-primitive coarse layer, and a non-primitive smooth layer. The non-primitive layer is constructed as volumes to keep consistent along the motion trajectory, which enables sparse representations over a learned 3-D spatio-temporal dictionary. Correspondingly, the target is formulated as an optimization problem by constructing a sparse representation of low-resolution frame patches or volumes over adaptive regularized dictionaries: a set of 2-D sub dictionary pairs trained from 2-D primitive patches and a 3-D dictionary trained from non-primitive volumes. In reconstruction, the lost high-frequency information of the down-sampled frames can be synthesized from the sparse spatio-temporal representation over the adaptive regularized dictionaries. Experimental results validate the compression efficiency of the proposed scheme versus the H.264/AVC in terms of both objective and subjective comparison.
Fully Decomposable Compressive Sampling With Joint Optimization for Multidimensional Sparse Representation

Conventional compressive sampling methods cannot efficiently exploit structured sparsity for sampling multidimensional signals like video sequences. In this paper, we propose a fully decomposable compressive sampling model that adopts the Kronecker product framework to exploit the structured sparsity spanning multidimensional signals. It enables efficient sampling in a progressive fashion by retaining the block-diagonal feature of Kronecker products. A synthetic sensing matrix is developed for joint optimization over sampling signals with multiple dimensions. Instead of adjusting global Gram matrix, separable minimization of mutual coherence in multiple dimensions is jointly formulated for a stable recovery with enhanced convergence rate. Sampling rate allocation is considered to improve recovery performance based on the decomposable compressive sampling. The proposed model is employed in video acquisition for temporal sparsity along motion trajectory. Experiment results show that the proposed model can improve the recovery performance with a reduced number of necessary samples in comparison to the state-of-the-art methods.
Data-Driven Hierarchical Structure Kernel for Multiscale Part-Based Object Recognition

Detecting generic object categories in images and videos are a fundamental issue in computer vision. However, it faces the challenges from inter and intraclass diversity, as well as distortions caused by viewpoints, poses, deformations, and so on. To solve object variations, this paper constructs a structure kernel and proposes a multiscale part-based model incorporating the discriminative power of kernels. The structure kernel would measure the resemblance of part-based objects in three aspects: 1) the global similarity term to measure the resemblance of the global visual appearance of relevant objects; 2) the part similarity term to measure the resemblance of the visual appearance of distinctive parts; and 3) the spatial similarity term to measure the resemblance of the spatial layout of parts. In essence, the deformation of parts in the structure kernel is penalized in a multiscale space with respect to horizontal displacement, vertical displacement, and scale difference. Part similarities are combined with different weights, which are optimized efficiently to maximize the intraclass similarities and minimize the interclass similarities by the normalized stochastic gradient ascent algorithm. In addition, the parameters of the structure kernel are learned during the training process with regard to the distribution of the data in a more discriminative way. With flexible part sizes on scale and displacement, it can be more robust to the intraclass variations, poses, and viewpoints. Theoretical analysis and experimental evaluations demonstrate that the proposed multiscale part-based representation model with structure kernel exhibits accurate and robust performance, and outperforms state-of-the-art object classification approaches.
Webly-supervised visual concept learning with cardinality guided instance mining and clustered multitask refinement

Conventional image classification and object detection methods depend on manual annotations, such as image-level labels and bounding boxes. However, the acquisition of such annotations for millions of images is trivial. This paper addresses the problem of webly-supervised visual concept learning, and develops an automatic algorithm using parallel text and visual corpora to discover informative visual patterns from the web images. Based on the mined patterns, a cardinality-guided multiple instance learning algorithm is designed to establish the link between the image patterns and the literal concepts. Furthermore, due to the diversity of visual concepts, we perform clustered multitask refinement on the learned concept classifiers to enhance their generalization capability via a clustered regularization. Experiments demonstrate the superiority of the proposed method over traditional approaches.
Joint Dynamic Rate Control and Transmission Scheduling for Scalable Video Multirate Multicast Over Wireless Networks

In this paper we consider the time-varying characteristics of practical wireless networks and propose a joint dynamic rate allocation and transmission scheduling optimization scheme for scalable video multirate multicast based on opportunistic routing (OR) and network coding. With OR the decision of optimal routes for scalable video coding layered streaming is integrated into the joint optimization formulation. The network throughput is also increased by taking advantage of the broadcast nature of the wireless shared medium and by network coding operations in intermediate nodes. To maximize the overall video reception quality among all destinations the proposed scheme can jointly optimize the video reception rate the associated routes to different destinations and the time fraction scheduling of transmitter sets that are concurrently transmitting in the shared wireless medium. By using dual decomposition and primal-dual update approach we develop a cross-layer algorithm in a fully distributed manner. Simulation results demonstrate significant network multicast throughput improvement and adaptation to dynamic network changes relative to existing optimization schemes.
Correlated Data Gathering on Dynamic Network Coding Policy and Opportunistic Routing in Wireless Sensor Network

This paper studies the problem of correlated data gathering in wireless sensor networks. For a maximum network utility, a efficient data capture and transmission framework is proposed for correlated sources, where localized S-W source coding, network coding based flow control and opportunistic routing are jointly optimized. To increase the throughput and guarantee the decodability simultaneously, a dynamic network coding strategy is proposed, with which the intermediate node can easily decide whether to make a combination among the incoming flows. Also, an opportunistic routing approach is presented, which adopts a new metric (minimum congestion price) for forwarding node selection and results in a maximum utility benefit for the sensor nodes. Through the Lagrange dual and gradient approach, a fully distributed algorithm is represented. And the convergence and performance are validated by the numerical results.
Multiscale Edge Coding and Adaptive Lifting for Depth Maps Coding in 3-D Video

A depth image represents three-dimensional scene information and is commonly used for depth image based rendering (DIBR) to support 3-D video applications. Reconstruction errors around depth edges lead to image blurring in synthesized views. This paper proposes a new coding method to improve the depth edge preserving effect. It makes use of both edge regularity and grayscale regularity to model the depth map, where the edge regularity is modeled by multiscale beamlet representation and the grayscale regularity is modeled by edge adaptive wavelet transform.
A hybrid wavelet convolution network with sparse-coding for image super-resolution

This paper proposes a hybrid wavelet convolution network (HWCN) which is composed of a scattering convolution component and a convolution neural component. The hierarchical end-to-end network implements sparse-coding and high-dimensional reconstruction for inverse problem through cascade convolutions. With the pre-defined scattering convolutions from nonlinear operators, the network can be tailored in accordance with the frequency property to provide sparse code candidates, and the convolution neural component could automatically select and weight these candidates for sparse coding. Given a tiny dataset, HWCN could train complex deep network with better generalization by regularization from scattering convolutions, and thereby is a competitive alternative to convolutional neural networks (CNN). Moreover, we further demonstrate that HWCN is a superior selection of sparse-coding based image super-resolution and achieves state-of-the-art performance.
Rate control for real-time video network transmission on end-to-end rate-distortion and application-oriented QoS

In the merged multimedia packet-switched networks, the limited or no end-to-end Quality of Service (QoS) guarantees induce that rate control has been evolved to joint source-channel adjustment architecture based on application-oriented QoS. Based on the statistical analysis under the spatial intra and temporal inter prediction subject to a universal spatial-temporal coding framework and a general error concealment by the decoder, an end-to-end distortion estimation model is proposed. On the basis of the analytic model, this paper fulfills a picture quality parameters selection solution with rate-distortion (R-D) Lagrange optimization for a general coding engine including H.264/AVC, exploiting either the source-driven iterative prediction or feedback recursion. Further, a corresponding joint source-channel rate control strategy is proposed. For the real-time variable bit-rate (VBR) video transmission under a given time-varying network condition, the strategy could estimate an instantaneous available transmission rate on traffic smoothing and codec's buffer control, adopt the proposed an end-to-end distortion regressive model and a global optimal error control parameters selection, and address the consistent bit allocation in group of picture (GOP) level, picture level, and MB level. The extensive network simulation experiments show a better and more consistent end-to-end picture quality, in contrast with the locally optimal control strategy at MB level.
Adaptive Submodular Dictionary Selection for Sparse Representation Modeling with Application to Image Super-Resolution

This paper proposes an adaptive dictionary learning approach based on sub modular optimization. A candidate atom set is constructed based on multiple bases from the combination of analytic and trained dictionaries. With the low-frequency components by the analytic DCT atoms, high-resolution dictionaries can be inferred through online learning to make efficient approximation with rapid convergence. It is formulated as a combinatorial optimization for approximate sub modularity, which is suitable for sparse representation based on dictionaries with arbitrary structures. In single-image super-resolution, the proposed scheme has been demonstrated to improve the reconstruction performance in comparison with double sparsity dictionary in terms of both objective and subjective restoration quality.
A scalable depth coding with arc breakpoints based synthesis in 3-D video

Depth map represents three-dimensional information and is used for depth image-based rendering to support 3-D video applications. Conventional video coding standards may cause serious coding artifacts along the depth discontinuities, which ultimately affect the synthesized view quality. This paper proposes an efficient technique to compress the depth map by encoding a reduced resolution of depth map and using special upsampling method to reconstruct the original depth map. Unlike previous upsampling schemes with the guidance of corresponding color video, the proposed method upsamples the depth map using the breakpoints which are generated at the decoder side with only a few breakpoints. Instead of the edge representation of the depth map, the breakpoints would substantially take less bits and low errors with the regularity of the contour. Experimental results show that the proposed technique significantly reduces bit rate while achieving a better quality of the synthesized view in terms of subjective and objective measures.
Complementary Contextual Models with FM-Index for DNA Compression

Demanding for efficient compression and storage of DNA sequences has been rising with the rapid growth of DNA sequencing technologies. Existing reference-based algorithms map all patterns to regions found in the reference sequence, which lead to redundancy of incomplete similarity. This paper proposes an efficient reference-based method for DNA sequence compression that integrates FM-index and complementary context models to improve compression performance. The proposed method introduces FM-index to represent the full-text matching for exact repeats between the target and reference sequences. For unmatched symbols, complementary context models are leveraged to make weighted estimation conditioned on variable-order contexts. Reversed reference index is used to guarantee the longest match of variable-length substrings. Experimental results show that the proposed method can achieve a 213-fold compression ratio when tested on the first Korean personal genome sequence data set.