nils-herrmann/clean_scirepeval huggingface dataset:
A Low Complexity Interest Point Detector

Interest point detection is a fundamental approach to feature extraction in computer vision tasks. To handle the scale invariance, interest points usually work on the scale-space representation of an image. In this letter, we propose a novel block-wise scale-space representation to significantly reduce the computational complexity of an interest point detector. Laplacian of Gaussian (LoG) filtering is applied to implement the block-wise scale-space representation. Extensive comparison experiments have shown the block-wise scale-space representation enables the efficient and effective implementation of an interest point detector in terms of memory and time complexity reduction, as well as promising performance in visual search.
Energy minimization via dynamic voltage scaling for real-time video encoding on mobile devices

This paper investigates the problem of minimizing energy consumption for real-time video encoding on mobile devices, by dynamically configuring the clock frequency in the CPU via the dynamic voltage scaling (DVS) technology. The problem can be formulated as a constrained optimization problem, whose objective is to minimize the total energy consumption of encoding video contents while respecting a real-time delay constraint. Under a probabilistic workload model, we obtain closed-form solutions for both the optimal clock frequency configuration and the resulted minimum energy. We also compare the optimal solution with a brute force flat frequency configuration. Numerical results indicate that our derived optimal solution outperforms the brute-force approach significantly. Moreover, we apply the optimal solution for real-time H.264/AVC video encoding application. Our numerical results suggest that an energy saving of 10%-20% can be achieved, compared to the flat clock frequency scheduling.
Nonlinear image restoration using recurrent radial basis function network

For nonlinear distorted images, the performance of the existing image restoration methods is limited in either visual quality or computational complexity. In this paper, we apply the recently developed technique called recurrent radial basis function network (RBFN) for nonlinear image restoration. We give the details of the construction of the recurrent RBFN network and the determination of the network parameters. Simulation results show that the proposed recurrent RBFN scheme outperforms the existing RBFN based methods in both visual quality and complexity when the degraded process is recursive.
Toward Transcoding as a Service in a Multimedia Cloud: Energy-Efficient Job-Dispatching Algorithm

In this paper, we investigate the energy-efficient job-dispatching algorithm for transcoding as a service (TaaS) in a multimedia cloud. We aim to minimize the energy consumption of service engines in the cloud while achieving low delay for TaaS. We formulate the job-dispatching problem as a constrained optimization problem under the framework of Lyapunov optimization. Using the drift-plus-penalty function, we propose an online algorithm that dispatches the transcoding jobs to service engines, with an objective to Reduce Energy consumption while achieving the QUEue STability (REQUEST). We first characterize the fundamental tradeoff between energy consumption and queue delay for the REQUEST algorithm numerically and obtain its performance bound theoretically. Second, we study the robustness of the REQUEST algorithm, with numerical results indicating that the REQUEST algorithm is robust to the inaccuracy of estimating the transcoding time. Third, we compare the performance of the REQUEST algorithm with the other two algorithms, i.e., the Round Robin and Random Rate algorithms. By simulation and real trace data, we show that by appropriately choosing the control variable, the REQUEST algorithm outperforms the Round Robin and Random Rate algorithms, with smaller time average energy consumption and time average queue length. The proposed REQUEST algorithm can be applied in cloud-assisted multimedia transcoding service.
QCCE: Quality constrained co-saliency estimation for common object detection

Despite recent advances in joint processing of images, sometimes it may not be as effective as single image processing for object discovery problems. In this paper while aiming for common object detection, we attempt to address this problem by proposing a novel QCCE: Quality Constrained Co-saliency Estimation method. The approach here is to iteratively update the saliency maps through co-saliency estimation depending upon quality scores, which indicate the degree of separation of foreground and background likelihoods (the easier the separation, the higher the quality of saliency map). In this way, joint processing is automatically constrained by the quality of saliency maps. Moreover, the proposed method can be applied to both unsupervised and supervised scenarios, unlike other methods which are particularly designed for one scenario only. Experimental results demonstrate superior performance of the proposed method compared to the state-of-the-art methods.
Admission Control Based on Rate-Variance Envelop for VBR Traffic Over IEEE 802.11e HCCA WLANs

With the increasing popularity of using wireless local area networks (WLANs) for Internet access, the controlled channel access mechanism in IEEE 802.11e WLANs, i.e., HCF controlled channel access (HCCA), has received much more attention since its inherent centralized mechanism is more efficient in handling time-bounded multimedia traffic. So far, only a few research studies address the admission control problem of variable bit rate (VBR) traffic over HCCA. These existing studies consider each traffic flow individually and, thus, cannot exploit the statistical multiplexing gain among multiple VBR traffic flows. In this paper, we apply the existing statistical multiplexing framework to the studied admission control problem, with all the features of IEEE 802.11e HCCA being taken into consideration. Experimental results show that our proposed admission control scheme achieves significant improvement in network utilization while still satisfying all the quality-of-service (QoS) requirements.
Accurate playfield detection using Area-of-Coverage

Playfield detection is an important task in sports video content analysis, as it provides the ground for further operations such as object detection, object tracking or semantic event highlight and summarization. Traditional approaches of playfield detection usually rely on the homogeneity cue of the field pixels' color to separate it from other areas within the image frame. Although encouraging results have been achieved using this methodology, the accuracy of the detection still largely varies depending on the grass pattern of the field. In this paper, we propose a novel approach to detect the playfield region using the concept of Area-of-Coverage. First, the system uses Gaussian Mixture Model (GMM) to obtain a rough segmentation of the playfield area. Based on this initial segmentation, feature points of the field are detected for calibration using homography and then the Area of Coverage (AoC) of the camera view is calculated. Finally, this AoC is back-projected to the image frame to provide a refine playfield detection. Experiments on real videos show that the proposed system achieves higher accuracy as well as stability compared to existing approaches.
Modeling Hop Length Distributions for Reactive Routing Protocols in One Dimensional MANETs

In mobile ad hoc networks (MANETs), packets hop from a source to a series of forwarding nodes until they reach the desired destination. Defining the hop length to be the distance between two adjacent forwarding nodes, we observe that the two adjacent forwarding nodes tend to be farther away from each other with a higher probability in a one-dimensional MANET. We derive the probability density functions for the hop lengths to confirm our observation. Applying the developed results, we further formulate the relationship between the mean number of hops and the distance between the source and the destination.
Robust Transmission of JPEG2000 Encoded Images Over Packet Loss Channels

JPEG2000, the latest international image compression standard, owns many unique characteristics that are different from other well-known image compression schemes such as JPEG and SPIHT. As a result, how to robustly transmit JPEG2000 bitstreams is an important research topic. In this paper, we apply our previous IL-ULP (improved layered unequal loss protection) scheme to transmit JPEG2000 coded images. We propose an accurate end-to-end distortion model to analyze the influence of different channel packets on the distortion of received images, where we consider the distortion contribution from each coding pass in each code-block. Our end-to-end analysis provides a feasible way to optimally allocate unequal FEC to a JPEG2000 bitstream according to given channel conditions. Experimental results demonstrate that our proposed IL-ULP can achieve good performance for transmission of JPEG2000 bitstreams.
Enriched Deep Recurrent Visual Attention Model for Multiple Object Recognition

We design an Enriched Deep Recurrent Visual Attention Model (EDRAM) — an improved attention-based architecture for multiple object recognition. The proposed model is a fully differentiable unit that can be optimized end-to-end by using Stochastic Gradient Descent (SGD). The Spatial Transformer (ST) was employed as visual attention mechanism which allows to learn the geometric transformation of objects within images. With the combination of the Spatial Transformer and the powerful recurrent architecture, the proposed EDRAM can localize and recognize objects simultaneously. EDRAM has been evaluated on two publicly available datasets including MNIST Cluttered (with 70K cluttered digits) and SVHN (with up to 250k real world images of house numbers). Experiments show that it obtains superior performance as compared with the state-of-the-art models.
LT codes decoding: Design and analysis

LT codes provide an efficient way to transfer information over erasure channels. Past research has illustrated that LT codes can perform well for a large number of input symbols. However, it is shown that LT codes have poor performance when the number of input symbols is small. We notice that the poor performance is due to the design of the LT decoding process. In this respect, we present a decoding algorithm called full rank decoding that extends the decodability of LT codes by usingWiedemann algorithm.We provide a detailed mathematical analysis on the rank of the random coefficient matrix to evaluate the probability of successful decoding for our proposed algorithm. Our studies show that our proposed method reduces the overhead significantly in the cases of small number of input symbols yet preserves the simplicity of the original LT decoding process.
FEC-based video streaming over packet loss networks with pre-interleaving

The authors present a FEC-based end-to-end error control scheme for video streaming over packet loss networks. We propose a novel robust video streaming system in which an interleaving is applied to the compressed bitstream before channel coding. The application of such preinterleaving is able to improve the performance of video streaming over packet loss networks because pre-interleaving can simultaneously satisfy different requirements arising from both channel coding and source coding. We demonstrate with simulations that the proposed approach can achieve a significant improvement for video streaming over heavy packet loss networks.
MIML-FCN+: Multi-Instance Multi-Label Learning via Fully Convolutional Networks with Privileged Information

Multi-instance multi-label (MIML) learning has many interesting applications in computer visions, including multi-object recognition and automatic image tagging. In these applications, additional information such as bounding-boxes, image captions and descriptions is often available during training phrase, which is referred as privileged information (PI). However, as existing works on learning using PI only consider instance-level PI (privileged instances), they fail to make use of bag-level PI (privileged bags) available in MIML learning. Therefore, in this paper, we propose a two-stream fully convolutional network, named MIML-FCN+, unified by a novel PI loss to solve the problem of MIML learning with privileged bags. Compared to the previous works on PI, the proposed MIML-FCN+ utilizes the readily available privileged bags, instead of hard-to-obtain privileged instances, making the system more general and practical in real world applications. As the proposed PI loss is convex and SGD-compatible and the framework itself is a fully convolutional network, MIML FCN+ can be easily integrated with state-of-the-art deep learning networks. Moreover, the flexibility of convolutional layers allows us to exploit structured correlations among instances to facilitate more effective training and testing. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed MIML-FCN+, outperforming state-of-the-art methods in the application of multi-object recognition.
Admission control in IEEE 802.11e wireless LANs

Although IEEE 802.11 based wireless local area networks have become more and more popular due to low cost and easy deployment, they can only provide best effort services and do not have quality of service supports for multimedia applications. Recently, a new standard,  802.11e, has been proposed, which introduces a so-called hybrid coordination function containing two medium access mechanisms: contention-based channel access and controlled channel access. In this article we first give a brief tutorial on the various MAC-layer QoS mechanisms provided by 802.11e. We show that the 802.11e standard provides a very powerful platform for QoS supports in WLANs. Then we provide an extensive survey of recent advances in admission control algorithms/protocols in IEEE 802.11e WLANs. Our survey covers the research work in admission control for both EDCA and HCCA. We show that the new MAC-layer QoS schemes and parameters provided in EDCA and HCCA can be well utilized to fulfill the requirements of admission control so that QoS for multimedia applications can be provided in WLANs. Last, we give a summary of the design of admission control in EDCA and HCCA, and point out the remaining challenges.
Cooperative Retransmissions through Collisions

Interference in wireless networks is one of the key capacity-limiting factors. Recently developed interference-embracing techniques show promising performance on turning collisions into useful transmissions. However, the interference-embracing techniques are hard to apply in practical applications due to their strict requirements. In this paper, we consider utilizing the interference-embracing techniques in a common scenario of two interfering sender-receiver pairs. By employing opportunistic listening and analog network coding (ANC), we show that compared to traditional ARQ retransmission, a higher retransmission throughput can be achieved by allowing two interfering senders to cooperatively retransmit selected lost packets at the same time. This simultaneous retransmission is facilitated by a simple handshaking procedure without introducing additional overhead. Simulation results demonstrate the superior performance of the proposed cooperative retransmission.
High-quality Kinect depth filtering for real-time 3D telepresence

3D telepresence is a next-generation multimedia application, offering remote users an immersive and natural video-conferencing environment with real-time 3D graphics. Kinect sensor, a consumer-grade range camera, facilitates the implementation of some recent 3D telepresence systems. However, conventional data filtering methods are insufficient to handle Kinect depth error because such error is quantized rather than just randomly-distributed. Hence, one could often observe large irregularly-shaped patches of pixels that receive the same depth values from Kinect. To enhance visual quality in 3D telepresence, we propose a novel depth data filtering method for Kinect by means of multi-scale and direction-aware support windows. In addition, we develop a GPU-based CUDA implementation that can perform real-time depth filtering. Results from the experiments show that our method can reconstruct hole-free surfaces that are smoother and less bumpy compared to existing methods like bilateral filtering.
Operational rate-distortion design for joint source-channel coding over noisy channels

An optimal joint source-channel coding (OJSCC) scheme is developed for memoryless generalized Gaussian distribution (GGD) sources encoding and transmission over noisy channels. Two channel models are studied, binary symmetric channels (BSC) for memoryless channels and Gilbert-Elliott channels (GEC) for bursty channels. The operational rate-distortion (R-D) function we adopted represents an end-to-end error measurement that includes errors due to both quantization and channel noise. In particular, we are able to incorporate both the channel transition probability and channel bit error rate in the case of bursty channels. With the operational R-D function, we can achieve an optimum tradeoff between source coding accuracy and channel error protection under a fixed transmission rate. Experiments show that for BSC, OJSCC outperforms the best channel optimized scalar quantization (COSQ) system at high bit rate constraint; while for GEC, we show that the optimal design achieves better performance than the popular designs based on either the average BER or the worst BER. Moreover, based on the results of OJSCC, we propose a robust joint source-channel coding (RJSCC) scheme based on a combination of OJSCC with allpass filtering, RJSCC can be applied to a broad class of GGD sources with shape factor /spl nu/<2.0 to achieve an improved transmission performance.
Robust joint source-channel coding for image transmission over wireless channels

We present a fixed-length robust joint source-channel coding (RJSCC) scheme for transmitting images over wireless channels. The system integrates a joint source-channel coding (JSCC) scheme with all-pass filtering source shaping to enable robust image transmission. In particular, we are able to incorporate both transition probability and bit error rate of a bursty channel model into an end-to-end rate-distortion (R-D) function to achieve an optimum tradeoff between source coding accuracy and channel error protection under a fixed transmission rate. Experimental results show that the proposed scheme can achieve not only high peak signal-to-noise ratio performance, but also excellent perceptual quality, especially when the channel mismatch occurs.
Wordfence: Text detection in natural images with border awareness

In recent years, text recognition has achieved remarkable success in recognizing scanned document text. However, word recognition in natural images is still an open problem, which generally requires time consuming post-processing steps. We present a novel architecture for individual word detection in scene images based on semantic segmentation. Our contributions are twofold: the concept of WordFence, which detects border areas surrounding each individual word and a novel pixelwise weighted softmax loss function which penalizes background and emphasizes small text regions. WordFence ensures that each word is detected individually, and the new loss function provides a strong training signal to both text and word border localization. The proposed technique avoids intensive post-processing, producing an end-to-end word detection system. We achieve superior localization recall on common benchmark datasets — 92% recall on ICDAR11 and ICDAR13 and 63% recall on SVT. Furthermore, our end-to-end word recognition system achieves state-of-the-art 86% F-Score on ICDAR13.
Quality-smoothed encoding for real-time video streaming applications

For video streaming applications, it is highly desired that video signals can be encoded in not only good average quality but also smooth video quality or less quality fluctuations among adjacent frames. In general, two-pass encoding schemes are often employed to achieve consistent video quality given the bandwidth constraints. However, for real-time video applications, it is impossible to encode a video sequence twice and thus global statistical information for the entire video sequence is not available. In this paper, we jointly consider the video smoothing technique and the bandwidth constraint by adoptively adjusting the bit rate allocated to the current frame with a sliding window. Experimental results show that our proposed scheme can achieve both smooth video quality and high average PSNR performance.
WLC19-5: Admission Control with Traffic Shaping for Variable Bit Rate Traffic in IEEE 802.11e WLANs

With the increasing popularity of using WLANs for Internet access, the controlled channel access mechanism in IEEE 802.11e WLANs, HCCA, has received much more attentions since its inherent centralized mechanism is more efficient in handling time-bounded multimedia traffics. So far, only a few research works address the admission control problem of variable bit rate (VBR) traffic over HCCA. These existing works consider each traffic flow individually and thus cannot exploit the statistical multiplexing gain among multiple VBR traffic flows. In this paper, we apply the existing statistical multiplexing works to the studied admission control problem with all the features of 802.11e HCCA being taken into consideration. Experimental results show that our proposed admission control achieves significant improvement in network utilization while still satisfying all the QoS requirements.
Action proposals using hierarchical clustering of super-trajectories

Action localization aims to determine the spatial and temporal location of certain action which appears in a video. To facilitate action localization, spatio-temporal proposals which are likely to contain the action of interest are extracted to reduce the search space of candidate locations in a video, inspired by the object proposals in images. In this paper, considering the effectiveness of spatio-temporal trajectories for video action recognition and action proposal generation, we build our unsupervised action proposal generation pipeline upon super-trajectories. Specifically, we first group trajectories into super-trajectories inspired by super-voxels, and then employ hierarchical clustering on super-trajectories by taking different aspect and temporal ratios into consideration. Comprehensive experiments on two benchmark datasets (i.e., UCF-sports and MSR-II) demonstrate that our action proposal generation pipeline not only achieves the state-of-the-art recall, but also achieves competitive results for the action localization task.
Maximum Multipath Routing Throughput in Multirate Wireless Mesh Networks

In this paper, we consider the problem of finding the maximum routing throughput between any pair of nodes in an arbitrary multirate wireless mesh network (WMN) using multiple paths. Multipath routing is an efficient technique to maximize routing throughput in WMN, however maximizing multipath routing throughput is a NP-complete problem due to the shared medium for electromagnetic wave transmission in wireless channel, inducing collision-free scheduling as part of the optimization problem. In this work, we first provide problem formulation that incorporates collision-free schedule, and then based on this formulation we design an algorithm with search pruning that jointly optimizes paths and transmission schedule. Though suboptimal, compared to the known optimal single path flow, we demonstrate that an efficient multipath routing scheme can increase the routing throughput by up to 100% for simple WMNs.