nils-herrmann/clean_scirepeval huggingface dataset:
LT-W: Improving LT Decoding With Wiedemann Solver

Luby transform (LT) codes provide an efficient way to transfer information over erasure channels. Past research has shown that LT codes can perform well for a large number of input symbols. However, mathematical analysis and simulation results have revealed that the packet overhead for LT decoders can be as large as 100% when the number of input symbols is small. Designing an efficient decoder to handle a small number of symbols becomes an imminent research issue. In this paper, we make an observation that LT decoders often fail to recover all the input symbols, while LT encoders have a high probability of producing a full-rank coefficient matrix. Motivated by this observation, we propose a novel decoding algorithm called LT-W, in which we incorporate the use of the Wiedemann solver into LT decoding to extend the decodability of LT codes. Extensive experiments show that our proposed method reduces the packet overhead significantly and yet preserves the efficiency of the original LT decoding process.
Weakly Supervised Fine-Grained Categorization With Part-Based Image Representation

In this paper, we propose a fine-grained image categorization system with easy deployment. We do not use any object/part annotation (weakly supervised) in the training or in the testing stage, but only class labels for training images. Fine-grained image categorization aims to classify objects with only subtle distinctions (e.g., two breeds of dogs that look alike). Most existing works heavily rely on object/part detectors to build the correspondence between object parts, which require accurate object or object part annotations at least for training images. The need for expensive object annotations prevents the wide usage of these methods. Instead, we propose to generate multi-scale part proposals from object proposals, select useful part proposals, and use them to compute a global image representation for categorization. This is specially designed for the weakly supervised fine-grained categorization task, because useful parts have been shown to play a critical role in existing annotation-dependent works, but accurate part detectors are hard to acquire. With the proposed image representation, we can further detect and visualize the key (most discriminative) parts in objects of different classes. In the experiments, the proposed weakly supervised method achieves comparable or better accuracy than the state-of-the-art weakly supervised methods and most existing annotation-dependent methods on three challenging datasets. Its success suggests that it is not always necessary to learn expensive object/part detectors in fine-grained image categorization.
Object Co-skeletonization with Co-segmentation

Recent advances in the joint processing of images have certainly shown its advantages over the individual processing. Different from the existing works geared towards co-segmentation or co-localization, in this paper, we explore a new joint processing topic: co-skeletonization, which is defined as joint skeleton extraction of common objects in a set of semantically similar images. Object skeletonization in real world images is a challenging problem, because there is no prior knowledge of the objects shape if we consider only a single image. This motivates us to resort to the idea of object co-skeletonization hoping that the commonness prior existing across the similar images may help, just as it does for other joint processing problems such as co-segmentation. Noting that skeleton can provide good scribbles for segmentation, and skeletonization, in turn, needs good segmentation, we propose a coupled framework for co-skeletonization and co-segmentation tasks so that they are well informed by each other, and benefit each other synergistically. Since it is a new problem, we also construct a benchmark dataset for the co-skeletonization task. Extensive experiments demonstrate that proposed method achieves very competitive results.
Efficient image retrieval based mobile indoor localization

Vision based localization has been investigated for many years. The existing Structure from Motion (SfM) technique can reconstruct the 3D models based on the input images. The image retrieval and feature matching allow us to find the correspondence between the query image and the 3D model. According to these, the location can be easily calculated. In mobile scenarios, the limited CPU speed, memory storage and network latency bring in new challenges. The state-of-the-art solution can not be easily adopted due to the complicated calculation and large resource consumption. In this paper, we leverage the techniques developed during the MPEG-7 Compact Descriptors for Visual Search (CDVS) standardization, which aims to provide high performance and low complexity compact descriptors. We show that these techniques are suitable for mobile device and can achieve state-of-the-art retrieval performance in indoor environment. Besides, we propose additional components including blur measurement and result smoothing to improve the performance of the location calculation process. Based on these techniques, a whole system which enables fast vision based localization on mobile device is developed. We present experiments on the real world situation, showing that the system can strike a balance between accuracy and efficiency.
Quality-Guided Fusion-Based Co-Saliency Estimation for Image Co-Segmentation and Colocalization

Despite the advantage of exploiting interimage information by performing joint processing of images for co-saliency, co-segmentation, or co-localization, it introduces a few drawbacks: 1) its necessity in scenarios where the joint processing might not perform better than individual image processing; 2) increased complexity over individual image processing; and 3) complex parameter tuning. In this paper, we propose a simple cosaliency estimation method where we fuse saliency maps of different images using the dense correspondence technique. More important, the co-saliency estimation is guided by our proposed quality measurement that helps decide whether the saliency fusion really improves the quality of the saliency map or not. Our basic idea for developing the quality metric is that a high-quality saliency map should have well-separated foreground and background, as well as a concentrated foreground like ground-truths. Extensive experiments on several benchmark datasets including the large-scale dataset, ImageNet, for the applications of foreground co-segmentation and co-localization show that our proposed framework is able to achieve very competitive results.
Use of pre-interleaving for video streaming over wireless access networks

We present an FEC-based end-to-end error control scheme for video streaming over wireless access networks, considering both bit errors and packet-loss. 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 pre-interleaving is able to significantly improve the error-combating performance of video streaming because the adoption of this pre-interleaving can simultaneously satisfy different requirements arising from both channel coding and source coding. Experimental results demonstrate the improved performance of the proposed pre-interleaving scheme, especially in the case of highly bursty and regular channel errors.
Delay-Optimized File Retrieval under LT-Based Cloud Storage

Fountain-code based cloud storage system provides reliable online storage solution through placing unlabeled content blocks into multiple storage nodes. Luby Transform (LT) code is one of the popular fountain codes for storage systems due to its efficient recovery. However, to ensure high success decoding of fountain codes based storage, retrieval of additional fragments is required, and this requirement could introduce additional delay. In this paper, we show that multiple stage retrieval of fragments is effective to reduce the file-retrieval delay. We first develop a delay model for various multiple stage retrieval schemes applicable to our considered system. With the developed model, we study optimal retrieval schemes given requirements on success decodability. Our numerical results suggest a fundamental tradeoff between the file-retrieval delay and the target probability of successful file decoding, and that the file-retrieval delay can be significantly reduced by optimally scheduling packet requests in a multi-stage fashion.
MPEG-4 based stereoscopic and multiview video coding

The paper proposes a stereoscopic and multiview video sequences encoder compatible with MPEG-4. The main view of the sequences is encoded as an MPEG-4 bitstream and the auxiliary views are encoded by joint motion and disparity compensation, so that both temporal and spatial redundancy are reduced and high compression is achieved. The disparity and motion fields for both main and auxiliary views are calculated using a joint disparity-motion estimation and regularization algorithm. We test the proposed encoder for coding stereoscopic (2-view) and 3-view video sequences and compare it with the MPEG multi-view profile (MVP) implemented on the MPEG-4 platform. The proposed system achieves higher image quality at the same bitrate than existing multiview video encoders and is suitable for various applications, such as video-conferencing and 3D telepresence systems.
Seeds-Based Part Segmentation by Seeds Propagation and Region Convexity Decomposition

Object part segmentation is an important and challenging task in computer vision. The existing supervised part segmentation methods need pixel level training data which leads to a huge workload for the user. In this paper a weakly supervised part segmentation method is proposed which segments part regions from multiple images by only several seeds on an image. Two aspects such as seed propagation among multiple images and part generation from seeds are considered. The first aspect is to generate part seeds in each image in terms of seed propagation which is accomplished by part matching combined with latent object regions. We fuse the local part matching and global shape cosegmentation to avoid the noise propagation. The second aspect is to segment part regions from object regions and part seeds which is formulated as the object shape decomposition model. The shape convexity analysis and seed location are fused to accomplish the decomposition and the final part segmentation. The proposed method is verified on the PASCAL 2010 dataset Bird dataset Cat-Dog dataset and UCF Sports Actions dataset. Experimental results demonstrate the effectiveness of the proposed method with larger intersection over union (IOU) values compared with existing weakly supervised part generation methods.
An efficient network coding based retransmission algorithm for wireless multicast

Retransmission based on packet acknowledgement (ACK/NAK) is a fundamental error control technique employed in IEEE 802.11–2007 unicast network. However the 802.11–2007 standard falls short of proposing a reliable MAC-level recovery protocol for multicast frames. In this paper we propose a latency and bandwidth efficient coding algorithm based on the principles of network coding for retransmitting lost packets in a singlehop wireless multicast network and demonstrate its effectiveness over previously proposed network coding based retransmission algorithms.
Performance Evaluation of IPTV Over Wireless Home Networks

The emergence of Internet Protocol Television (IPTV) has brought potentials to revolutionize personal entertainment. Streaming TV content over the highly pervasive wireless networks allows easy access to personalized entertainment. Focusing on wireless home entertainment which is one of the main driving forces of IPTV development, we develop a Markovian framework that investigates several important issues related to network capacity and streaming quality in an IEEE 802.11e enabled wireless home network. The Markovian framework captures not only the IEEE 802.11e MAC protocol performance, but also the statistical characteristics of IPTV media streams. The inclusion of these two key descriptions allows our model to be practically used in wireless home network planning and design. To deal with the complexity in the model, we apply the efficient Matrix Geometric approach to obtain numerical results. We further perform simulations with real IPTV traffic to not only validate our analytical results, but also obtain further insight to the performance.
Scalable Video Adaptation in Wireless Home Networks with a Mixture of IPTV and VoD Users

In this paper, we propose a scalable video adaptation mechanism to improve the overall quality of service (QoS) in wireless home networks with a mixture of IPTV and VoD users. Unlike most of the existing studies on video streaming over WLANs, which usually focus on only one type of video streams, either stored videos or live videos, here we consider a mixture of live and stored videos. We make use of the pre-buffering time of VoD users in the rate adaptation for both IPTV and VoD users so as to achieve an overall optimal QoE for all the users. In addition, we employ the standard H.264 SVC and consider a practical multi-rate scenario, where the physical data rate of a wireless user is determined according to its distance to the access point (AP). The corresponding multi-rate multi-queue MAC-layer throughput is analyzed so as to accurately estimate the bandwidth for the video streaming. The ns-2 simulations verify the effectiveness of the proposed scalable video adaptation.
Group saliency propagation for large scale and quick image co-segmentation

Most of the existing co-segmentation methods are usually complex, and require pre-grouping of images, fine-tuning a few parameters and initial segmentation masks etc. These limitations become serious concerns for their application on large scale datasets. In this paper, Group Saliency Propagation (GSP) model is proposed where a single group saliency map is developed, which can be propagated to segment the entire group. In addition, it is also shown how a pool of these group saliency maps can help in quickly segmenting new input images. Experiments demonstrate that the proposed method can achieve competitive performance on several benchmark co-segmentation datasets including ImageNet, with the added advantage of speed up.
Dynamic Programming-Based Reverse Frame Selection for VBR Video Delivery Under Constrained Resources

In this paper, we investigate optimal frame-selection algorithms based on dynamic programming for delivering stored variable bit rate (VBR) video under both bandwidth and buffer size constraints. Our objective is to find a feasible set of frames that can maximize the video's accumulated motion values without violating any constraint. It is well known that dynamic programming has high complexity. In this research, we propose to eliminate nonoptimal intermediate frame states, which can effectively reduce the complexity of dynamic programming. Moreover, we propose a reverse frame selection (RFS) algorithm, where the selection starts from the last frame and ends at the first frame. Compared with the conventional dynamic programming-based forward frame selection, the RFS is able to find all of the optimal results for different preloads in one round. We further extend the RFS scheme to solve the problem of frame selection for VBR channels. In particular, we first perform the RFS algorithm offline, and the complexity is modest and scalable with the aids of frame stuffing and nonoptimal state elimination. During online streaming, we only need to retrieve the optimal frame-selection path from the pregenerated offline results, and it can be applied to any VBR channels as long as the VBR channels can be modeled as piecewise CBR channels. Experimental results show good performance of our proposed algorithms
A Novel Reverse Frame Selection Scheme for Video Streaming over VBR Channels

In this paper, we propose a reverse frame selection (RFS) scheme based on dynamic programming to solve for the problem of video streaming over VBR channels. In particular, we first consider forward frame selection (FFS) for video streaming over CBR channels. We propose to discard non-optimal states in FFS to reduce the computational cost of dynamic programming. Then we find that the problem can also be solved by RFS with one additional benefit of finding all the optimal results for different preloads in one round. Furthermore, we extend RFS for video streaming over VBR channels where we do not know when and how the channel is going to change in the future. The major advantage of our proposed scheme is that we only need to run RFS several times, and the obtained results can be applied to any type of VBR channels with bandwidth changes occurring at any time. Preliminary results show the good performance of our proposed scheme
Joint Network Coding for interfering wireless multicast networks

Interference in wireless networks is one of the key-capacity limiting factor. The multicast capacity of an ad-hoc wireless network decreases with an increasing number of transmitting and/or receiving nodes within a fixed area. Digital Network Coding (DNC) has been shown to improve the multicast capacity of non-interfering wireless network. However recently proposed Physical-layer Network Coding (PNC) and Analog Network Coding (ANC) has shown that it is possible to decode an unknown packet from the collision of two packet, when one of the colliding packet is known a priori. Taking advantage of such collision decoding scheme, in this paper we propose a Joint Network Coding based Cooperative Retransmission (JNC-CR) scheme, where we show that ANC along with DNC can offer a much higher retransmission gain than that attainable through either ANC, DNC or Automatic Repeat reQuest (ARQ) based retransmission. This scheme can be applied for two wireless multicast groups interfering with each other. Because of the broadcast nature of the wireless transmission, receivers of different multicast group can opportunistically listen and cache packets from the interfering transmitter. These cached packets, along with the packets the receiver receives from its transmitter can then be used for decoding the JNC packet. We validate the higher retransmission gain performance of JNC with an optimal DNC scheme, using simulation.
Layered unequal loss protection with pre-interleaving for progressive image transmission over packet loss channels

Most existing ULP (unequal loss protection) schemes do not consider the minimum quality requirement and usually have high computation complexity. Previously, we proposed a layered ULP (L-ULP) scheme to solve the mentioned problems at the cost of performance degradation. In this paper, we propose to combine the L-ULP with the preinterleaving, which is able to delay the occurrence of the first unrecoverable loss in the source data bitstream while still keeping the original priorities among different layers. Experimental results show that the proposed joint L-ULP and pre-interleaving scheme is able to achieve as good performance as that of the ULP while the complexity is much lower.
Dense correspondence based prediction for image set compression

In this paper, we propose a novel dense correspondence based prediction approach to reduce the inter-image redundancy for image set compression. Unlike previous methods, we manage to utilize the dense correspondence to predict and parameterize the inter-image relation and then reconstruct a new reference for the subsequent HEVC inter-prediction and encoding. Comparing to relevant state-of-the-art feature-based methods, our method is able to locally approximate the inter-image relation and thus more robust to complex local variations. Experimental results show that our proposed approach achieves better coding gains when the local variations are dominant.
Efficient object feature selection for action recognition

Currently most action recognition or video classification tasks highly rely on the motion features such as state-of-the-art Improved Dense Trajectory (IDT) features. Despite the huge success, IDT features lack of rich static object-level information. In this paper, we make use of the object-level features for action recognition tasks. For efficiently and effectively processing large-scale video data, we propose a two-layer feature selection framework including local object feature selection (LS) and global feature selection (GS). Both of the selection methods can improve recognition accuracy while greatly reducing the feature dimension or feature processing complexity. Experimental results show that the selected object-level features contain complimentary information to IDT features and the combination with IDT features can further improve the recognition accuracy significantly.
Multiple foreground recognition and cosegmentation: An object-oriented CRF model with robust higher-order potentials

Localizing, recognizing, and segmenting multiple foreground objects jointly from a general user's photo stream that records a specific event is an important task with many useful applications. As argued in recent Multiple Foreground Cosegmentation (MFC) work by Kim and Xing, this task is very challenging in that it contrasts substantially from the classical cosegmentation problem, and aims to parse a set of realistic event photos but each containing irregularly occurring multiple foregrounds with high appearance and scene configuration variations. Inspired by the impressive advance in scene understanding and object recognition, this paper casts the multiple foreground recognition and cosegmentation (MFRC) problem within a conditional random fields (CRFs) framework in a principled manner. We capitalize centrally on the key objective that MFRC is to segment out and annotate foreground objects or “things” rather than “stuff”. To this end, we exploit a few complementary objectness cues (e.g. contours, object detectors and layout) and propose novel and efficient methods to capture object-level information. Integrating object potentials as soft constraints (e.g. robust higher-order potentials defined over detected object regions) with low-level unary and pairwise terms holistically, we solve the MFRC task with a probabilistic CRF model. The inference for such a CRF model is performed efficiently with graph cut based move making algorithms. With a minimal amount of user annotations on just a few example photos, the proposed approach produces spatially coherent, boundary-aligned segmentation results with correct and consistent object labeling. Experiments on the FlickrMFC dataset justify that our method achieves state-of-the-art performance.
A high-performance and low-complexity video transcoding scheme for video streaming over wireless links

Video streaming over wireless links involves two basic needs: rate reduction transcoding and error control channel coding. For traditional transcoding systems, the performance is usually proportional to the system complexity. In this paper, we propose a novel transcoding scheme, which can achieve better performance with reduced system complexity. Coupled with a simple joint source-channel bit allocation approach, the proposed transcoding can accurately throttle the source coding rate so that there is sufficient bandwidth left for channel coding to correct channel errors in wireless links. Simulation results demonstrate that the proposed video streaming system can achieve a good performance without much increasing in system complexity.