nils-herrmann/clean_scirepeval huggingface dataset:
Robust partial face recognition using instance-to-class distance

We present a new face recognition approach from partial face patches by using an instance-to-class distance. While numerous face recognition methods have been proposed over the past two decades, most of them recognize persons from whole face images. In many real world applications, partial faces usually occur in unconstrained scenarios such as visual surveillance systems. Hence, it is very important to recognize an arbitrary facial patch to enhance the intelligence of such systems. In this paper, we develop a robust partial face recognition approach based on local feature representation, where the similarity between each probe patch and gallery face is computed by using the instance-to-class distance with the sparse constraint. Experiments on two popular face datasets are presented to show the efficacy of our proposed method.
A new content-based hybrid video transcoding method

Many video transcoding architectures have been proposed to reduce the bitrates of compressed video bitstreams. Requantization of transformed coefficients (such as DCT coefficients), spatial resolution down-sampling, and temporal resolution down-sampling are the three transcoding tools which are commonly used to reduce compressed video bitrates. We propose a new video transcoding method that combines the use of different transcoding tools to transcode compressed video bit streams to various target bitrates while maintaining good subjective/objective video quality. The transcoding tools are selected based on the video content, which is characterized by two video content descriptors inferring the video spatial and motion activities, respectively. These two content descriptors can be readily computed from the compressed video bit streams without full decompression. Experimental results show that the proposed method can provide better video quality than other existing transcoders that make use of a single transcoding tool.
Arbitrary downsizing video transcoding using fast motion vector reestimation

We propose a video transcoding method for arbitrarily downsizing a precoded video by reestimating from its original motion vectors the new motion vectors required to code the downsized video. Compared with the existing methods for downsizing a precoded video by an integral factor, the main advantage of the proposed method is that the spatial resolution of the precoded video can be freely adjusted to meet different bandwidth and device requirements. Experimental results show that the proposed method can obtain arbitrarily downsized video with good perceptual quality while reducing the computational complexity of the process.
Effects of facial alignment for age estimation

Age estimation is an important enabling capability for the near future, especially in applications related to Human Computer Interaction. Perhaps due to technical difficulties, age estimation has only recently begun to receive more attention. One of the more important pre-processing steps before age estimation is facial alignment, which spatially transforms a face image to align certain facial features, in order to maximize classification accuracy. However the capability for facial alignment in automated age estimation literature is commonly assumed, and the effects of facial alignment is not addressed, even though it is an important issue especially for live deployment. In this paper, we present, to our knowledge, the results of the first systematic investigation on the effects of facial alignment on age estimation accuracy, and conclude with some directions on further topics of investigation in this area.
A vision-based approach to early detection of drowning incidents in swimming pools

We present in this paper a vision-based approach to detection of drowning incidents in swimming pools at the earliest possible stage. The proposed approach consists of two main parts: a vision component which can reliably detect and track swimmers in spite of large scene variations of monitored pool areas, and an event-inference module which parses observation sequences of swimmer features for possible drowning behavioral signs. The vision component employs a model-based approach to represent and differentiate the background pool areas and foreground swimmers. The event-inference module is constructed based on a finite state machine, which integrates several reasoning rules formulated from universal motion characteristics of drowning swimmers. Possible drowning incidents are quickly detected using a sequential change detection algorithm. We have applied the proposed approach to a number of video clips of simulated drowning and obtained promising results as reported in this paper.
Deep Transfer Metric Learning.

Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption does not hold in many real visual recognition applications, especially when samples are captured across different data sets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML, where the output of both the hidden layers and the top layer are optimized jointly. To preserve the local manifold of input data points in the metric space, we present two new methods, DTML with autoencoder regularization and DSTML with autoencoder regularization. Experimental results on face verification, person re-identification, and handwritten digit recognition validate the effectiveness of the proposed methods.
A MAXMIN resource allocation approach for scalable video delivery over multiuser MIMO-OFDM systems

In this paper, we propose a novel approach for scalable video delivery over multiuser Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems with MAXMIN fairness ensured. Scalable Video Coding (SVC) is efficient for rate adaptation and unequal protection in wireless networks where heterogeneous mobile clients have different hardware specifications and time-varying channel conditions. Using the scalability characteristics of SVC, we propose a general approach to ensure fairness for multiple scalable video downlink from a multiple-antenna base station. The fairness is achieved by packet priority analysis referring to layer dependency from application layer and a MAXMIN resource allocation at the physical layer. Based on packet priorities, time-frequency resource, power and modulation schemes are adaptively selected for transmission. This scheme significantly improves the overall system performance and guarantee fairness among users, as demonstrated by experimental comparisons with conventional radio resource allocation schemes.
Arbitrary downsizing video transcoding using H.26L standard

Downsizing video transcoding is a useful technique for adapting the bit rate or frame size of a precoded video to suit better the constraints of different transmission networks and receiving devices. To avoid frame skipping and maintain good quality at low bit rates, we have proposed H.263-based video transcoding techniques for downszing a precoded video by an arbitrary factor. In this paper, we extend the proposed techniques by using the newly developed H.26L standard in order to fully exploit the advantages of arbitrary downsizing video transcoding. Experimental results show that the proposed method can achieve much better video quality than that using H.263 standard.
Program segmentation for TV videos

A number of video browsing and retrieval systems use a hierarchical video structure (video, scene, group, shot, and keyframe) as the basis for information organization and access. However, this structure cannot represent and manage efficiently the videos recorded from TV channels, since TV videos are usually very long and consist of many scenes. More importantly, people tend to watch these videos by program and not by scenes or shots. To address this problem, we insert a new structure level, referred to as program, between the video and scene level to enhance the hierarchical structure representation. An automatic program segmentation algorithm is presented for constructing the video structure in this paper. Experimental results based on real-world TV videos demonstrate the effectiveness of the proposed approach.
Object Instance Search in Videos via Spatio-Temporal Trajectory Discovery

Given a specific object as query, object instance search aims to not only retrieve the images or frames that contain the query, but also locate all its occurrences. In this work, we explore the use of spatio-temporal cues to improve the quality of object instance search from videos. To this end, we formulate this problem as the spatio-temporal trajectory search problem, where a trajectory is a sequence of bounding boxes that locate the object instance in each frame. The goal is to find the top- K trajectories that are likely to contain the target object. Despite the large number of trajectory candidates, we build on a recent spatio- temporal search algorithm for event detection to efficiently find the optimal spatio- temporal trajectories in large video volumes , with complexity linear to the video volume size. We solve the key bottleneck in applying this approach to object instance search by leveraging a randomized approach to enable fast scoring of any bounding boxes in the video volume. In addition , we present a new dataset for video object instance search. Experimental results on a 73-hour video dataset demonstrate that our approach improves the performance of video object instance search and localization over the state-of-the-art search and tracking methods.
An Efficient Early Termination Algorithm of Intra Prediction for H.264/AVC

We propose in this paper an efficient early termination algorithm of intra prediction for H.264/AVC. It uses the spatial correlation after 16times16 inter prediction to make the judgement on whether to discard intra prediction or not. Experimental results demonstrate that the proposed algorithm can save the encoding time of H.264/AVC (JM98) between 25-45% with negligible degradation in the quality
Real-Time, Adaptive, and Locality-Based Graph Partitioning Method for Video Scene Clustering

We propose in this paper an efficient, adaptive, and locality-based graph partitioning method for video scene clustering. First, a graph partitioning method is proposed to group video shots into scenes, and a peer-group filtering (PGF) scheme is used to identify all the shots similar to each particular shot based on Fisher's discriminant analysis. To work with computable shot similarity measures that have only limited discriminating power, we develop a graph partitioning scheme to cluster the shots by maximizing the likeness of shots within the same cluster and minimizing that between different clusters. Second, considering that video data are normally obtained and viewed sequentially, we propose to perform a locality-based PGF and graph partitioning on video segments with 50 shots, 100 shots, and so on. This proposed locality-based method has the advantage that the number of scene clusters is not required to be known a priori, and it can achieve performance comparable to that processing on the whole video sequence. Experimental results are presented to demonstrate the effectiveness and efficiency of the proposed method.
Fine-grained face verification: Dataset and baseline results

This paper investigates the problem of fine-grained face verification under unconstrained conditions. For the conventional face verification task, the verification model is trained with some positive and negative face pairs, where each positive sample pair contains two face images of the same person while each negative sample pair usually consists of two face images from different subjects. However, in many real applications, facial appearance of the twins looks very similar even if they are considered as a negative pair in face verification. Therefore, it is important to differentiate a given face pair to determine whether it is from the same person or a twins for a practical face verification system because most existing face verification systems fails to work well in such a scenario. In this work, we define the problem as fine-grained face verification and collect an unconstrained face dataset which contains 455 pairs of identical twins to generate negative face pairs to evaluate several baseline verification models for fine-grained unconstrained face verification. Benchmark results on the unsupervised setting and restricted setting show the challenge of the fine-grained face verification in the wild.
Blind PSNR estimation using shifted blocks for JPEG images

We propose a no-reference method for estimating the peak signal-to-noise ratio (PSNR) of images subject to quantization noise in the block-based discrete cosine transform (bDCT) domain. The proposed method uses bDCT coefficients with shifted block boundary positions from the compressed image to estimate the Laplace distribution parameter of the bDCT coefficients of the original image. The resulting distributions are used to estimate the quantization noise power and the PSNR of the compressed image.
Improved shot boundary detection method based on text edges

Shot boundary detection is a pre-requisite technique for video indexing and retrieval. To avoid the influence of flashlight on abrupt shot detection, many edge-based techniques are studied thoroughly. However, these techniques are still susceptible to miss and mistake detecting the abrupt changes. Our observation shows that one of the reasons for these errors is the existence of superimposed text which has rich edges and is ever presented in video frames. To provide a solution, we present a novel method that utilizes the edge type, text edge (edge in text area) or non-text-edge (edge in other text area), reducing erroneous detection with the appearance of video text. Compared to other edge-based detection techniques, experimental results show that our proposed method achieves preferable performance.
Accurate face localization in videos using effective information propagation

In this paper, we present a novel approach to accurate detection and tracking of human faces in videos. The idea is to propagate the information of a group of seed faces detected off-line with high certainties to recover the faces undetected or detected with only low confidence. Specifically, our approach first estimates from the color of the seed faces a person-specific skin color model, and based on which a particle filtering is performed for sequential face tracking. Then, a backward propagation scheme is devised to optimize the overall localization results in terms of smoothness. Experimental results demonstrate the efficacy of our approach.
A scalable resource allocation framework for SVC video transmissions over downlink MIMO-OFDM networks

In this paper, we propose a scalable resource allocation framework for transmission of multiple scalable videos over downlink MIMO-OFDM networks. First, we analyze the rate-utility relationship of scalable video by a packet prioritization scheme, which ensures an optimized video utility under the rate constraint. The scalable framework is then proposed to achieve differentiated service objectives for different video layers. To provide every user with a fair opportunity to receive video for basic viewing, a MAXMIN fairness is designed to have their base layer video packets received. When all users have their base layer packets successfully received, the resources are distributed to exploit network efficiency. Resource allocations are accomplished by the efficient bit loading and power allocation for different optimization goals. The performance of the proposed scheme is validated by comparing with conventional resource allocation schemes.
Enhanced gait recognition based on weighted dynamic feature

Gait Energy Image (GEI) has been shown to be a robust gait descriptor for gait recognition, and many algorithms based on GEI have been proposed. We propose in this paper an improved algorithm to exploit the discriminative information of GEI in identifying walking people based on gait sequences. Specifically, we first obtain the discriminative power of each pixel in the GEI, referred to as feature weight or feature score, through statistic learning from the whole gallery set. We then generate a binary mask for each frame in a gait sequence according to the intensity value of the GEI to separate the dynamic part from static part of GEI. Combining the feature score and the binary mask, we arrive at a new feature for every GEI for discriminative representation and effective recognition. Experimental results on both NLPR and USF databases show the effectiveness of our proposed algorithm in terms of gait recognition rate.
A comparative study of age-invariant face recognition with different feature representations

Age invariant face recognition is an important yet less investigated problem in the face recognition community. In this paper, we empirically evaluate state-of-the-art facial feature representations for age-invariant face recognition. Three representative features including local binary pattern (LBP), Gabor wavelets and gradient orientation pyramid (GOP) were applied, followed by a principal component analysis (PCA) to reduce the dimensions of the extracted features. Experimental results on the MORPH database, one of the largest publicly available face dataset containing thousands of longitudinal images are presented. Experimental results show that Gabor wavelets feature with five scales and eight orientations is the optimal feature representation method for age-invariant face recognition.
A unified probabilistic approach to face detection and tracking

In this paper, we present a novel probabilistic approach to detecting and tracking human faces in video sequences. Specifically, with the use of multimodal observations a graphical chain model integrating smooth regularization is proposed. Using this model, the recovery of faces that are not detected or detected with only low confidence by a frame-based detector can be formulated as an overall optimization problem constrained by a set of reference faces, which are initially detected with high certainties. Experimental results demonstrate the effectiveness of our approach.
Video Summarization via Multi-view Representative Selection

Video contents are inherently heterogeneous. To exploit different feature modalities in a diverse video collection for video summarization, we propose to formulate the task as a multi-view representative selection problem. The goal is to select visual elements that are representative of a video consistently across different views (i.e., feature modalities). We present in this paper the multi-view sparse dictionary selection with centroid co-regularization (MSDS-CC) method, which optimizes the representative selection in each view, and enforces that the view-specific selections to be similar by regularizing them towards a consensus selection. The problem can be efficiently solved by an alternating minimizing optimization with the fast iterative shrinkage thresholding algorithm (FISTA). We also show how the formulation can be applied to category-specific video summarization by incorporating visual co-occurrence priors. Experiments on benchmark video datasets validate the effectiveness of the proposed approach in comparison with other video summarization methods and representative selection methods.
Efficient rate control for MPEG-2 to H.264/AVC transcoding

A new video coding standard, H.264/AVC, has recently been standardized. It achieves its high coding efficiency by employing a number of advances in video coding technology. We propose an efficient rate control method for MPEG-2 to H.264/AVC transcoding. Specifically, the values of quantization parameters for I and B frames are computed adaptively based on the side information from the precoded video. Simulations show that our proposed method can meet the target bitrate accurately and achieves a better performance than the rate control scheme of the reference software, H.264/AVC JM 7.4, without increasing the complexity.
Performance Factors for Evaluating Color Filter Array Demosaicking Algorithms

Demosaicking process is generally performed to reconstruct full color images from color filter array samples acquired by digital cameras. Over the past recent years, a large number of demosaicking algorithms have been proposed in the literature. When comparing the performances of different demosaicking algorithms, some existing work might use different experimental settings and test images. The objective of this paper is to show that different experimental settings and test images could affect the comparison results substantially. In particular, a series of experiments have been conducted to examine and demonstrate the effects of three experimental settings-CFA arrangement, image size, and image luminance-on demosaicking performance. It is advocated that for a fair comparison of different demosaicking algorithms, these performance factors should be taken into consideration
Video transcoding for fast forward/reverse video playback

Fast forward and fast reverse playbacks are two common video browsing functions provided in many analog and digital video players. They help users quickly find and access video segments of interest by scanning through the content of a video at a faster than normal playback speed. We propose a video transcoding approach to realizing fast forward and reverse video playbacks by generating a new compressed video from a pre-coded video. To reduce the computational requirements, we design and compare several fast algorithms for estimating the motion vectors required in transcoded video. To accommodate changes due to frame skipping for fast video playback, we also alter the group-of-pictures structure of transcoded video. In addition, subjective tests are conducted to assess the minimum video peak-signal-to-noise-ratio degradation that is perceptible to viewers at different fast playback speeds. To this end, we obtain an adaptive video transcoding method, which combines intra-coding and inter-coding with a fast motion vector reestimation method to strike a good balance between computational complexity and transcoded video quality. Experimental results are reported to show the efficacy of the proposed method.
Deep Coupled Metric Learning for Cross-Modal Matching

In this paper, we propose a new deep coupled metric learning (DCML) method for cross-modal matching, which aims to match samples captured from two different modalities (e.g., texts versus images, visible versus near infrared images). Unlike existing cross-modal matching methods which learn a linear common space to reduce the modality gap, our DCML designs two feedforward neural networks which learn two sets of hierarchical nonlinear transformations (one set for each modality) to nonlinearly map samples from different modalities into a shared latent feature subspace, under which the intraclass variation is minimized and the interclass variation is maximized, and the difference of each data pair captured from two modalities of the same class is minimized, respectively. Experimental results on four different cross-modal matching datasets validate the efficacy of the proposed approach.