George Toderici
George Toderici is a research scientist / TLM of the Neural Compression team in Google Research. He and his team are exploring new methods for compression of multimedia content using techniques inspired from the neural network domain. Previously he has worked on video classification tasks based on classical methods as well as more modern neural network-based methods. Dr. Toderici has been involved in organizing the first and second Workshop and Challenge on Learned Image Compression (CLIC 2018-2021 at CVPR), the first and second YouTube-8M workshop at CVPR 2017, ECCV 2018, ICCV 2019, the THUMOS 2014 workshop at ECCV, and is one of the co-authors of the Sports-1M and Atomic Video Actions (AVA) datasets. Previously he has served as a Deep Learning area co-chair for ACM Intl. Conf. on Multimedia (MM) in 2014, In addition, he has served in the program committees of CVPR, ECCV, ICCV, ICLR and NIPS for numerous years. His research interests include deep learning, action recognition and video classification.
Authored Publications
Google Publications
Other Publications
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VCT: A Video Compression Transformer
Sung Jin Hwang
NeurIPS 2022, NeurIPS 2022
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We show how transformers can be used to vastly simplify neural video compression. Previous methods have been relying on an increasing number of architectural biases and priors, including motion prediction and warping operations, resulting in complex models. Instead, we independently map input frames to representations and use a transformer to model their dependencies, letting it predict the distribution of future representations given the past. The resulting video compression transformer outperforms previous methods on standard video compression data sets. Experiments on synthetic data show that our model learns to handle complex motion patterns such as panning, blurring and fading purely from data. Our approach is easy to implement, and we release code to facilitate future research.
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Neural Video Compression using GANs for Detail Synthesis and Propagation
European Conference on Computer Vision (2022)
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We present the first neural video compression method based on generative adversarial networks (GANs). Our approach significantly outperforms previous neural and non-neural video compression methods in a user study, setting a new state-of-the-art in visual quality for neural methods. We show that the GAN loss is crucial to obtain this high visual quality. Two components make the GAN loss effective: we i) synthesize detail by conditioning the generator on a latent extracted from the warped previous reconstruction to then ii) propagate this detail with high-quality flow. We find that user studies are required to compare methods, i.e., none of our quantitative metrics were able to predict all studies. We present the network design choices in detail, and ablate them with user studies.
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Nonlinear Transform Coding
Philip A. Chou
Sung Jin Hwang
IEEE Trans. on Special Topics in Signal Processing, vol. 15 (2021) (to appear)
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We review a class of methods that can be collected under the name nonlinear transform coding (NTC), which over the past few years have become competitive with the best linear transform codecs for images, and have superseded them in terms of rate–distortion performance under established perceptual quality metrics such as MS-SSIM. We assess the empirical rate–distortion performance of NTC with the help of simple example sources, for which the optimal performance of a vector quantizer is easier to estimate than with natural data sources. To this end, we introduce a novel variant of entropy-constrained vector quantization. We provide an analysis of various forms of stochastic optimization techniques for NTC models; review architectures of transforms based on artificial neural networks, as well as learned entropy models; and provide a direct comparison of a number of methods to parameterize the rate–distortion trade-off of nonlinear transforms, introducing a simplified one.
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Scale-Space Flow for End-to-End Optimized Video Compression
Sung Jin Hwang
2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR)
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Despite considerable progress on end-to-end optimized deep networks for image
compression, video coding remains a challenging task. Recently proposed
methods for learned video compression use optical flow and bilinear warping
for motion compensation and show competitive rate-distortion performance
relative to hand-engineered codecs like H.264 and HEVC. However, these
learning-based methods rely on complex architectures and training schemes
including the use of pre-trained optical flow networks, sequential training of
sub-networks, adaptive rate control, and buffering intermediate
reconstructions to disk during training. In this paper, we show that a
generalized warping operator that better handles common failure cases,
e.g. disocclusions and fast motion, can provide competitive compression
results with a greatly simplified model and training procedure. Specifically,
we propose scale-space flow, an intuitive generalization of optical
flow that adds a scale parameter to allow the network to better model
uncertainty. Our experiments show that a low-latency video compression model
(no B-frames) using scale-space flow for motion compensation can outperform
analogous state-of-the art learned video compression models while being
trained using a much simpler procedure and without any pre-trained optical
flow networks.
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High Fidelity Generative Image Compression
Michael Tschannen
Advances in Neural Information Processing Systems 34 (2020)
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We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system. In particular, we investigate normalization layers, generator and discriminator architectures, training strategies, as well as perceptual losses. In contrast to previous work, i) we obtain visually pleasing reconstructions that are perceptually similar to the input, ii) we operate in a broad range of bitrates, and iii) our approach can be applied to high-resolution images. We bridge the gap between rate-distortion-perception theory and practice by evaluating our approach both quantitatively with various perceptual metrics, and with a user study. The study shows that our method is preferred to previous approaches even if they use more than 2x the bitrate.
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End-to-end Learning of Compressible Features
Abhinav Shrivastava
2020 IEEE Int. Conf. on Image Processing (ICIP)
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Pre-trained convolutional neural networks (CNNs) are very powerful as an off the shelf feature generator and have been shown to perform very well on a variety of tasks. Unfortunately, the generated features are high dimensional and expensive to store: potentially hundreds of thousands of floats per example when processing videos. Traditional entropy based lossless compression methods are of little help as they do not yield desired level of compression while general purpose lossy alternatives (e.g. dimensionality reduction techniques) are sub-optimal as they end up losing important information. We propose a learned method that jointly optimizes for compressibility along with the original objective for learning the features. The plug-in nature of our method makes it straight-forward to integrate with any target objective and trade-off against compressibility. We present results on multiple benchmarks and demonstrate that features learned by our method maintain their informativeness while being order of magnitude more compressible.
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Image-dependent local entropy models for image compression with deep networks
Sung Jin Hwang
Michele Covell
International Conference on Image Processing (2018)
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The leading approach for image compression with artificial neural networks (ANNs) is to learn a nonlinear transform and a fixed entropy model that is directly optimized for rate-distortion performance. We show that this approach can be significantly improved by incorporating spatially local, image-dependent entropy models. The key insight is that existing ANN-based methods learn an entropy model that is shared between the encoder and decoder, but they do not transmit any side information that would allow the model to adapt to the structure of a specific image. We present a method for augmenting ANN-based image coders with image-dependent side information that leads to a 17.8% rate reduction over a state-of-the-art ANN-based baseline model on a standard evaluation set, and 70-98% reductions on images with low visual complexity that are poorly captured by a fixed, global entropy model.
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Towards a Semantic Perceptual Image Metric
Sung Jin Hwang
Sergey Ioffe
Sean O'Malley
Charles Rosenberg
2018 25th IEEE Int. Conf. on Image Processing (ICIP)
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We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification. We fit the metric to a new database based on 140k unique images annotated with ground truth by human raters who received minimal instruction. The resulting metric shows competitive performance on TID 2013, a database widely used to assess image quality assessments methods. More interestingly, it shows strong responses to objects potentially carrying semantic relevance such as faces and text, which we demonstrate using a visualization technique and ablation experiments. In effect, the metric appears to model a higher influence of semantic context on judgements, which we observe particularly in untrained raters. As the vast majority of users of image processing systems are unfamiliar with Image Quality Assessment (IQA) tasks, these findings may have significant impact on real-world applications of perceptual metrics.
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Joint Autoregressive and Hierarchical Priors for Learned Image Compression
Advances in Neural Information Processing Systems 31 (2018)
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Recent models for learned image compression are based on autoencoders that learn approximately invertible mappings from pixels to a quantized latent representation. The transforms are combined with an entropy model, which is a prior on the latent representation that can be used with standard arithmetic coding algorithms to generate a compressed bitstream. Recently, hierarchical entropy models were introduced as a way to exploit more structure in the latents than previous fully factorized priors, improving compression performance while maintaining end-to-end optimization. Inspired by the success of autoregressive priors in probabilistic generative models, we examine autoregressive, hierarchical, and combined priors as alternatives, weighing their costs and benefits in the context of image compression. While it is well known that autoregressive models can incur a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and can be combined to exploit the probabilistic structure in the latents better than all previous learned models. The combined model yields state-of-the-art rate–distortion performance and generates smaller files than existing methods: 15.8% rate reductions over the baseline hierarchical model and 59.8%, 35%, and 8.4% savings over JPEG, JPEG2000, and BPG, respectively. To the best of our knowledge, our model is the first learning-based method to outperform the top standard image codec (BPG) on both the PSNR and MS-SSIM distortion metrics.
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Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates in Recurrent Convolutional Neural Networks
Damien Vincent
Michele Covell
Sung Jin Hwang
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
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We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result using a single model. First, we show that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to several metrics. Second, we modify the recurrent architecture to improve spatial diffusion, which allows the network to more effectively capture and propagate image information through the network’s hidden state. Finally, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited number of bits to encode visually complex image regions. We evaluate our method on the Kodak and Tecnick image sets and compare against standard codecs as well recently published methods based on deep neural networks.
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AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
Carl Martin Vondrick
Jitendra Malik
CVPR (2018)
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This paper introduces a video dataset of spatio-temporally localized Atomic Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual actions in 430 15-minute video clips, where actions are localized in space and time, resulting in 1.58M action labels with multiple labels per person occurring frequently. The key characteristics of our dataset are: (1) the definition of atomic visual actions, rather than composite actions; (2) precise spatio-temporal annotations with possibly multiple annotations for each person; (3) exhaustive annotation of these atomic actions over 15-minute video clips; (4) people temporally linked across consecutive segments; and (5) using movies to gather a varied set of action representations. This departs from existing datasets for spatio-temporal action recognition, which typically provide sparse annotations for composite actions in short video clips. We will release the dataset publicly.
AVA, with its realistic scene and action complexity, exposes the intrinsic difficulty of action recognition. To benchmark this, we present a novel approach for action localization that builds upon the current state-of-the-art methods, and demonstrates better performance on JHMDB and UCF101-24 categories. While setting a new state of the art on existing datasets, the overall results on AVA are low at 15.6% mAP, underscoring the need for developing new approaches for video understanding.
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Spatially adaptive image compression using a tiled deep network
Michele Covell
Sung Jin Hwang
Damien Vincent
Proceedings of the International Conference on Image Processing (2017), pp. 2796-2800
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Deep neural networks represent a powerful class of function approximators that
can learn to compress and reconstruct images. Existing image compression
algorithms based on neural networks learn quantized representations with a
constant spatial bit rate across each image. While entropy coding introduces
some spatial variation, traditional codecs have benefited significantly by
explicitly adapting the bit rate based on local image complexity and visual
saliency. This paper introduces an algorithm that combines deep neural
networks with quality-sensitive bit rate adaptation using a tiled network. We
demonstrate the importance of spatial context prediction and show improved
quantitative (PSNR) and qualitative (subjective rater assessment) results
compared to a non-adaptive baseline and a recently published image compression
model based on fully-convolutional neural networks.
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Variable Rate Image Compression with Recurrent Neural Networks
Sean M. O'Malley
Sung Jin Hwang
Damien Vincent
Michele Covell
International Conference on Learning Representations (2016)
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A large fraction of Internet traffic is now driven by requests from mobile devices with relatively small screens and often stringent bandwidth requirements. Due to these factors, it has become the norm for modern graphics-heavy websites to transmit low-resolution, low-bytecount image previews (thumbnails) as part of the initial page load process to improve apparent page responsiveness. Increasing thumbnail compression beyond the capabilities of existing codecs is therefore a current research focus, as any byte savings will significantly enhance the experience of mobile device users. Toward this end, we propose a general framework for variable-rate image compression and a novel architecture based on convolutional and deconvolutional LSTM recurrent networks. Our models address the main issues that have prevented autoencoder neural networks from competing with existing image compression algorithms: (1) our networks only need to be trained once (not per-image), regardless of input image dimensions and the desired compression rate; (2) our networks are progressive, meaning that the more bits are sent, the more accurate the image reconstruction; and (3) the proposed architecture is at least as efficient as a standard purpose-trained autoencoder for a given number of bits. On a large-scale benchmark of 32×32 thumbnails, our LSTM-based approaches provide better visual quality than (headerless) JPEG, JPEG2000 and WebP, with a storage size that is reduced by 10% or more.
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Full Resolution Image Compression with Recurrent Neural Networks
Damien Vincent
Sung Jin Hwang
Michele Covell
arxiv (2016)
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This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural network for entropy coding. We compare RNN types (LSTM, associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study "one-shot" versus additive reconstruction architectures and introduce a new scaled-additive framework. We compare to previous work, showing improvements of 4.3%-8.8% AUC (area under the rate-distortion curve), depending on the perceptual metric used. As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.
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YouTube-8M: A Large-Scale Video Classification Benchmark
Nisarg Kothari
Balakrishnan Varadarajan
arXiv:1609.08675 (2016)
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Many recent advancements in Computer Vision are attributed to large datasets. Open-source software packages for Machine Learning and inexpensive commodity hardware have reduced the barrier of entry for exploring novel approaches at scale. It is possible to train models over millions of examples within a few days. Although large-scale datasets exist for image understanding, such as ImageNet, there are no comparable size video classification datasets.
In this paper, we introduce YouTube-8M, the largest multi-label video classification dataset, composed of ~8 million videos---500K hours of video---annotated with a vocabulary of 4803 visual entities. To get the videos and their (multiple) labels, we used the YouTube Data APIs. We filtered the video labels (Freebase topics) using both automated and manual curation strategies, including by asking Mechanical Turk workers if the labels are visually recognizable. Then, we decoded each video at one-frame-per-second, and used a Deep CNN pre-trained on ImageNet to extract the hidden representation immediately prior to the classification layer. Finally, we compressed the frame features and make both the features and video-level labels available for download. The dataset contains frame-level features for over 1.9 billion video frames and 8 million videos, making it the largest public multi-label video dataset.
We trained various (modest) classification models on the dataset, evaluated them using popular evaluation metrics, and report them as baselines. Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using the publicly-available TensorFlow framework. We plan to release code for training a basic TensorFlow model and for computing metrics.
We show that pre-training on large data generalizes to other datasets like Sports-1M and ActivityNet. We achieve state-of-the-art on ActivityNet, improving mAP from 53.8% to 77.8%. We hope that the unprecedented scale and diversity of YouTube-8M will lead to advances in video understanding and representation learning.
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Efficient Large Scale Video Classification
Balakrishnan Varadarajan
dblp computer science bibliography, http://dblp.org (2015) (to appear)
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Video classification has advanced tremendously over the recent years. A large part of the improvements in video classification had to do with the work done by the image classification community and the use of deep convolutional networks (CNNs) which produce competitive results with hand- crafted motion features. These networks were adapted to use video frames in various ways and have yielded state of the art classification results. We present two methods that build on this work, and scale it up to work with millions of videos and hundreds of thousands of classes while maintaining a low computational cost. In the context of large scale video processing, training CNNs on video frames is extremely time consuming, due to the large number of frames involved. We propose to avoid this problem by training CNNs on either YouTube thumbnails or Flickr images, and then using these networks' outputs as features for other higher level classifiers. We discuss the challenges of achieving this and propose two models for frame-level and video-level classification. The first is a highly efficient mixture of experts while the latter is based on long short term memory neural networks. We present results on the Sports-1M video dataset (1 million videos, 487 classes) and on a new dataset which has 12 million videos and 150,000 labels.
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Beyond Short Snippets: Deep Networks for Video Classification
Joe Yue-Hei Ng
Matthew Hausknecht
Rajat Monga
Computer Vision and Pattern Recognition (2015)
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Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval. In this work we propose and evaluate several deep neural network architectures to combine image information across a video over longer time periods than previously attempted. We propose two methods capable of handling full length videos. The first method explores various convolutional temporal feature pooling architectures, examining the various design choices which need to be made when adapting a CNN for this task. The second proposed method explicitly models the video as an ordered sequence of frames. For this purpose we employ a recurrent neural network that uses Long Short-Term Memory (LSTM) cells which are connected to the output of the underlying CNN. Our best networks exhibit significant performance improvements over previously published results on the Sports 1 million dataset (73.1% vs. 60.9%) and the UCF-101 datasets with (88.6% vs. 88.0%) and without additional optical flow information (82.6% vs. 72.8%).
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Pose Embeddings: A Deep Architecture for Learning to Match Human Poses
Greg Mori
Nisarg Kothari
Alexander Toshev
arXiv (2015)
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We present a method for learning an embedding that places images of humans in similar poses nearby. This embedding can be used as a direct method of comparing images based on human pose, avoiding potential challenges of estimating body joint positions. Pose embedding learning is formulated under a triplet-based distance criterion. A deep architecture is used to allow learning of a representation capable of making distinctions between different poses. Experiments on human pose matching and retrieval from video data demonstrate the potential of the method.
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Large-scale Video Classification with Convolutional Neural Networks
Andrej Karpathy
Sanketh Shetty
Li Fei-Fei
Proceedings of International Computer Vision and Pattern Recognition (CVPR 2014), IEEE
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Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a dataset of 1 million YouTube videos belonging to 487 classes. We study multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggest a multi-resolution, foveated architecture as a promising way of regularizing the learning problem and speeding up training. Our best spatio-temporal networks display significant performance improvements compared to strong feature-based baselines (55.3% to 63.9%), but only a surprisingly modest improvement compared to single-frame models (59.3% to 60.9%). We further study the generalization performance of our best model by retraining the top layers on the UCF-101 action Recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63.3% up from 43.9%).
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We consider the problem of content-based automated
tag learning. In particular, we address semantic varia-
tions (sub-tags) of the tag. Each video in the training
set is assumed to be associated with a sub-tag label, and
we treat this sub-tag label as latent information. A latent
learning framework based on LogitBoost is proposed which
jointly considers both tag label and the latent sub-tag label.
The latent sub-tag information is exploited in our frame-
work to assist the learning of our end goal, i.e., tag predic-
tion. We use the cowatch information to initialize the learn-
ing process. In experiments, we show that the proposed
method achieves significantly better results over baselines
on a large-scale testing video set which contains about 50
million YouTube videos.
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Finding Meaning on YouTube: Tag Recommendation and Category Discovery
Computer Vision and Pattern Recognition, IEEE (2010)
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We present a system that automatically recommends tags for YouTube
videos solely based on their audiovisual content. We also propose a novel framework
for unsupervised discovery of video categories that exploits knowledge mined
from the World-Wide Web text documents/searches. First, video content to tag
association is learned by training classifiers that map audiovisual
content-based features from millions of videos on YouTube.com to existing
uploader-supplied tags for these videos. When a new video is uploaded, the
labels provided by these classifiers are used to automatically suggest tags
deemed relevant to the video. Our system has learned a vocabulary of over 20,000 tags.
Secondly, we mined large volumes of Web pages and search queries to discover a
set of possible text entity categories and a set of associated is-A
relationships that map individual text entities to categories. Finally, we
apply these is-A relationships mined from web text on the tags learned from
audiovisual content of videos to automatically synthesize a reliable set of
categories most relevant to videos -- along with a mechanism to predict these
categories for new uploads. We then present rigorous rating studies that
establish that: (a) the average relevance of tags automatically recommended by
our system matches the average relevance of the uploader-supplied tags at the
same or better coverage and (b) the average precision@K of video categories
discovered by our system is 70% with K=5.
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Adaptive, selective, automatic tonal enhancement of faces
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ACM Multimedia, ACM, New York, NY, USA (2009), pp. 677-680
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A fast and robust method for video contrast enhancement is presented.
The method uses the histogram of each frame, along with upper and lower
bounds computed per shot in order to enhance the current frame. This
ensures that the artifacts introduced during the enhancement is reduced
to a minimum. Traditional methods that do not compute per-shot estimates
tend to over-enhance parts of the video such as fades and transitions.
Our method does not suffer from this problem, which is essential for a
fully automatic algorithm. We present the parameters for our methods
which yielded the best human feedback, which showed that out of 208 videos,
203 were enhanced, while the remaining 5 were of too poor quality to
be enhanced. Additionally, we present a visual comparison of our work with
the recently-proposed Weighted Thresholded Histogram Equalization (WTHE) algorithm.
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This paper discusses a new method for automatic
discovery and organization of descriptive concepts (labels)
within large real-world corpora of user-uploaded multimedia,
such as YouTube.com. Conversely, it also provides validation
of existing labels, if any. While training, our method does not
assume any explicit manual annotation other than the weak
labels already available in the form of video title, descrip-
tion, and tags. Prior work related to such auto-annotation
assumed that a vocabulary of labels of interest (e.g., indoor,
outdoor, city, landscape) is specified a priori. In contrast,
the proposed method begins with an empty vocabulary. It
analyzes audiovisual features of 25 million YouTube.com videos
– nearly 150 years of video data – effectively searching for
consistent correlation between these features and text metadata.
It autonomously extends the label vocabulary as and when it
discovers concepts it can reliably identify, eventually leading
to a vocabulary with thousands of labels and growing. We
believe that this work significantly extends the state of the art
in multimedia data mining, discovery, and organization based
on the technical merit of the proposed ideas as well as the
enormous scale of the mining exercise in a very challenging,
unconstrained, noisy domain.
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3D-aided 2D Face Recognition
George Passalis
Stefanos Zafireiou
Georgios Tzimiropoulos
Maria Petrou
Theoharis Theoharis
Ioannis A. Kakadiaris
Computer Vision and Pattern Recognition, IEEE (2010)
Ethnicity- and gender-based subject retrieval using 3-D face-recognition techniques
Sean M. O'Malley
George Passalis
Theoharis Theoharis
Ioannis A. Kakadiaris
International Journal of Computer Vision, vol. 89, Issue 2 (2010), pp. 382-391
Unified 3D Face and Ear Recognition using Wavelets on Geometry Images
Theoharis Theoharis
Georgios Passalis
Ioannis A. Kakadiaris
Pattern Recognition, vol. 41 Issue 3 (2008), pp. 796-804
General Voxelization Algorithm with Scalable GPU Implementation
Georgios Passalis
Theoharis Theoharis
Ioannis A. Kakadiaris
Journal of Graphics Tools, vol. 12, Num. 1 (2007), pp. 61-71
3D face recognition in the presence of facial expressions: an annotated deformable model approach
Ioannis A. Kakadiaris
Georgios Passalis
Najam Murtuza
Nikos Karampatziakis
Theoharis Theoharis
Pattern Analysis and Machine Intelligence, vol. 13, Num. 12 (2007)
An Automated Method for Human Face Modeling and Relighting with Application to Face Recognition
Georgios Passalis
Theoharis Theoharis
Ioannis A. Kakadiaris
Proceedings of the First International Workshop on Photometric Analysis For Computer Vision (2007)
Expression-invariant multispectral face recognition: You can smile now!
Ioannis A. Kakadiaris
Georgios Passalis
Yunliang Lu
Nikos Karampatziakis
Najam Murtuza
Theoharis Theoharis
Biometric Technology for Human Identification III, Defense and Security Symposium (2006)
3D Face Recognition
Ioannis A. Kakadiaris
Georgios Passalis
Najam Murtuza
Theoharis Theoharis
British Machine Vision Conference (2006)
Multimodal face recognition: combination of geometry with physiological information
Ioannis A. Kakadiaris
Georgios Passalis
Theoharis Theoharis
Ioannis Konstantinidis
Najam Murtuza
Computer Vision and Pattern Recognition Conference (2005)
Evaluation of variability and significance of fundus camera lens distortion
Adol Esquivel
Helen K. Li
Ioannis A. Kakadiaris
International Conference of the IEEE Engineering in Medicine and Biology (2004)
