The Decade of the Auteurs: The Institutional Reorganization of the Romanian Film Industry in the 1990s

Romanian cinema in the 1990s was defined, among others, by its failed attempt at institutional reorganization, due to which fewer and fewer films were released towards the end of the decade - a process which culminated in 2000, when not a single feature film was released. However, before this virtual collapse of the Romanian film industry, sixty or so films were financed and produced. By taking a look at their opening credits, one would be perhaps surprised to notice mostly familiar names - directors and writers which were highly prominent during the communist era. In cinema, as in other cultural fields, the cultural elites managed, at the beginning of the 1990s, to use their cultural capital gained during the communist years in order to take over the industry. The films made during this transitional period were ideologically conservative, rich in anticommunist rhetoric and - paradoxically - financed and produced using a state-sponsored infrastructure developed two decades earlier, during Nicolae Ceaușescu's regime. Taking into account the long-lasting institutional transformation of the Romanian film industry and the critical reception of Romanian films before and after 1989, this article tries to offer a context for the processes taking shape in the 1990s and to suggest the main causes for the postcommunist reconfiguration of the cultural field, due to which mainly one kind of anticommunist rhetoric gained visibility during this decade

Chi-Squared Distance and Metamorphic Virus Detection

Malware are programs that are designed with a malicious intent. Metamorphic malware change their internal structure each generation while still maintaining their original behavior. As metamorphic malware become more sophisticated, it is important to develop efficient and accurate detection techniques. Current commercial antivirus software generally try to scan for malware signatures within files and match them against a known set of signatures; therefore, they are not able to detect metamorphic malware that change their body from generation to generation, with each copy comprised of its own unique signature. Machine learning methods such as hidden Markov models (HMM) have shown promising results in detecting metamorphic malware. However, it is possible to exploit a weakness in HMMs and avoid detection by morphing and merging the malware with contents from normal files. As an alternative approach, we consider combining HMMs with the statistical framework of the chi-squared test to build a new detection method. This paper will present the experimental results of our proposed hybrid detector in metamorphic malware detection

Pose Embeddings: A Deep Architecture for Learning to Match Human Poses

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

Full Resolution Image Compression with Recurrent Neural Networks

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.Comment: Updated with content for CVPR and removed supplemental material to an external link for size limitation

Beyond Short Snippets: Deep Networks for Video Classification

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%)

Late Modernist Noirs: Béla Tarr’s Damnation/ Kárhozat and György Fehér’s Passion/ Szenvedély

Since the 80s, a large number of films, manifestly indebted to the classic American noir films of the 40s and 50s, have been appropriately labeled neo-noirs. An interesting, but less well documented version of this phenomenon, mostly American in its nature, is the case of some of the films belonging to the so-called Hungarian “Black Series”. Made at the end of the 80s and during the 90s, these films are modernist, stylized versions of the classic noir films. This essay tries to give an outline of this East European reappraisal of the noir film, by insisting on the narrative and aesthetical strategies used by directors such as Béla Tarr or György Fehér in order to deconstruct the classical genre

Multi-Realism Image Compression with a Conditional Generator

By optimizing the rate-distortion-realism trade-off, generative compression approaches produce detailed, realistic images, even at low bit rates, instead of the blurry reconstructions produced by rate-distortion optimized models. However, previous methods do not explicitly control how much detail is synthesized, which results in a common criticism of these methods: users might be worried that a misleading reconstruction far from the input image is generated. In this work, we alleviate these concerns by training a decoder that can bridge the two regimes and navigate the distortion-realism trade-off. From a single compressed representation, the receiver can decide to either reconstruct a low mean squared error reconstruction that is close to the input, a realistic reconstruction with high perceptual quality, or anything in between. With our method, we set a new state-of-the-art in distortion-realism, pushing the frontier of achievable distortion-realism pairs, i.e., our method achieves better distortions at high realism and better realism at low distortion than ever before