Character Sequence Models for ColorfulWords

We present a neural network architecture to predict a point in color space from the sequence of characters in the color's name. Using large scale color--name pairs obtained from an online color design forum, we evaluate our model on a "color Turing test" and find that, given a name, the colors predicted by our model are preferred by annotators to color names created by humans. Our datasets and demo system are available online at colorlab.us

New Waves, New Spaces: Estonian Experimental Cinema of the 1970s

Using the label of “new wave” in the context of Estonian cinema is highly problematic and controversial because, unlike in France or, to take a more similar socio-political framework, in Czechoslovakia, the (Soviet) Estonian filmic arena did not see a creative outburst synchronous with and comparable to, both in scope of innovative production and international acclaim, the cinematic practices adorned with the adjective “new” elsewhere in Europe. While the heyday of various new waves, both in Western Europe and in the Soviet bloc, is normally limited to the period between the mid-1950s and the ruptures of 1968, in Estonia, as the local literary critic Mart Velsker (1999: 1211) has accurately argued, the essence of the innovative 1960s “is manifested in its most vivid form some time between 1968 and 1972, that is, at the end of the decade and partly even beyond it.” Compared to other artistic genres, however, Estonian cinema was severely lagging behind, both in achievement and in reputation. The true “Estonian New Wave” has been defined by local critics as born and burgeoning in the end of the 1970s and the beginning of the 1980s (Orav 2003: 54ff; Kärk 1995: 117; Kirt 1980: 33-4), when a new generation of young filmmakers entered the stagnated cinematic stage with bravado, finally inverting the low ebb that had lasted nearly a decade. Yet, in the midst of the ebbing waters of the early 1970s, a dark horse emerged, whose artistic contribution to Estonian cinematic heritage deserves to be identified as a new wave in miniature, a veritable diamond, albeit perhaps rough-cut. This author was Jaan Tooming, an actor and a theatre director, whose films constitute a fundamentally unprecedented phenomenon in Estonian cinema. His controversial, stylistically and semantically rich output, composed of unceasingly intriguing visual utterances, provides a fascinating order of spatial representations, which reconfigure Estonian cinematic territories in several respects and, at the same time, re-evaluate and criticize quite provocatively the historical and conceptual framework of imagining national, social and personal identities. The following investigation of Tooming’s films will concentrate chiefly on the spatial representations and practices, with digressions into the domain of re/constructing identities, both personal and collective

The Mountains Are Calling

Mark Knapp’s Relationship Model in Communication Studies attempts to limit human relationships to a specific, linear array of stages and categories. This research project attempts to critique the rigidity of Knapp’s model, while simultaneously attempting to posit film as an incredibly influential tool (if not an alternative model entirely) for communication both diegetically – within the realm of the film – and in conversation with an audience. The silent nature of the creative filmed portion of this project is in direct opposition to Knapp’s model, which inadvertently roots human relationships in language

Fine-graind Image Classification via Combining Vision and Language

Fine-grained image classification is a challenging task due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to the same basic-level category. Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy. Despite achieving promising results, these methods mainly have two limitations: (1) not all the parts which obtained through the part detection models are beneficial and indispensable for classification, and (2) fine-grained image classification requires more detailed visual descriptions which could not be provided by the part locations or attribute annotations. For addressing the above two limitations, this paper proposes the two-stream model combining vision and language (CVL) for learning latent semantic representations. The vision stream learns deep representations from the original visual information via deep convolutional neural network. The language stream utilizes the natural language descriptions which could point out the discriminative parts or characteristics for each image, and provides a flexible and compact way of encoding the salient visual aspects for distinguishing sub-categories. Since the two streams are complementary, combining the two streams can further achieves better classification accuracy. Comparing with 12 state-of-the-art methods on the widely used CUB-200-2011 dataset for fine-grained image classification, the experimental results demonstrate our CVL approach achieves the best performance.Comment: 9 pages, to appear in CVPR 201

Scene extraction in motion pictures

This paper addresses the challenge of bridging the semantic gap between the rich meaning users desire when they query to locate and browse media and the shallowness of media descriptions that can be computed in today\u27s content management systems. To facilitate high-level semantics-based content annotation and interpretation, we tackle the problem of automatic decomposition of motion pictures into meaningful story units, namely scenes. Since a scene is a complicated and subjective concept, we first propose guidelines from fill production to determine when a scene change occurs. We then investigate different rules and conventions followed as part of Fill Grammar that would guide and shape an algorithmic solution for determining a scene. Two different techniques using intershot analysis are proposed as solutions in this paper. In addition, we present different refinement mechanisms, such as film-punctuation detection founded on Film Grammar, to further improve the results. These refinement techniques demonstrate significant improvements in overall performance. Furthermore, we analyze errors in the context of film-production techniques, which offer useful insights into the limitations of our method

Eileen Chang and cinema

The death of Eileen Chang on September 8, 1995 in Los Angeles made headlines in all the Chinese newspapers. In the Chinese-speaking areas of Taiwan, Hong Kong, and Mainland China, a veritable cult of mystique has been built around her by both public media and the large number of her fans (who called themselves Chang-mi or Chang-fans”). However, in the last twenty-three years of her life Chang lived quietly and incognito in Los Angeles, shunning all social contact and escaping publicity by constantly changing her residences in numerous hotels, motels, and small apartment houses until her death in an obscure apartment building in the Westwood section of Los Angeles. This “mystery” of her last years adds only more glamour to her legend: she was like a retired movie star past her prime, like Greta Garbo