Enhancing Perceptual Attributes with Bayesian Style Generation

Deep learning has brought an unprecedented progress in computer vision and significant advances have been made in predicting subjective properties inherent to visual data (e.g., memorability, aesthetic quality, evoked emotions, etc.). Recently, some research works have even proposed deep learning approaches to modify images such as to appropriately alter these properties. Following this research line, this paper introduces a novel deep learning framework for synthesizing images in order to enhance a predefined perceptual attribute. Our approach takes as input a natural image and exploits recent models for deep style transfer and generative adversarial networks to change its style in order to modify a specific high-level attribute. Differently from previous works focusing on enhancing a specific property of a visual content, we propose a general framework and demonstrate its effectiveness in two use cases, i.e. increasing image memorability and generating scary pictures. We evaluate the proposed approach on publicly available benchmarks, demonstrating its advantages over state of the art methods.Comment: ACCV-201

Twin Constellations: Parallelism and Stance in Stand-Up Comedy

This paper addresses the interrelations between poetic parallelism and interactional stance-taking in stand-up comedy by examining commercially edited recordings of stand-up routines performed by two contemporary comics. Methodologically, the article suggests a heuristic distinction between 1) an approach to parallelism as a textual and rhetorical device based on sequential repetition of units of expression, and 2) a more positional or symbolic orientation that conceptualizes parallelism as a higher-order structural and functional principle. It is concluded that both types rely on iconic mappings across co-textual signs. The flexibility of parallelism is simultaneously proposed as affording diversity on the level of discursive presentation.Abstract from website.Antti Lindfors is a Ph.D. candidate of Folkloristics at the University of Turku, Finland. His doctoral dissertation on the poetics and performance of stand-up comedy, addressing stand-up from the perspectives of narration and gestures, satire and ethics, as well as confession

Phonetic variation of f 0 range in L1 and L2 : a comparison between Italian, English and Spanish native and non-native speakers

This work was carried out with the purpose of investigating the use of language-specific features of pitch span and level in L2. Different languages were investigated: on the one hand, we analysed productions in L2 Spanish and English, uttered by Italian learners with different proficiency levels; on the other hand, we analysed productions in L2 Italian uttered by Spanish and English speakers. The results show a very heterogeneous situation: to some extent, learners seem to be sensitive to f 0 excursion and modulation of the L2 input they receive; however, these intonational features of Target Language speech: i) are out of non-native speaker’s control, ii) do not affect all the aspects of L2 productions, and iii) present a high degree of inter-speaker variability

Simple wealth distribution model causing inequality-induced crisis without external shocks

We address the issue of the dynamics of wealth accumulation and economic crisis triggered by extreme inequality, attempting to stick to most possibly intrinsic assumptions. Our general framework is that of pure or modified multiplicative processes, basically geometric Brownian motions. In contrast with the usual approach of injecting into such stochastic agent models either specific, idiosyncratic internal nonlinear interaction patterns, or macroscopic disruptive features, we propose a dynamic inequality model where the attainment of a sizable fraction of the total wealth by very few agents induces a crisis regime with strong intermittency, the explicit coupling between the richest and the rest being a mere normalization mechanism, hence with minimal extrinsic assumptions. The model thus harnesses the recognized lack of ergodicity of geometric Brownian motions. It also provides a statistical intuition to the consequences of Thomas Piketty's recent "$r>g$" (return rate $>$ growth rate) paradigmatic analysis of very-long-term wealth trends. We suggest that the "water-divide" of wealth flow may define effective classes, making an objective entry point to calibrate the model. Consistently, we check that a tax mechanism associated to a few percent relative bias on elementary daily transactions is able to slow or stop the build-up of large wealth. When extreme fluctuations are tamed down to a stationary regime with sizable but steadier inequalities, it should still offer opportunities to study the dynamics of crisis and the inner effective classes induced through external or internal factors.Comment: 15 pages, 11 figures. Work initiated from discussion on Aristotle's status revisited by Paul Jorion in the many cases where the law of supply and demand fails. Accepted for publication in Physical Review E on April 19, 201

ISS++: Image as Stepping Stone for Text-Guided 3D Shape Generation

In this paper, we present a new text-guided 3D shape generation approach (ISS++) that uses images as a stepping stone to bridge the gap between text and shape modalities for generating 3D shapes without requiring paired text and 3D data. The core of our approach is a two-stage feature-space alignment strategy that leverages a pre-trained single-view reconstruction (SVR) model to map CLIP features to shapes: to begin with, map the CLIP image feature to the detail-rich 3D shape space of the SVR model, then map the CLIP text feature to the 3D shape space through encouraging the CLIP-consistency between rendered images and the input text. Besides, to extend beyond the generative capability of the SVR model, we design a text-guided 3D shape stylization module that can enhance the output shapes with novel structures and textures. Further, we exploit pre-trained text-to-image diffusion models to enhance the generative diversity, fidelity, and stylization capability. Our approach is generic, flexible, and scalable, and it can be easily integrated with various SVR models to expand the generative space and improve the generative fidelity. Extensive experimental results demonstrate that our approach outperforms the state-of-the-art methods in terms of generative quality and consistency with the input text. Codes and models are released at https://github.com/liuzhengzhe/ISS-Image-as-Stepping-Stone-for-Text-Guided-3D-Shape-Generation.Comment: Under review of TPAM