3,399 research outputs found
RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model
Text-to-image generation (TTI) refers to the usage of models that could
process text input and generate high fidelity images based on text
descriptions. Text-to-image generation using neural networks could be traced
back to the emergence of Generative Adversial Network (GAN), followed by the
autoregressive Transformer. Diffusion models are one prominent type of
generative model used for the generation of images through the systematic
introduction of noises with repeating steps. As an effect of the impressive
results of diffusion models on image synthesis, it has been cemented as the
major image decoder used by text-to-image models and brought text-to-image
generation to the forefront of machine-learning (ML) research. In the era of
large models, scaling up model size and the integration with large language
models have further improved the performance of TTI models, resulting the
generation result nearly indistinguishable from real-world images,
revolutionizing the way we retrieval images. Our explorative study has
incentivised us to think that there are further ways of scaling text-to-image
models with the combination of innovative model architectures and prediction
enhancement techniques. We have divided the work of this survey into five main
sections wherein we detail the frameworks of major literature in order to delve
into the different types of text-to-image generation methods. Following this we
provide a detailed comparison and critique of these methods and offer possible
pathways of improvement for future work. In the future work, we argue that TTI
development could yield impressive productivity improvements for creation,
particularly in the context of the AIGC era, and could be extended to more
complex tasks such as video generation and 3D generation
A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images
Predictive coding is attractive for compression onboard of spacecrafts thanks
to its low computational complexity, modest memory requirements and the ability
to accurately control quality on a pixel-by-pixel basis. Traditionally,
predictive compression focused on the lossless and near-lossless modes of
operation where the maximum error can be bounded but the rate of the compressed
image is variable. Rate control is considered a challenging problem for
predictive encoders due to the dependencies between quantization and prediction
in the feedback loop, and the lack of a signal representation that packs the
signal's energy into few coefficients. In this paper, we show that it is
possible to design a rate control scheme intended for onboard implementation.
In particular, we propose a general framework to select quantizers in each
spatial and spectral region of an image so as to achieve the desired target
rate while minimizing distortion. The rate control algorithm allows to achieve
lossy, near-lossless compression, and any in-between type of compression, e.g.,
lossy compression with a near-lossless constraint. While this framework is
independent of the specific predictor used, in order to show its performance,
in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless
compression standard, obtaining an extension that allows to perform lossless,
near-lossless and lossy compression in a single package. We show that the rate
controller has excellent performance in terms of accuracy in the output rate,
rate-distortion characteristics and is extremely competitive with respect to
state-of-the-art transform coding
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
Pigment Melanin: Pattern for Iris Recognition
Recognition of iris based on Visible Light (VL) imaging is a difficult
problem because of the light reflection from the cornea. Nonetheless, pigment
melanin provides a rich feature source in VL, unavailable in Near-Infrared
(NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical
not stimulated in NIR. In this case, a plausible solution to observe such
patterns may be provided by an adaptive procedure using a variational technique
on the image histogram. To describe the patterns, a shape analysis method is
used to derive feature-code for each subject. An important question is how much
the melanin patterns, extracted from VL, are independent of iris texture in
NIR. With this question in mind, the present investigation proposes fusion of
features extracted from NIR and VL to boost the recognition performance. We
have collected our own database (UTIRIS) consisting of both NIR and VL images
of 158 eyes of 79 individuals. This investigation demonstrates that the
proposed algorithm is highly sensitive to the patterns of cromophores and
improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on
Instruments and Measurements, Volume 59, Issue number 4, April 201
Olfaction, Emtion & the Amygdala: arousal-dependent modulation of long-term autobiographical memory and its association with olfaction
The sense of smell is set apart from other sensory modalities. Odours possess the capacity to trigger immediately strong emotional memories. Moreover, odorous stimuli provide a higher degree of memory retention than other sensory stimuli. Odour perception, even in its most elemental form - olfaction - already involves limbic structures. This early involvement is not paralleled in other sensory modalities. Bearing in mind the considerable connectivity with limbic structures, and the fact that an activation of the amygdala is capable of instantaneously evoking emotions and facilitating the encoding of memories, it is unsurprising that the sense of smell has its characteristic nature. The aim of this review is to analyse current understanding of higher olfactory information processing as it relates to the ability of odours to spontaneously cue highly vivid, affectively toned, and often very old autobiographical memories (episodes known anecdotally as Proust phenomena). Particular emphasis is placed on the diversity of functions attributed to the amygdala. Its role in modulating the encoding and retrieval of long-term memory is investigated with reference to lesion, electrophysiological, immediate early gene, and functional imaging studies in both rodents and humans. Additionally, the influence of hormonal modulation and the adrenergic system on emotional memory storage is outlined. I finish by proposing a schematic of some of the critical neural pathways that underlie the odour-associated encoding and retrieval of emotionally toned autobiographical memories
- ā¦