Abstract

We present a new additive image factorization technique that treats images to be composed of multiple latent specular components which can be simply estimated recursively by modulating the sparsity during decomposition. Our model-driven {\em RSFNet} estimates these factors by unrolling the optimization into network layers requiring only a few scalars to be learned. The resultant factors are interpretable by design and can be fused for different image enhancement tasks via a network or combined directly by the user in a controllable fashion. Based on RSFNet, we detail a zero-reference Low Light Enhancement (LLE) application trained without paired or unpaired supervision. Our system improves the state-of-the-art performance on standard benchmarks and achieves better generalization on multiple other datasets. We also integrate our factors with other task specific fusion networks for applications like deraining, deblurring and dehazing with negligible overhead thereby highlighting the multi-domain and multi-task generalizability of our proposed RSFNet. The code and data is released for reproducibility on the project homepage.

Abstract

This edited volume presents perspectives from computer science, information theory, neuroscience and brain imaging, aesthetics, social sciences, psychiatry, and philosophy to answer frontier questions related to artificial intelligence and human experience. Can a machine think, believe, aspire and be purposeful as a human? What is the place in the machine world for hope, meaning and transformative enlightenment that inspires human existence? How, or are, the minds of machines different from that of humans and other species? These questions are responded to along with questions in the intersection of health, intelligence and the brain. It highlights the place of consciousness by attempting to respond to questions with the help of fundamental reflections on human existence, its life-purposes and machine intelligence.

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The philosophical discourses on AI have profound impact on the traditional philosophical notions such as human intelligence, mind-world relationship, and the good-life. The new terminologies like “Posthumanism”, “Transhumanism”, and “Cyborg” have been problematizing the notion of the ‘human’ itself as it is being claimed by a few that the arrival of AI would finally erase the boundaries between humans and machines. Posthuman philosophers argue that the ‘end of humanism’ started by the postmodern philosophers would reach its zenith in AI. In this chapter, we will examine the notion of ‘intelligence’ in AI through the phenomenological inquiry into the nature of human existence and would inquire whether the metanarrative of AI would encompass the embodied intelligence as found in humans

Abstract

Solar absorption spectra collected using a ground-based Fourier Transform Infrared (FTIR) Spectrometer were used to retrieve the column-averaged concentrations (X) of CO2, CH4, CO, and N2O during the years 2016–2019 in the Suburban region of Shadnagar, Telangana, India. For the research period, the diurnal mean amplitudes of XCO2, XCH4, XCO, and XN2O are ∼ 13 ppmv, ∼50 ppbv, ∼13 ppbv, and ∼ 19 ppbv respectively. Strong seasonality at the study site is indicated by the seasonal variations of XCO2 and XCO of 18.3 ppmv and 39.2 ppbv, respectively. Additionally, we compared FTIR data to XCO2 detected by Orbiting Carbon Observatory-2 (OCO-2) and CO columns from Measurements of Pollution in the Troposphere (MOPITT). For the XCO2 and CO columns, the comparison research shows a mean relative bias between FTIR data and satellite data of 0.92 % and 0.71 %, respectively. The FTIR and satellite data show a Pearson correlation coefficient (r) of 0.71 (XCO2, N = 26 co-located) and 0.60 (CO columns, N = 44), respectively. The findings demonstrate a good agreement between our data and satellite data.

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Abstract

The convergence of biotechnology and artificial intelligence has the potential to transform drug development, especially in the field of therapeutic peptide design. Peptides are short chains of amino acids with diverse therapeutic applications that offer several advantages over small molecular drugs, such as targeted therapy and minimal side effects. However, limited oral bioavailability and enzymatic degradation have limited their effectiveness. With advances in deep learning techniques, innovative approaches to peptide design have become possible. In this work, we demonstrate HYDRA: a hybrid deep learning approach that leverages the distribution modeling capabilities of a diffusion model and combines it with a binding affinity maximization algorithm that can be used for de novo design of peptide binders given target receptors. As an application, we have used our approach to design therapeutic peptides targeting proteins expressed by Plasmodium falciparum Erythrocyte Membrane Protein 1 (PfEMP1) genes. The ability of our model to generate peptides conditioned on the target receptor’s binding sites makes it a promising approach for developing effective therapies for malaria and other diseases.

Abstract

Large-scale deployment of fully autonomous vehicles requires a very high degree of robustness to unstructured traffic, weather conditions, and should prevent unsafe mispredictions. While there are several datasets and benchmarks focusing on segmentation for drive scenes, they are not specifically focused on safety and robustness issues. We introduce the IDD-AW dataset, which provides 5000 pairs of high-quality images with pixel-level annotations, captured under rain, fog, low light, and snow in unstructured driving conditions. As compared to other adverse weather datasets, we provide i.) more annotated images, ii.) paired Near-Infrared (NIR) image for each frame, iii.) larger label set with a 4-level label hierarchy to capture unstructured traffic conditions. We benchmark state-of-the-art models for semantic segmentation in IDD-AW. We also propose a new metric called “Safe mean Intersection over Union (Safe mIoU)” for hierarchical datasets which penalizes dangerous mispredictions that are not captured in the traditional definition of mean Intersection over Union (mIoU). The results show that IDD-AW is one of the most challenging datasets to date for these tasks. The dataset and code will be available here: http://iddaw.github.io.

Abstract

We introduce multimodal story summarization by leveraging TV episode recaps – short video sequences interweaving key visual moments and dialog from previous episodes to bring viewers up to speed. We propose PlotSnap, a dataset featuring two crime thriller TV shows with rich recaps, and long-form content over 40 minutes per episode. Recap shots are mapped to corresponding sub-stories that serve as labels for story summarization. We propose a hierarchical model TaleSumm that (i) processes entire episodes by creating compact shot and dialog representations, and (ii) predicts importance scores for each video shot and dialog utterance by enabling interactions between local story groups. Unlike traditional summarization tasks, our method extracts multiple plot points from long-form videos. We present a thorough evaluation on this task, including promising cross-series generalization. TaleSumm shows good results on video summarization benchmarks.

Abstract

Indian folk paintings have a rich mosaic of symbols, colors, textures, and stories making them an invaluable repository of cultural legacy. The paper presents a novel approach to classifying these paintings into distinct art forms and tagging them with their unique salient features. A custom dataset named FolkTalent, comprising 2279 digital images of paintings across 12 different forms, has been prepared using websites that are direct outlets of Indian folk paintings. Tags covering a wide range of attributes like color, theme, artistic style, and patterns are generated using GPT4, and verified by an expert for each painting. Classification is performed employing the RandomForest ensemble technique on fine-tuned Convolutional Neural Network (CNN) models to classify Indian folk paintings, achieving an accuracy of 91.83%. Tagging is accomplished via the prominent fine-tuned CNN-based backbones with a custom classifier attached to its top to perform multi-label image classification. The generated tags offer a deeper insight into the painting, enabling an enhanced search experience based on theme and visual attributes. The proposed hybrid model sets a new benchmark in folk painting classification and tagging, significantly contributing to cataloging India’s folk-art heritage.