Reimagining Reality: A Comprehensive Survey of Video Inpainting Techniques

This paper offers a comprehensive analysis of recent advancements in video inpainting techniques, a critical subset of computer vision and artificial intelligence. As a process that restores or fills in missing or corrupted portions of video sequences with plausible content, video inpainting has evolved significantly with the advent of deep learning methodologies. Despite the plethora of existing methods and their swift development, the landscape remains complex, posing challenges to both novices and established researchers. Our study deconstructs major techniques, their underpinning theories, and their effective applications. Moreover, we conduct an exhaustive comparative study, centering on two often-overlooked dimensions: visual quality and computational efficiency. We adopt a human-centric approach to assess visual quality, enlisting a panel of annotators to evaluate the output of different video inpainting techniques. This provides a nuanced qualitative understanding that complements traditional quantitative metrics. Concurrently, we delve into the computational aspects, comparing inference times and memory demands across a standardized hardware setup. This analysis underscores the balance between quality and efficiency: a critical consideration for practical applications where resources may be constrained. By integrating human validation and computational resource comparison, this survey not only clarifies the present landscape of video inpainting techniques but also charts a course for future explorations in this vibrant and evolving field

Natural Dye Extracted from Vitex negundo as a Potential Alternative to Synthetic Dyes for Dyeing of Silk

Since the last decade, the application of natural dyes on textile material has been gaining popularity all over the world, possibly because of the increasing awareness of issues concerning the environment, ecology and pollution control. The present paper investigates extraction of natural dye from leaves of the plant Vitex negundo,which is an abundant, cheap, and readily available agricultural by-product. Water extracts from V. negundo was used to dye silk fabrics. Optimum extraction conditions included pH 9, duration 120 min, and temperature 90 C. Optimum dyeing conditions included dyeing pH 5 and duration of 60 min. Potash alum, tannic and tartaric acid were used as mordants, all of which are benign to human health and the environment. Color strength and color coordinates in terms of L*, a*, b*, C, and h were examined. A range of shades were obtained when fabrics were dyed with different mordants and mordanting techniques. The extracted dye was tested for some of the eco-parameters using atomic absorption spectrophotometry and GC/MS. The test results were compared with set standards to determine the eco-friendliness of natural dye. Their concentrations were found to be lower than the stipulated limits. Dyed samples were tested for antimicrobial activity against gram-positive and gram-negative bacteria. The dyed silk fabrics showed acceptable fastness properties and were also found to possess antibacterial activity. It can be concluded that the abundantly available agricultural byproduct V. negundo has great potential to be effectively utilized as a natural dye for silk

Resource efficient action recognition in videos

This thesis traces an innovative journey in the domain of real-world action recognition, in particular focusing on memory and data efficient systems. It begins by introducing a novel approach for smart frame selection, which significantly reduces computational costs in video classification. It further optimizes the action recognition process by addressing the challenges of training time and memory consumption in video transformers, laying a strong foundation for memory efficient action recognition. The thesis then delves into zero-shot learning, focusing on the flaws of the currently existing protocol and establishing a new split for true zero-shot action recognition, ensuring zero overlap between unseen test classes and training or pre-training classes. Building on this, a unique cluster-based representation, optimized using reinforcement learning, is proposed for zero-shot action recognition. Crucially, we show that a joint visual-semantic representation learning is essential for improved performance. We also experiment with feature generation approaches for zero-shot action recognition by introducing a synthetic sample selection methodology extending the utility of zero-shot learning to both images and videos and selecting high-quality samples for synthetic data augmentation. This form of data valuation is then incorporated for our novel video data augmentation approach where we generate video composites using foreground and background mixing of videos. The data valuation helps us choose good composites at a reduced overall cost. Finally, we propose the creation of a meaningful semantic space for action labels. We create a textual description dataset for each action class and propose a novel feature generating approach to maximise the benefits of this semantic space. The research contributes significantly to the field, potentially paving the way for more efficient, resource-friendly, and robust video processing and understanding techniques

FE-Adapter: adapting image-based emotion classifiers to videos

Utilizing large pre-trained models for specific tasks has yielded impressive results. However, fully finetuning these increasingly large models is becoming prohibitively resource-intensive. This has led to a focus on more parameterefficient transfer learning, primarily within the same modality. But this approach has limitations, particularly in video understanding where suitable pre-trained models are less common. Addressing this, our study introduces a novel cross-modality transfer learning approach from images to videos, which we call parameter-efficient image-to-video transfer learning. We present the Facial-Emotion Adapter (FE-Adapter), designed for efficient fine-tuning in video tasks. This adapter allows pre-trained image models, which traditionally lack temporal processing capabilities, to analyze dynamic video content efficiently. Notably, it uses about 15 times fewer parameters than previous methods, while improving accuracy. Our experiments in video emotion recognition demonstrate that the FE-Adapter can match or even surpass existing fine-tuning and video emotion models in both performance and efficiency. This breakthrough highlights the potential for cross-modality approaches in enhancing the capabilities of AI models, particularly in fields like video emotion analysis where the demand for efficiency and accuracy is constantly rising

Dyeing Properties of Natural Dye Syzygium Cuminii on Silk

Dyeing behavior of natural dye extracted from the bark of Syzygium cuminii L has been studied on silk fabric. Colour values and colour co-ordinates were examined in terms of K/S and L* a* b* C and h. A range of shades were obtained by using various mordants and mordanting techniques. Dye was tested for some of the eco-parameters using atomic absorption spectrophotometry and GC/MS. The test results were compared with the set standards to determine the eco-friendliness of natural dye. Their concentrations were much below the stipulated limits. Dyed samples were tested for antimicrobial activity against Gram-positive and Gram-negative bacteria and were found to possess antibacterial activit

Watt For What: Rethinking Deep Learning's Energy-Performance Relationship

Deep learning models have revolutionized various fields, from image recognition to natural language processing, by achieving unprecedented levels of accuracy. However, their increasing energy consumption has raised concerns about their environmental impact, disadvantaging smaller entities in research and exacerbating global energy consumption. In this paper, we explore the trade-off between model accuracy and electricity consumption, proposing a metric that penalizes large consumption of electricity. We conduct a comprehensive study on the electricity consumption of various deep learning models across different GPUs, presenting a detailed analysis of their accuracy-efficiency trade-offs. By evaluating accuracy per unit of electricity consumed, we demonstrate how smaller, more energy-efficient models can significantly expedite research while mitigating environmental concerns. Our results highlight the potential for a more sustainable approach to deep learning, emphasizing the importance of optimizing models for efficiency. This research also contributes to a more equitable research landscape, where smaller entities can compete effectively with larger counterparts. This advocates for the adoption of efficient deep learning practices to reduce electricity consumption, safeguarding the environment for future generations whilst also helping ensure a fairer competitive landscape

Dyeing of silk using Madhuca longifolia as natural dye source

The dried leaves of Madhuca longifolia has been evaluated for their potential as a source for natural dyeing of silk. Dye has been extracted under optimum conditions such as extraction pH (10), time (60 min) and temperature (95̊°C). The extracted dye has been applied on the silk fabrics and a range of shades are obtained using different methods with or without using mordants. It is found that mordants have a significant effect on the color of dyed silk fabrics. The dyed samples have been evaluated for color measurements and standard wash, light and rub fastness tests. The extracted dye is also tested for some of the eco-parameters using atomic absorption spectrophotometry and GC/MS. The test results are compared with set standards to determine the eco-friendliness of natural dye. Their concentrations are found to be lower than the stipulated limits. The dyed samples are also tested for antimicrobial activity against Gram-positive and Gram-negative bacteria. The dyed silk fabrics show acceptable fastness properties and are found to possess antibacterial activity. The results show that Madhuca longifolia leaves are promising as a natural colorant, which would, in turn, pave the way for the discovery of a new range of environment-friendly dyes for textile materials

Effect of Packaging and Storage Temperature on Shelf-Life of Minimally Processed Onion (Allium cepa L.)

Minimally processed onion is a ready-to-use onion product offering the consumer a fully usable commodity, without much change to freshness of the produce. Effect of packaging and storage temperature on shelf-life in minimally processed onion was studied. Packaging and temperature play an important role in determining shelf-life in minimally processed onion. Onion pieces approx. 8-10mm thick were cut with a plain, sharp knife and subjected to dip-treatment with the firming agent, calcium lactate (2%), for 5 minutes. The samples were surface-dried and packaged in polypropylene bags of size 250 X 125mm, of variable thicknesses (25, 50 or 75μm) and stored at low temperatures and high RH:8±1°C and 83±2% RH; 10±1°C and 82±2% RH; and, 12±1°C and 80±2% RH. It was found that onion cv. Arka Sona sliced with a plain, sharp knife, pre-treated with 2% calcium lactate, surface-dried and packaged in polypropylene bags sized 250X125mm (50μm thick), and stored at 8+1°C and 83±2% RH retained freshness and nutritive value, were microbially safe and acceptable, with a shelf-life of 14 days at storage