Abstract

Deep learning models are susceptible to backdoor attacks involving malicious attackers perturbing a small subset of training data with a trigger to causes misclassifications. Various triggers have been used including semantic triggers that are easily realizable without requiring attacker to manipulate the image. The emergence of generative AI has eased generation of varied poisoned samples. Robustness across types of triggers is crucial to effective defense. We propose Prototype Guided Backdoor Defense (PGBD), a robust post-hoc defense that scales across different trigger types, including previously unsolved semantic triggers. PGBD exploits displacements in the geometric spaces of activations to penalize movements towards the trigger. This is done using a novel sanitization loss of a post-hoc finetuning step. The geometric approach scales easily to all types of attacks. PGBD achieves better performance across all settings. We also present the first defense against a new semantic attack on celebrity face images.

Abstract

Centrality measures offer a mechanism to quantify various notions of the importance of vertices in a network. These measures find application in social network analysis, the study of disease spread, and the like. Since the networks of interest are usually large, parallelism has become a powerful tool for computing these measures. However, for some of these applications, it is crucial to study how these centrality values change when there are changes to the underlying network. As a result, parallel algorithms that examine how to update these values due to changes to the graph are gaining research interest. In this paper, we study the update of the percolation centrality measure in a dynamic graph. We present parallel algorithms to handle changes to the percolation value of a batch of vertices and the addition and deletion of a batch of edges to the graph. We leverage the graphs' structural properties to improve our algorithms' performance. To our knowledge, we are the first to propose such algorithms for percolation centrality. We implement and benchmark our dynamic algorithms on a server with two AMD EPYC CPUs and an Nvidia A100 GPU. Our experiments on a collection of real-world graphs indicate that our algorithms achieve speedups of 8.79x and 2.82x on CPU and GPU, respectively, for edge updates, and 309.44x and 18.71x on CPU and GPU, respectively, for vertex percolation updates over state-of-the-art static algorithms for a batch of 10000 edges and vertices, respectively.

Abstract

Imagine sitting in a presentation, trying to follow the speaker while simultaneously scanning the slides for relevant information. While the entire slide is visible, identifying the relevant regions can be challenging. As you focus on one part of the slide, the speaker moves on to a new sentence, leaving you scrambling to catch up visually. This constant back-and-forth creates a disconnect between what’s being said and the most important visual elements, making it hard to absorb key details—especially in fast-paced or content-heavy presentations, as in a conference talk. This will require an understanding of slides, especially text, graphics, and layout. We introduce a method that automatically identifies and highlights the most relevant slide regions based on the speaker’s narrative. By analyzing spoken content and matching it with textual or graphical elements in the slides, our approach ensures better synchronization between what you hear and what you need to attend to. We explore different ways of solving this problem and assess their success and failure cases. Analyzing multimedia documents is emerging as a key requirement for seamless understanding of content-rich videos, such as educational videos and conference talks, by reducing cognitive strain and improving comprehension. Code and dataset available at: https://github.com/meghamariamkm2002/Slide_Highlight

Abstract

Handwritten Text Recognition (HTR) presents a significant challenge in computer vision due to various factors such as individual writing styles, noise, blur, and other imperfections in the text. This challenge is further exacerbated when dealing with languages using Indian scripts, which are characterized by complex character structures, extensive character inventories, and specific cultural nuances. In this study, we address these challenges by focusing on enhancing handwritten text recognition for ten Indic languages: Hindi, Bengali, Telugu, Tamil, Gujarati, Gurumukhi, Oriya, Kannada, Malayalam, and Urdu. We aim to improve recognition accuracy by leveraging the Permuted Autoregressive Sequence Model (PARSeq), an extension of the transformer-based model. Our results demonstrate the superiority of the PARSeq model over existing approaches, particularly in achieving state-of-the-art performance across most languages. Additionally, we investigate the efficacy of transfer learning from printed text to handwritten text, revealing its potential to enhance recognition performance. The trained models and code are publicly available at https://github.com/LalithaEvani/Indic-HTR-CVIP-2024.

Abstract

Reliable toxic gas detection is vital for residential and industrial safety. While precise sensors are expensive, affordable ones face challenges of nonlinearity and environmental sensitivity, particularly from temperature (T) and humidity (H) effects. In this work, we present a novel predeployment calibration framework that accounts for these environmental factors on the sensor behavior, given by the resistance ratio (Rs/Ro) at constant gas concentration levels. The proposed method first refines the baseline resistance (Rs) estimation by fitting a power-law model to known gas concentrations and then applies a cubic regression model to capture the nonlinear effects of temperature and humidity on the Rs/Ro ratio. Cubic regression achieves superior accuracy (>5.8%) over lower order models while reducing over-fitting risks compared to higher order polynomials. It achieves 99.65% average accuracy, outperforming the 96.73% from standard libraries. This improved performance is particularly notable at low ppm levels, where direct Rs measurements are typically noisy and unstable. The enhanced stability and accuracy of the proposed method were validated over a continuous 90-min test period.

Abstract

Efficient algorithms for graph connected components and minimum spanning tree (MST) computations are vital in diverse domains, from medical diagnostics to chip design. Yet, as graph sizes grow to billions of edges, existing approaches often fail due to limited device memory. This challenge demands innovative rethinking of both algorithmic design and implementation strategies. We address this need by presenting a novel Parallel Heterogeneous External Memory (PHEM) framework, which harnesses both multicore CPU and GPU resources to tackle large-scale graph processing. Our method minimizes communication overhead while maximizing computational throughput through efficient copy- computation strategies. Experiments on a variety of real-world and synthetic benchmarks demonstrate that our algorithms for connected components and minimum spanning tree in the PHEM model outperform current state-of-the-art solutions, significantly advancing the capability to process massive graphs.

Abstract

Optical Character Recognition (OCR) for Indian languages is challenging due to diverse scripts, complex characters, and varied writing styles. This work presents HindiOCR-VLM, a Vision-Language Model adapted for Hindi OCR using Low-Rank Adaptation (LoRA) on a model pre-trained for Chinese and English. We explore two adaptation strategies: (i) a textit{single-stage model that directly predicts text at the page level --- bypassing intermediate word or line detection --- focused on printed documents, and (ii) a two-stage model for multi-domain scenarios (printed, handwritten, and scene text), which first detects words and then recognizes them individually. Inspired by human learning processes, we propose a progressive learning approach --- a training strategy to the textit{single-stage} model to enhance language acquisition and accelerate convergence. Leveraging the vision encoder’s rich representations, our method enables effective multi-domain training. Experimental results and ablation studies show that textit{HindiOCR-VLM} handles the complexities of the Devanagari script well and outperforms domain-specific models, offering a unified and robust solution for Hindi OCR. Code and resources are available at: https://hindiocr-vlm.github.io/.

Abstract

Globesity has widely affected the world population. It is a multifactorial health problem. Obesity can be caused by high-fat intake, high-calorie intake, physical inactivity, age, gender, or hormonal issues that can induce many health issues, such as cardiovascular disorders, diabetes, and metabolic disorders. Health facilities are insufficient to provide services to the whole population. In such a case, introducing Artificial Intelligence (AI) in the health sector is one of the most significant steps that can be taken. With the help of AI, the prognosis, diagnosis, and treatment can be provided to individuals within their homes and setups without any physical interactions. Assembling the data collected with AI can help to give more information on the type of obesity prevailing in a particular society so that it could be prevented in the population. Since AI learning is relatively novel within the existing system of medicine, enabling …

Abstract

Instruction fine-tuning of large language models (LLMs) is a powerful method for improving task-specific performance, but it can inadvertently lead to a phenomenon where models generate harmful responses when faced with malicious prompts. In this paper, we explore Low-Rank Adapter Fusion (LoRA) as a means to mitigate these risks while preserving the model’s ability to handle diverse instructions effectively. Through an extensive comparative analysis against established baselines using recognized benchmark datasets, we demonstrate a 42% reduction in the harmfulness rate by leveraging LoRA fusion between a task adapter and a safety adapter, the latter of which is specifically trained on our safety dataset. In addition, we made noteworthy observations related to exaggerated safety behavior, where the model rejects safe prompts that closely resemble unsafe ones.

Abstract

The partition of 1947 represents a significant rupture in the history of modern South Asia. Current literary research on partition primarily focuses on Hindi and Urdu narratives from India and Pakistan. This paper aims to study the use of silence in Bangladeshi partition stories and analyze the use of various narrative tools such as ellipses, deliberate pauses, omissions, and atmospheric cues by their authors for producing this phenomenon of silence. This paper claims that the use of silence in these Bangladeshi partition stories is strategic and not accidental. It also demonstrates that this narrative silence resembles the kind of silence present in the testimonies of partition survivors. This analysis of silence is based upon close readings of The Escape (1950) by Syed Waliullah, The Exile (1987) by Hasan Azizul Huq, and Roots (1998) by Syeda Farida Rahman. These stories feature themes that are representative of the experience of general people during partition like displacement, identity, belonging, memory, psychological trauma, communal and sexual violence, etc. These short stories are written by Bangladeshi authors from diverse sexual and social backgrounds, who also wrote across different periods spanning from the 1940s to the 1990s.