Author: Gabriel Thomas

Authors: Ananya Pandey、Dinesh Kumar Vishwakarma Paper: https://arxiv.org/abs/2408.10246 Introduction Sarcasm is a complex form of communication often conveyed through a combination of linguistic and non-linguistic cues. Recognizing sarcasm in conversations is a challenging task for computer vision and natural language processing systems. Traditional sarcasm recognition methods have primarily focused on text, but for more reliable identification, it is essential to consider visual, acoustic, and textual information. This blog explores VyAnG-Net, a novel multi-modal sarcasm recognition model that integrates visual, acoustic, and glossary features to enhance sarcasm detection accuracy. Related Work Unimodal Sarcasm Recognition Text-Based Sarcasm Recognition Early research in sarcasm detection…

Authors: Hong Xie、Mingze Zhong、Defu Lian、Zhen Wang、Enhong Chen Paper: https://arxiv.org/abs/2408.10895 Introduction In the digital age, online product rating systems have become a cornerstone for assessing the quality of products and services. Platforms like Amazon and TripAdvisor rely heavily on user-generated ratings to guide potential customers. However, these systems are not without flaws. One significant issue is the herding effect, where users’ ratings are influenced by previous ratings, leading to biased and potentially misleading assessments. This study, conducted by Hong Xie, Mingze Zhong, Defu Lian, Zhen Wang, and Enhong Chen, delves into the herding effects in recommendation systems and proposes methods to…

Authors: Hong Su Paper: https://arxiv.org/abs/2408.09958 Introduction In the realm of deep learning, the vanishing gradient problem has long been a significant challenge, particularly when training very deep neural networks. Residual Networks (ResNet) have been instrumental in addressing this issue by introducing skip connections, which allow gradients to flow directly through the network, thereby facilitating the training of much deeper networks. However, the traditional implementation of ResNet combines the input (ipd) and the transformed data (tfd) in a fixed 1:1 ratio, which may not be optimal across all scenarios. In this paper, we introduce AdaResNet (Auto-Adapting Residual Network), a novel architecture…

Authors: Xiao Wang、Yuehang Li、Fuling Wang、Shiao Wang、Chuanfu Li、Bo Jiang Paper: https://arxiv.org/abs/2408.09743 Introduction X-ray image-based medical report generation is a critical application of Artificial Intelligence (AI) in healthcare. The goal is to leverage AI models to generate high-quality medical reports from X-ray images, thereby reducing the workload of doctors and minimizing patient waiting times. Despite significant advancements, current models still struggle to match the expertise of professional physicians due to challenges such as data privacy concerns, lack of quality and diversity in training datasets, and the rarity of certain diseases. This paper introduces a novel context-guided efficient X-ray medical report generation framework,…

Authors: Emanuele De Angelis、Maurizio Proietti、Francesca Toni Paper: https://arxiv.org/abs/2408.10126 Introduction Assumption-Based Argumentation (ABA) is a structured form of argumentation that has been widely recognized for its ability to unify various non-monotonic reasoning formalisms, including logic programming. ABA frameworks allow for the representation of defeasible knowledge, which is subject to argumentative debate. Traditionally, these frameworks are provided upfront, but this paper addresses the challenge of automating their learning from background knowledge and examples. Specifically, the focus is on brave reasoning under stable extensions for ABA, introducing a novel algorithm that leverages Answer Set Programming (ASP) for implementation. Related Work Several forms of…

Authors: Kang Du、Zhihao Liang、Zeyu Wang Paper: https://arxiv.org/abs/2408.08524 Introduction Reconstructing physical attributes from multiple observations has long been a challenge in computer vision and computer graphics. Illumination is a highly diverse and complicated factor that significantly influences observations. Illumination Decomposition (ID) aims to achieve controllable lighting editing and produce various visual effects. However, ID is an extremely ill-posed problem, as varying interactions between different lighting distributions and materials can produce identical light effects. This issue is compounded by the complexity of illumination (e.g., self-emission, direct, and indirect illumination). Without priors of geometry and materials, this task becomes exceedingly difficult. Furthermore, the…

Authors: Marion Ho-Dac Paper: https://arxiv.org/abs/2408.08318 Introduction The European Union (EU) has recently adopted a uniform legal framework applicable to artificial intelligence systems (AI systems) within its internal market. The Regulation (EU) 2024/1689, known as the “Artificial Intelligence Act” (AI Act), is a landmark piece of legislation that sets binding regulatory requirements for key operators, including public authorities, in the global value chain of high-risk AI systems marketed or used within the EU. This blog provides a detailed analysis of the AI Act, focusing on its scope, compliance regimes, and multi-level governance structure. I) The Scope of the AI Act A)…

Authors: Ilya Kuleshov、Galina Boeva、Vladislav Zhuzhel、Evgenia Romanenkova、Evgeni Vorsin、Alexey Zaytsev Paper: https://arxiv.org/abs/2408.08055 COTODE: COntinuous Trajectory Neural Ordinary Differential Equations for Modelling Event Sequences Introduction Event sequences are prevalent in various domains, such as banking transactions, medical histories, sales data, and earthquake records. These sequences often exhibit uneven structures, posing challenges for traditional processing algorithms. While conventional methods model hidden data dynamics as probabilistic processes, recent advancements have explored the use of Neural Ordinary Differential Equations (ODEs) to process sequential data. This paper introduces COTODE, a novel approach that models event sequences through continuous trajectories using Neural ODEs, addressing the limitations of discontinuous…

Authors: Jiajie Li、Garrett Skinner、Gene Yang、Brian R Quaranto、Steven D Schwaitzberg、Peter C W Kim、Jinjun Xiong Paper: https://arxiv.org/abs/2408.07981 Introduction The field of surgery is inherently multimodal, involving dynamic sequences of actions and multi-stage processes that cannot be fully captured through static imagery. While large language models (LLMs) have shown significant promise in medical question answering, their application has been largely limited to static images. This paper introduces LLaVA-Surg, a novel multimodal conversational assistant designed to understand and engage in discussions about surgical videos. The key contributions include the creation of Surg-QA, a large-scale dataset of surgical video-instruction pairs, and the development of a…

Authors: Yujia Wu、Yiming Shi、Jiwei Wei、Chengwei Sun、Yuyang Zhou、Yang Yang、Heng Tao Shen Paper: https://arxiv.org/abs/2408.06740 Introduction Personalized text-to-image generation has become a significant area of research due to its ability to create high-fidelity portraits of specific identities based on user-defined prompts. Traditional methods often involve test-time fine-tuning or adding an additional pre-trained branch, which can be inefficient and struggle to maintain identity fidelity while preserving the model’s original generative capabilities. This paper introduces DiffLoRA, a novel approach that leverages diffusion models to predict personalized low-rank adaptation (LoRA) weights from reference images, integrating these weights into the text-to-image model for efficient and accurate personalization…