论文分享 | 多模态大模型相关研究进展

1. Exploiting VLM Localizability and Semantics for Open Vocabulary Action Detection
2. Large Vision-Language Models for Remote Sensing Visual Question Answering
3. VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?
4. BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization
5. LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language Interpretation

1.Exploiting VLM Localizability and Semantics for Open Vocabulary Action Detection

Authors: Wentao Bao, Kai Li, Yuxiao Chen, Deep Patel, Martin Renqiang Min, Yu Kong

https://arxiv.org/abs/2411.10922

论文摘要

Action detection aims to detect (recognize and localize) human actions spatially and temporally in videos. Existing approaches focus on the closed-set setting where an action detector is trained and tested on videos from a fixed set of action categories. However, this constrained setting is not viable in an open world where test videos inevitably come beyond the trained action categories. In this paper, we address the practical yet challenging Open-Vocabulary Action Detection (OVAD) problem. It aims to detect any action in test videos while training a model on a fixed set of action categories. To achieve such an open-vocabulary capability, we propose a novel method OpenMixer that exploits the inherent semantics and localizability of large vision-language models (VLM) within the family of query-based detection transformers (DETR). Specifically, the OpenMixer is developed by spatial and temporal OpenMixer blocks (S-OMB and T-OMB), plus a dynamically fused alignment (DFA) module. The three components collectively leverage the strong generalization from pretrained VLMs and end-to-end learning from the DETR design. Moreover, we established OVAD benchmarks under various settings, and the experimental results show that the OpenMixer outperforms the baselines for detecting both seen and unseen actions. We release the codes, models, and dataset splits at https://github.com/Cogito2012/OpenMixer.

论文简评

该篇论文针对开放词汇动作检测（Open-Vocabulary Action Detection，OVAD）问题提出了一个名为OpenMixer的方法，利用大型视觉语言模型（VLMs）来增强视频中动作的识别和定位能力。该文引入了空间和时间上的OpenMixer块以及动态融合对齐模块，旨在提高动作识别和定位的能力。作者声称实验结果证明他们的方法具有强大的有效性，并通过多个数据集的详细评估展示了这一效果。

整体而言，该论文提出了一种新颖且实用的解决方案，旨在解决开放词汇的动作检测问题。它强调了深度学习在处理视觉-语言任务中的潜力，并通过详细的实验验证了其有效性和实用性。值得注意的是，这篇论文不仅深入研究了OVAD问题，还探讨了如何利用现有的大模型来改善动作识别和定位能力，为未来的研究提供了一个新的方向。总之，这篇论文是一个重要的研究成果，对相关领域的发展有着积极的影响。

2.Large Vision-Language Models for Remote Sensing Visual Question Answering

Authors: Surasakdi Siripong, Apirak Chaiyapan, Thanakorn Phonchai

https://arxiv.org/abs/2411.10857

论文摘要

Remote Sensing Visual Question Answering (RSVQA) is a challenging task that involves interpreting complex satellite imagery to answer natural language questions. Traditional approaches often rely on separate visual feature extractors and language processing models, which can be computationally intensive and limited in their ability to handle open-ended questions. In this paper, we propose a novel method that leverages a generative Large Vision-Language Model (LVLM) to streamline the RSVQA process. Our approach consists of a two-step training strategy: domain-adaptive pretraining and prompt-based fine-tuning. This method enables the LVLM to generate natural language answers by conditioning on both visual and textual inputs, without the need for predefined answer categories. We evaluate our model on the RSVQAxBEN dataset, demonstrating superior performance compared to state-of-the-art baselines. Additionally, a human evaluation study shows that our method produces answers that are more accurate, relevant, and fluent. The results highlight the potential of generative LVLMs in advancing the field of remote sensing analysis.

论文简评

这篇关于远程感知视觉问题回答（RSVQA）的研究论文提出了一种新的方法来解决这个问题，这种方法利用了大型视觉语言模型（LVLMs）。作者提出了一个两步训练策略，其中包括针对特定领域进行的适应性预训练和基于提示的微调，以提高LVLMs在RSVQA任务上的表现。实验结果表明，提出的策略在RSVQAxBEN数据集上显著优于现有最先进的模型，证明了该方法对于复杂遥感查询的有效性。 该研究的关键点在于：首先，使用LVLMs来解决RSVQA是一个创新的想法，解决了当前遥感应用中面临的重大挑战；其次，提出的方法是一种有效的改进模型性能的策略，通过两个阶段的训练实现了这一目标；最后，对RSVQAxBEN数据集进行了详细的评估，结果显示该方法的表现比基准模型显著更好，并获得人类专家的高度评价，进一步增强了其可靠性。总的来说，这篇论文为解决RSVQA提供了有价值的见解和解决方案，具有很高的学术价值和实用意义。

3.VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?

Authors: Yunlong Tang, Junjia Guo, Hang Hua, Susan Liang, Mingqian Feng, Xinyang Li, Rui Mao, Chao Huang, Jing Bi, Zeliang Zhang, Pooyan Fazli, Chenliang Xu

https://arxiv.org/abs/2411.10979

论文摘要

The advancement of Multimodal Large Language Models (MLLMs) has enabled significant progress in multimodal understanding, expanding their capacity to analyze video content. However, existing evaluation benchmarks for MLLMs primarily focus on abstract video comprehension, lacking a detailed assessment of their ability to understand video compositions, including the nuanced interpretation of how visual elements combine and interact within highly compiled video contexts. We introduce VidComposition, a new benchmark specifically designed to evaluate the video composition understanding capabilities of MLLMs, using carefully curated compiled videos and cinematic-level annotations. VidComposition includes 982 videos with 1706 multiple-choice questions, covering various compositional aspects such as camera movement, angle, shot size, narrative structure, character actions, and emotions, etc. Our comprehensive evaluation of 33 open-source and proprietary MLLMs reveals a significant performance gap between human and model capabilities. This highlights the limitations of current MLLMs in understanding complex, compiled video compositions and offers insights into areas for further improvement. The leaderboard and evaluation code are available at https://yunlong10.github.io/VidComposition/}{https://yunlong10.github.io/VidComposition/}.

论文简评

综上所述，该论文提出了一种专门用于评估多模态大型语言模型（MLLMs）对视频组合理解能力的基准VidComposition。这一创新性工作旨在通过涵盖多种构成元素（如摄像机运动和叙事结构）的982个精选视频和1706道多项选择题，从而全面展示其性能差距。它为人类与模型之间的认知差异提供了明确的证据。此外，广泛的研究表明，这项研究不仅展示了现有模型在构建视频场景中的局限性，也为未来的研究指明了方向，即如何利用MLLMs更好地理解和创作电影、电视和其他形式的内容。总之，该论文提供了一个有价值且前瞻性的视角，值得进一步深入研究以提升MLLMs的理解能力和创造力。

4.BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization

Authors: Md. Nazmus Sadat Samin, Jawad Ibn Ahad, Tanjila Ahmed Medha, Fuad Rahman, Mohammad Ruhul Amin, Nabeel Mohammed, Shafin Rahman

https://arxiv.org/abs/2411.10879

论文摘要

This study focuses on recognizing Bangladeshi dialects and converting diverse Bengali accents into standardized formal Bengali speech. Dialects, often referred to as regional languages, are distinctive variations of a language spoken in a particular location and are identified by their phonetics, pronunciations, and lexicon. Subtle changes in pronunciation and intonation are also influenced by geographic location, educational attainment, and socioeconomic status. Dialect standardization is needed to ensure effective communication, educational consistency, access to technology, economic opportunities, and the preservation of linguistic resources while respecting cultural diversity. Being the fifth most spoken language with around 55 distinct dialects spoken by 160 million people, addressing Bangla dialects is crucial for developing inclusive communication tools. However, limited research exists due to a lack of comprehensive datasets and the challenges of handling diverse dialects. With the advancement in multilingual Large Language Models (mLLMs), emerging possibilities have been created to tackle the challenges of dialectal Automatic Speech Recognition (ASR) and Machine Translation (MT). This study presents an end-to-end pipeline for converting dialectal Noakhali speech to standard Bangla speech. This investigation includes constructing a large-scale diverse dataset with dialectal speech signals that tailor the fine-tuning process in ASR and LLM for transcribing dialect speech to dialect text and translating dialect text to standard Bangla text. Our experiments demonstrated that fine-tuning the Whisper ASR model achieved a Character Error Rate (CER) of 0.8% and Word Error Rate (WER) of 1.5%, while the BanglaT5 model attained a BLEU score of 41.6% for dialect-to-standard text translation. We completed our end-to-end pipeline for dialect standardization by utilizing AlignTTS, a text-to-speech (TTS) model. With potential applications across different dialects, this research lays the groundwork for future investigations into Bangla dialect standardization.

论文简评

该论文提出了一个端到端系统，用于识别和标准化孟加拉语方言，特别是聚焦诺阿克利方言。它整合了自动语音识别（ASR）、机器翻译（MT）和文本到语音（TTS），以将方言口语转录为标准孟加拉语，这是该领域的一个重大空白，由于缺乏训练数据而受到限制。作者提供了全面的数据集，并报告了使用现有模型通过微调方法取得的令人鼓舞成果，显示出对提出的数据集的有效性有显著改进。这些结果表明，通过使用提供的数据集，可以有效提高方言识别系统的性能。此外，论文还讨论了研究中存在的挑战，并提出了未来的研究方向。总之，这篇论文提供了一个创新的解决方案，旨在解决方言识别领域的实际问题，具有重要的理论意义和实践价值。

5.LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language Interpretation

Authors: Zhenshi Li, Dilxat Muhtar, Feng Gu, Xueliang Zhang, Pengfeng Xiao, Guangjun He, Xiaoxiang Zhu

https://arxiv.org/abs/2411.09301

论文摘要

Automatically and rapidly understanding Earth's surface is fundamental to our grasp of the living environment and informed decision-making. This underscores the need for a unified system with comprehensive capabilities in analyzing Earth's surface to address a wide range of human needs. The emergence of multimodal large language models (MLLMs) has great potential in boosting the efficiency and convenience of intelligent Earth observation. These models can engage in human-like conversations, serve as unified platforms for understanding images, follow diverse instructions, and provide insightful feedback. In this study, we introduce \MODELNAME, an MLLM specialized in understanding remote sensing (RS) images, designed to expertly perform a wide range of RS understanding tasks aligned with human instructions. \MODELNAME~features an enhanced vision encoder and a novel bridge layer, enabling efficient visual compression and better language-vision alignment. To further enhance RS-oriented vision-language alignment, we propose a large-scale RS image-caption dataset, generated through feature-guided image recaptioning. Additionally, we introduce an instruction dataset specifically designed to improve spatial recognition abilities. Extensive experiments demonstrate the superior performance of \MODELNAME~across various RS image understanding tasks. We also evaluate different MLLM performances in complex RS perception and instruction following using a complicated multi-choice question evaluation benchmark, providing a reliable guide for future model selection and improvement. Data, code, and models will be available at \url{https://github.com/NJU-LHRS/LHRS-Bot}.

论文简评

《LHRS-Bot-Nova：一种多模态大型语言模型（MMLLM）》是本文所研究的主要课题，旨在为遥感图像解释提供新的解决方案。该文提出了一个新的数据集（LHRS-Align-Recap），以及一个具有增强视觉编码器和混合专家桥接层的新架构，旨在提高视觉与语言的对齐性和空间识别能力。此外，作者声称他们的模型在各种遥感任务上均表现出色，并通过大量实验来证明这一点。因此，本文主要关注的是远程传感图像解释领域的一个重要问题，即如何使用大规模的语言模型去处理这类图像。同时，他们提出了一种新的数据集，用于改善图像与描述语句的匹配质量，并展示了一个能够显著提升模型性能的新架构。这些成就使得LHRS-Bot-Nova成为一项具有重要意义的研究成果，值得进一步探索其应用潜力。

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