by jsendak | Apr 25, 2025 | Computer Science
arXiv:2504.16405v1 Announce Type: new
Abstract: The furnishing of multi-modal large language models (MLLMs) has led to the emergence of numerous benchmark studies, particularly those evaluating their perception and understanding capabilities.
Among these, understanding image-evoked emotions aims to enhance MLLMs’ empathy, with significant applications such as human-machine interaction and advertising recommendations. However, current evaluations of this MLLM capability remain coarse-grained, and a systematic and comprehensive assessment is still lacking.
To this end, we introduce EEmo-Bench, a novel benchmark dedicated to the analysis of the evoked emotions in images across diverse content categories.
Our core contributions include:
1) Regarding the diversity of the evoked emotions, we adopt an emotion ranking strategy and employ the Valence-Arousal-Dominance (VAD) as emotional attributes for emotional assessment. In line with this methodology, 1,960 images are collected and manually annotated.
2) We design four tasks to evaluate MLLMs’ ability to capture the evoked emotions by single images and their associated attributes: Perception, Ranking, Description, and Assessment. Additionally, image-pairwise analysis is introduced to investigate the model’s proficiency in performing joint and comparative analysis.
In total, we collect 6,773 question-answer pairs and perform a thorough assessment on 19 commonly-used MLLMs.
The results indicate that while some proprietary and large-scale open-source MLLMs achieve promising overall performance, the analytical capabilities in certain evaluation dimensions remain suboptimal.
Our EEmo-Bench paves the path for further research aimed at enhancing the comprehensive perceiving and understanding capabilities of MLLMs concerning image-evoked emotions, which is crucial for machine-centric emotion perception and understanding.
Enhancing Multi-Modal Large Language Models (MLLMs) with Image-Evoked Emotions
This article introduces the concept of image-evoked emotions and its relevance in enhancing the empathy of multi-modal large language models (MLLMs). MLLMs have gained significant attention in various domains, including human-machine interaction and advertising recommendations. However, the evaluation of MLLMs’ understanding of image-evoked emotions is currently limited and lacks a systematic and comprehensive assessment.
The Importance of Emotion in MLLMs
Emotion plays a crucial role in human communication and understanding, and the ability to perceive and understand emotions is highly desirable in MLLMs. By incorporating image-evoked emotions into MLLMs, these models can better empathize with users and provide more tailored responses and recommendations.
The EEmo-Bench Benchmark
To address the limitations in evaluating MLLMs’ understanding of image-evoked emotions, the authors introduce EEmo-Bench, a novel benchmark specifically designed for this purpose. EEmo-Bench focuses on the analysis of the evoked emotions in images across diverse content categories.
The benchmark includes the following core contributions:
- Diversity of evoked emotions: To assess emotional attributes, the authors adopt an emotion ranking strategy and utilize the Valence-Arousal-Dominance (VAD) model. A dataset of 1,960 images is collected and manually annotated for emotional assessment.
- Four evaluation tasks: Four tasks are designed to evaluate MLLMs’ ability to capture evoked emotions and their associated attributes: Perception, Ranking, Description, and Assessment. Additionally, image-pairwise analysis is introduced for joint and comparative analysis.
- Thorough assessment of MLLMs: A comprehensive evaluation is conducted on 19 commonly-used MLLMs, with a collection of 6,773 question-answer pairs. The results highlight the performance of different models in various evaluation dimensions.
Insights and Future Directions
The results of the EEmo-Bench benchmark reveal that while some proprietary and large-scale open-source MLLMs show promising overall performance, there are still areas in which these models’ analytical capabilities can be improved. This highlights the need for further research and innovation to enhance MLLMs’ comprehension and perception of image-evoked emotions.
The concepts discussed in this article are highly relevant to the wider field of multimedia information systems, as they bridge the gap between textual data and visual content analysis. Incorporating image-evoked emotions into MLLMs opens up new avenues for research in areas such as virtual reality, augmented reality, and artificial reality.
The multi-disciplinary nature of the concepts presented here underscores the importance of collaboration between researchers from fields such as computer vision, natural language processing, and psychology. By combining expertise from these diverse domains, we can develop more sophisticated MLLMs that truly understand and respond to the emotions evoked by visual stimuli.
In conclusion, the EEmo-Bench benchmark serves as a stepping stone for future research in enhancing the comprehension and perception capabilities of MLLMs in the context of image-evoked emotions. This research has significant implications for machine-centric emotion perception and understanding, with applications ranging from personalized user experiences to improved advertising recommendations.
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by jsendak | Apr 25, 2025 | Computer Science
Expert Commentary:
The article highlights the challenges faced by small and medium-sized enterprises (SMEs) in the context of sustainability and compliance with global carbon regulations. SMEs often struggle to navigate the complex carbon trading process and face entry barriers into carbon markets.
The proposed solution, a blockchain-based decentralized carbon credit trading platform tailored specifically for SMEs in Taiwan, offers several advantages. By leveraging blockchain technology, the platform aims to reduce informational asymmetry and intermediary costs, two key challenges in carbon markets.
One interesting aspect of this proposal is the integration of Ethereum-based smart contracts. Smart contracts automate transactions, provide transparency, and reduce administrative burdens. This tackles the technical complexities and market risks associated with carbon trading, making it more accessible for SMEs.
To validate the effectiveness of the proposed system, a controlled experimental design was conducted, comparing it with a conventional centralized carbon trading platform. The statistical analysis confirmed that the blockchain-based platform minimized time and expenses while ensuring compliance with the Carbon Border Adjustment Mechanism (CBAM) and the Clean Competition Act (CCA).
The study also applied the Kano model to measure user satisfaction, identifying essential features and prioritizing future enhancements. This approach ensures that the platform meets the needs of SMEs and continues to evolve based on their requirements.
Overall, this research contributes a comprehensive solution for SMEs seeking to achieve carbon neutrality. By harnessing blockchain technology, the platform addresses key barriers and empowers SMEs to participate in global carbon markets. It highlights the transformative potential of blockchain in creating a more sustainable and transparent future.
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by jsendak | Apr 24, 2025 | Computer Science
arXiv:2504.15376v1 Announce Type: cross
Abstract: We introduce CameraBench, a large-scale dataset and benchmark designed to assess and improve camera motion understanding. CameraBench consists of ~3,000 diverse internet videos, annotated by experts through a rigorous multi-stage quality control process. One of our contributions is a taxonomy of camera motion primitives, designed in collaboration with cinematographers. We find, for example, that some motions like “follow” (or tracking) require understanding scene content like moving subjects. We conduct a large-scale human study to quantify human annotation performance, revealing that domain expertise and tutorial-based training can significantly enhance accuracy. For example, a novice may confuse zoom-in (a change of intrinsics) with translating forward (a change of extrinsics), but can be trained to differentiate the two. Using CameraBench, we evaluate Structure-from-Motion (SfM) and Video-Language Models (VLMs), finding that SfM models struggle to capture semantic primitives that depend on scene content, while VLMs struggle to capture geometric primitives that require precise estimation of trajectories. We then fine-tune a generative VLM on CameraBench to achieve the best of both worlds and showcase its applications, including motion-augmented captioning, video question answering, and video-text retrieval. We hope our taxonomy, benchmark, and tutorials will drive future efforts towards the ultimate goal of understanding camera motions in any video.
CameraBench: A Step Towards Understanding Camera Motion in Videos
In the world of multimedia information systems, understanding camera motion in videos is a crucial task. It has applications in various domains such as animations, artificial reality, augmented reality, and virtual realities. To improve camera motion understanding, a team of researchers has introduced CameraBench, a large-scale dataset and benchmark.
CameraBench comprises approximately 3,000 diverse internet videos that have been annotated by experts using a rigorous multi-stage quality control process. This dataset presents a significant contribution to the field, as it provides a valuable resource for assessing and improving camera motion understanding algorithms.
One key aspect of CameraBench is the collaboration with cinematographers, which has led to the development of a taxonomy of camera motion primitives. This taxonomy helps classify different types of camera motions and their dependencies on scene content. For example, a camera motion like “follow” requires understanding of moving subjects in the scene.
To evaluate human annotation performance, a large-scale human study was conducted. The results showed that domain expertise and tutorial-based training significantly enhance accuracy. Novices may initially struggle with differentiating between camera motions like zoom-in (a change of intrinsics) and translating forward (a change of extrinsics). However, through training, they can learn to differentiate between these motions.
The researchers also evaluated Structure-from-Motion (SfM) models and Video-Language Models (VLMs) using CameraBench. They found that SfM models struggle to capture semantic primitives that depend on scene content, while VLMs struggle with geometric primitives that require precise estimation of trajectories. To address these limitations, a generative VLM was fine-tuned with CameraBench to achieve a hybrid model that combines the strengths of both approaches.
This hybrid model opens up a range of applications, including motion-augmented captioning, video question answering, and video-text retrieval. By better understanding camera motions in videos, these applications can be enhanced, providing more immersive experiences for users.
The taxonomy, benchmark, and tutorials provided with CameraBench are valuable resources for researchers and practitioners working towards the ultimate goal of understanding camera motions in any video. The multi-disciplinary nature of camera motion understanding makes it relevant to various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Apr 24, 2025 | Computer Science
Expert Commentary: Improving Code Editing with EditLord
In software development, code editing is a foundational task that plays a crucial role in ensuring the effectiveness and functionality of the software. The article introduces EditLord, a code editing framework that aims to enhance the performance, robustness, and generalization of code editing procedures.
A key insight presented in EditLord is the use of a language model (LM) as an inductive learner to extract code editing rules from training code pairs. This approach allows for the formulation of concise meta-rule sets that can be utilized for various code editing tasks.
One notable advantage of explicitly defining the code transformation steps is that it addresses the limitations of existing approaches that treat code editing as an implicit end-to-end task. By breaking down the editing process into discrete and explicit steps, EditLord overcomes the challenges related to suboptimal performance and lack of robustness and generalization.
The use of LM models in EditLord offers several benefits. Firstly, it enables the augmentation of training samples through the manifestation of rule sets specific to each sample. This augmentation process can greatly enhance the finetuning process or assist in prompting- and iterative-based code editing. Secondly, by leveraging LM models, EditLord achieves improved editing performance and robustness compared to existing state-of-the-art methods.
Furthermore, EditLord demonstrates its effectiveness across critical software engineering and security applications, LM models, and editing modes. The framework achieves an average improvement of 22.7% in editing performance and 58.1% in robustness. It also ensures a 20.2% higher level of functional correctness, which is crucial in the development of reliable and secure software.
The advancements brought by EditLord have significant implications for the field of code editing and software development as a whole. By explicitly defining code transformation steps and utilizing LM models, developers can benefit from enhanced performance, robustness, generalization, and functional correctness. This can lead to more efficient and reliable software development processes, ultimately resulting in higher-quality software products.
Future Outlook
Looking ahead, the concepts and techniques introduced by EditLord open doors for further research and development in code editing. One possible direction is the exploration of different types of language models and their impact on code editing performance. Additionally, investigating the integration of other machine learning techniques and algorithms with EditLord could yield even more significant improvements.
Moreover, the application of EditLord to specific domains, such as machine learning or cybersecurity, may uncover domain-specific code editing rules and optimizations. This domain-specific approach could further enhance the performance and accuracy of code editing in specialized software development areas.
Overall, EditLord presents a promising framework for code editing, offering a more explicit and robust approach to code transformation. Its adoption has the potential to revolutionize the software development process, leading to higher efficiency, reliability, and security in software creation.
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by jsendak | Apr 23, 2025 | Computer Science
arXiv:2504.15066v1 Announce Type: new
Abstract: Incorporating visual modalities to assist Automatic Speech Recognition (ASR) tasks has led to significant improvements. However, existing Audio-Visual Speech Recognition (AVSR) datasets and methods typically rely solely on lip-reading information or speaking contextual video, neglecting the potential of combining these different valuable visual cues within the speaking context. In this paper, we release a multimodal Chinese AVSR dataset, Chinese-LiPS, comprising 100 hours of speech, video, and corresponding manual transcription, with the visual modality encompassing both lip-reading information and the presentation slides used by the speaker. Based on Chinese-LiPS, we develop a simple yet effective pipeline, LiPS-AVSR, which leverages both lip-reading and presentation slide information as visual modalities for AVSR tasks. Experiments show that lip-reading and presentation slide information improve ASR performance by approximately 8% and 25%, respectively, with a combined performance improvement of about 35%. The dataset is available at https://kiri0824.github.io/Chinese-LiPS/
Incorporating Multimodal Visual Cues for Audio-Visual Speech Recognition
Automatic Speech Recognition (ASR) tasks have greatly benefited from the inclusion of visual modalities. However, existing Audio-Visual Speech Recognition (AVSR) datasets and methods often focus solely on lip-reading or speaking contextual video, neglecting the potential of combining different valuable visual cues within the speaking context. In this paper, the authors introduce the Chinese-LiPS multimodal AVSR dataset and present the LiPS-AVSR pipeline, which leverages lip-reading and presentation slide information as visual cues for AVSR tasks.
The Chinese-LiPS dataset is a comprehensive collection comprising 100 hours of speech, video, and corresponding manual transcription. What sets this dataset apart is the inclusion of not only lip-reading information but also the presentation slides used by the speaker. This multi-disciplinary approach allows for a more holistic understanding of the audio-visual speech data, capturing the subtle nuances and context that improve ASR performance.
The LiPS-AVSR pipeline developed based on the Chinese-LiPS dataset demonstrates the effectiveness of leveraging multiple visual cues. The experiments conducted show that lip-reading information improves ASR performance by approximately 8%, while presentation slide information leads to a significant improvement of about 25%. When combined, the performance improvement reaches approximately 35%. This highlights the synergy of different visual cues and the potential for further enhancement in AVSR tasks.
This research embodies the multi-disciplinary nature of multimedia information systems, incorporating elements from speech recognition, computer vision, and human-computer interaction. By combining the analytical power of machine learning algorithms with visual and textual information, this work pushes the boundaries of AVSR systems and opens up new avenues for research.
Furthermore, the incorporation of visual cues extends beyond AVSR and has implications for other areas such as animations, artificial reality, augmented reality, and virtual realities. These technologies heavily rely on the integration of audio and visual information, and leveraging multimodal cues can greatly enhance the immersive experience and realism. The Chinese-LiPS dataset and the LiPS-AVSR pipeline serve as valuable resources for researchers and industry professionals working in these fields, providing a foundation for developing more advanced and accurate systems.
In conclusion, the release of the Chinese-LiPS multimodal AVSR dataset and the development of the LiPS-AVSR pipeline demonstrate the power of incorporating multiple visual cues for improved ASR performance. This work showcases the multi-disciplinary nature of multimedia information systems and has far-reaching implications for various domains. By combining lip-reading and presentation slide information, the LiPS-AVSR pipeline sets a new standard for AVSR systems and opens up exciting possibilities for further research and development.
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by jsendak | Apr 22, 2025 | Computer Science
Experimentation is a critical component of game development and live operations, as it allows teams to constantly improve player engagement, retention, and monetization. This comprehensive guide explores the various aspects of implementing experimentation in the gaming industry, covering every stage of the game development lifecycle and the marketing mix.
One of the key points made in the article is the importance of conducting concept testing and prototyping before launching a game. This allows developers to gather valuable feedback from potential players and make informed decisions about the game’s features, mechanics, and overall design. By involving players in the development process early on, teams can ensure that they are creating a game that aligns with player preferences and market demand.
As for post-launch experimentation, the article highlights the significance of personalization and LiveOps. With player populations becoming increasingly diverse, it is crucial for game developers to tailor their experiences to individual player preferences. By utilizing data-driven techniques and conducting continuous experiments, developers can fine-tune game mechanics, offer personalized content, and enhance the overall player experience.
Gaming presents its own unique challenges when it comes to experimentation. The highly engaged nature of gaming communities means that developers must carefully consider the impact of changes on player experiences and community dynamics. Additionally, the complexity of interactive systems and the constantly evolving behaviors of players require tailored approaches to experimentation. This could include A/B testing different game mechanics, conducting player surveys, or analyzing in-game telemetry data.
The article emphasizes the importance of collaboration between product, marketing, and analytics teams in successfully implementing experimentation. By bringing together these different areas of expertise, developers can ensure that their experiments are based on comprehensive data, align with the game’s overall vision, and have a positive impact on the player experience.
Ethical considerations also play a significant role in experimentation in gaming. The article acknowledges the need for fairness and player autonomy, highlighting the importance of informed consent and transparency when conducting experiments. Developers must ensure that their experiments do not disrupt the player experience or exploit players for the sake of monetization.
In conclusion, experimentation is a vital tool for game developers to drive innovation and adapt their games to the ever-changing preferences of players. By implementing experimentation throughout the game development lifecycle and engaging in continuous personalization and LiveOps, developers can create more engaging, tailored, and successful gaming experiences.
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