United States

Recently, large language models (LLMs) and multimodal large language models (MLLMs) have demonstrated promising results on document visual question answering (VQA) task, particularly after training on document instruction datasets. An effective evaluation method for document instruction data is crucial in constructing instruction data with high efficacy, which, in turn, facilitates the training of LLMs and MLLMs for document VQA. However, most existing evaluation methods for instruction data are limited to the textual content of the instructions themselves, thereby hindering the effective assessment of document instruction datasets and constraining their construction. In this paper, we propose ProcTag, a data-oriented method that assesses the efficacy of document instruction data. ProcTag innovatively performs tagging on the execution process of instructions rather than the instruction text itself. By leveraging the diversity and complexity of these tags to assess the efficacy of the given dataset, ProcTag enables selective sampling or filtering of document instructions. Furthermore, DocLayPrompt, a novel semi-structured layout-aware document prompting strategy, is proposed for effectively representing documents. Experiments demonstrate that sampling existing open-sourced and generated document VQA/instruction datasets with ProcTag significantly outperforms current methods for evaluating instruction data. Impressively, with ProcTag-based sampling in the generated document datasets, only 30.5\% of the document instructions are required to achieve 100\% efficacy compared to the complete dataset.

AAAI 2025

ProcTag: Process Tagging for Assessing the Efficacy of Document Instruction Data

mult modal vision

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



In the logic of theory change, the AGM model has acquired the status of a standard model. However, the AGM model does not seem adequate for some contexts and application domains. This inspired many researchers to propose extensions and generalizations to AGM. Among these extensions, one of the most important are belief bases. Belief bases have more expressivity than belief sets, as explicit and implicit beliefs have different statuses. In this paper, we present  reformulation, a belief change operation that allows us to reformulate a belief base making some particular sentences explicit without modifying the consequences of the belief base. We provide a constructive method and its axiomatic characterization.

On the Logic of Theory Base Change: Reformulation of Belief Bases

Federated clustering is crucial to mining knowledge from unlabeled non-Independent Identically Distributed (non-IID) data distributed to multiple clients while preserving their privacy. Most existing attempts learn cluster distributions at local clients, and then securely pass the desensitized information to the server for aggregation. However, federated clustering techniques are still relatively unexplored, especially for some tricky but common problems, including the heterogeneity in client storage, computing power, communication, and the unknown number of proper clusters $k$. To further bridge the gap between federated clustering and real application scenarios, this paper first shows that the client asynchrony and unknown $k$ are complex coupling problems, and then proposes an Asynchronous Federated Cluster Learning (AFCL) method accordingly. To learn an appropriate number of cluster distributions from the asynchronous non-IID clients, we spread excess seed points to the clients as a learning medium and coordinate them across the clients to form a consensus. To alleviate the distribution imbalance caused by the unforeseen asynchrony, we also design a balancing mechanism for seed updating. As a result, the seeds gradually adapt to each other to reveal a proper number of clusters. Extensive experiments demonstrate the efficacy of AFCL. The source code of AFCL is provided in the uploaded ZIP file.

Asynchronous Federated Clustering with Unknown Number of Clusters

Learning-based image dehazing methods have achieved remarkable results on synthetic haze datasets. However, due to the lack of real paired haze/clean data and robust features, most existing dehazing methods may leave residual haze and produce unrealistic colors in real-world haze condition. In this paper, we propose a novel Structure Guided Dehazing Network (SGDN) that operates in both RGB and YCbCr color spaces for real-world dehazing. Our SGDN comprises two meticulously designed key modules: the Bi-Color Cooperative Bridge (BCB) and the Guidance Enhancement Module (GEM). Specifically, the BCB consists of the Phase Integration Module (PIM) and the Interactive Attention Module (IAM), which exploit the rich texture features of the YCbCr space to guide the RGB space to recover clearer features in both frequency and spatial domains.  To ensure the tonal consistency of the images, the GEM is proposed that further enhances the color perception of RGB features by aggregating the channel information of YCbCr features. As a result, our method surpasses existing state-of-the-art methods across multiple real-world smoke/haze datasets.  Finally, for effective supervised learning, we create a new trainable dataset called the Real-World Well-Aligned Haze Dataset (RWAHD), which includes a variety of scenes from different geographical regions and climate conditions.

Guided Real Image Dehazing Using YCbCr Color Space

Few-shot learning (FSL) aims to recognize new concepts using a limited number of visual samples. Existing approaches attempt to incorporate semantic information into the limited visual data for category understanding. However, these methods often enrich class-level feature representations with abstract category names, failing to capture the nuanced features essential for effective generalization. To address this issue, we propose a novel framework for FSL, which incorporates both the abstract class semantics and the concrete class entities extracted from Large Language Models (LLMs), to enhance the representation of the class prototypes. Specifically, our framework composes a Semantic-guided Visual Pattern Extraction (SVPE) module and a Prototype-Calibration (PC) module, where the SVPE meticulously extracts semantic-aware visual patterns across diverse scales, while the PC module seamlessly integrates these patterns to refine the visual prototype, enhancing its representativeness. Extensive experiments on four few-shot classification benchmarks and the BSCD-FSL cross-domain benchmarks showcase remarkable advancements over the current state-of-the-art methods. Notably, for the challenging one-shot setting, our approach, utilizing the ResNet-12 backbone, achieves an impressive average improvement of 1.95% over the second-best competitor.

Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning

Writing comprehensive and accurate descriptions of technical drawings in patent documents is crucial for effective knowledge sharing and enabling replication and protection of intellectual property. However, the automation of this task has largely been overlooked by the research community. To this end, we introduce PatentDesc-355K, a novel large-scale dataset containing ∼355K patent images along with their brief and detailed textual descriptions extracted from 60K+ US patent documents. Further, we propose PatentLMM, a novel multimodal large language model specifically tailored for generating high-quality descriptions of patent figures. Our proposed PatentLMM comprises two key components: (i) PatentMME, a specialized multi-modal vision encoder that captures the unique structural elements of patent figures, and (ii) PatentLLaMA, a domain-adapted version of LLaMA fine-tuned on a large collection of patents. Extensive experiments demonstrate that training a vision encoder specifically designed for patent figures significantly boosts the performance, generating coherent descriptions compared to fine-tuning similar-sized off-the-shelf multi-modal models. PatentDesc-355K and PatentLMM pave the way for automating patent figure understanding, enabling efficient knowledge sharing, and faster drafting of patent documents. We make the code and data publicly available.

PatentLMM: Large Multimodal Model for Generating Descriptions for Patent Figures

Variational autoencoder performs well in community detection on static networks, but it is difficult to directly extend to continuous dynamic networks. The main reason is that traditional methods mainly rely on adjacency structures to complete the inference and generation processes. However, continuous dynamic networks cannot be described by this structure because the inherent timeliness and causality information of the network would be lost. To address this issue, we propose a novel variational autoencoder, CT-VAE, for community detection in continuous dynamic networks, along with its scalable variant, CT-CAVAE. By conceptualizing node interactions as event streams and adopting the Hawkes process to capture temporal dynamics and causality, and incorporating them into the inference process, CT-VAE can effectively extend the traditional inference approach to continuous dynamic networks. Additionally, in the generation phase, CT-VAE combines pseudo-labeling and compact constraint strategies to facilitate the reconstruction process of non-adjacent structures. For the scalable variant, CT-CAVAE, end-to-end community detection is achieved by cleverly combining Gaussian mixture distribution. Extensive experimental results demonstrate that the proposed CT-VAE and CT-CAVAE achieve more favorable performance compared with the state-of-the-art baselines.

Community-Aware Variational Autoencoder for Continuous Dynamic Networks

Recent face forgery detection methods based on disentangled representation learning utilize paired images for cross-reconstruction, aiming to extract forgery-relevant attributes and forgery-irrelevant content. However, there still exist the following issues that may comprise the detector performance: 1) using information-dense images as the decoupling targets increases the decoupling difficulty; 2) the extracted attribute features are reconstruction-irrelevant rather than forgery-relevant, and single-scale forgery representation decoupling cannot capture sufficient discriminative information; 3) the generalization performance of decoupled attribute features is poor as the detector focuses on learning specific artifact types in the training set. To address these issues, we propose a novel disentangled representation learning framework for deepfake detection. First, we extract features by partitioning the dense information within the image, focusing independently on texture, color, or edges. These features are then used as the decoupling targets rather than the images themselves, which could mitigate the decoupling difficulty. Second, we extend reconstruction loss from image-level to feature-level, thus extending the forgery representation decoupling from single-scale to multi-scale. Third, we propose a critical forgetting mechanism that forces the detector to forget the most salient features during training, which correspond to specific forgery artifact types in the training set. Extensive experimental results validate the efficacy of the proposed method.

Critical Forgetting-Based Multi-Scale Disentanglement for Deepfake Detection

This paper develops a Versatile and Honest vision language Model (VHM) for remote sensing image analysis. VHM is built on a large-scale remote sensing image-text dataset with rich-content captions (VersaD), and an honest instruction dataset comprising both factual and deceptive questions (HnstD). Unlike prevailing remote sensing image-text datasets, in which image captions focus on a few prominent objects and their relationships, VersaD captions provide detailed information about image properties, object attributes, and the overall scene. This comprehensive captioning enables VHM to thoroughly understand remote sensing images and perform diverse remote sensing tasks. Moreover, different from existing remote sensing instruction datasets that only include factual questions, HnstD contains additional deceptive questions stemming from the non-existence of objects. This feature prevents VHM from producing affirmative answers to nonsense queries, thereby ensuring its honesty. In our experiments, VHM significantly outperforms various vision language models on common tasks of scene classification, visual question answering, and visual grounding. Additionally, VHM achieves competent performance on several unexplored tasks, such as building vectorizing, multi-label classification and honest question answering.

VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis

With the growing demand for solutions to real-world video challenges, interest in dense video captioning (DVC) has been on the rise. DVC involves the automatic captioning and localization of untrimmed videos. Several studies highlight the challenges of DVC and introduce improved methods utilizing prior knowledge such as pre-training and external memory. In this research, we propose a model that leverages the prior knowledge of human-oriented hierarchical dense memory inspired by human memory hierarchy and cognition. To mimic human-like memory recall, we construct a hierarchical memory and a hierarchical memory reading module. We build an efficient hierarchical dense memory by employing clustering of memory events and summarization using large language models. Comparative experiments demonstrate that this hierarchical memory recall process improves the performance of DVC by achieving state-of-the-art performance on YouCook2 and ViTT datasets.

HiCM²: Hierarchical Compact Memory Modeling for Dense Video Captioning

Recently, deep learning based methods have revolutionized the remote sensing image segmentation.However, these methods usually rely on a predefined semantic class set, thus needs additional image annotation and model training when adapting to new classes. More importantly, they are unable to segment arbitrary semantic class. In this work, we introduce Open-Vocabulary Remote Sensing Image Semantic Segmentation (OVRSISS), which aims to segment arbitrary semantic class in remote sensing images. To address the lack of OVRSISS datasets, we develop LandDiscover50K, a comprehensive dataset of 51,846 images covering 40 diverse classes. In addition, we propose a novel framework named GSNet that integrates domain priors from special remote sensing models and versatile capabilities of general vision-language models. Technically, GSNet consists of a Dual Stream Image Encoder (DSIE), a Query-Guided Feature Fusion (QGFF), and a Residual Information Preservation Decoder (RIPD). DSIE first captures comprehensive features from both special models and general models in dual streams. Then, with the guidance of variable vocabularies, QGFF integrates specialist and generalist features, enabling them to complement each other. Finally, RIPD is proposed to aggregate multi-source features for more accurate mask predictions. Experiments show that our method outperforms other methods by a large margin, and our proposed LandDiscover50K improves the performance of OVRSISS methods. The proposed dataset and method will be made publicly available.

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