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In real-world scenarios, acquiring dense depth measurements is challenging, so typically sparse depth data is obtained. This has led to studies in depth completion to complete depth maps. Existing methods have primarily focused on in-domain generalization, which targets performance on specific datasets, while domain-generalizable depth completion remains a challenge due to domain shifts. In this paper, we leverage the depth prior provided by a pre-trained depth diffusion model, which implicitly understands how to fill in depth information. By utilizing the comprehensive prior knowledge across various scenes, we enhance the generalizability and quality of depth maps in depth completion. We present the prior-based depth completion pipeline that effectively integrates sparse measurements with relative depth prior, enabling zero-shot metric depth completion. To achieve desirable solutions, we perform an adaptation during the reverse sampling process using the optimization loop as hard guidance. Additionally, we propose the outlier filtering method based on a prior about the depth affinity relationships. We also propose the outlier filtering method based on a prior about the depth affinity relationships. Our zero-shot adaptation method demonstrates generalization across various domain datasets, achieving an average performance improvement of 22.8% over previous state-of-the-art methods, and improves spatial understanding by sharpening details.

AAAI 2025

Zero-shot Depth Completion via Test-time Alignment with Affine-invariant Depth Prior

vision for robotics

autonomous driving

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-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

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.

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 search scenarios, user experience can be hindered by erroneous queries due to typos, voice errors, or knowledge gaps. Therefore, query correction is crucial for search engines. Current correction models, usually small models trained on specific data, often struggle with queries beyond their training scope or those requiring contextual understanding. While the advent of Large Language Models (LLMs) offers a potential solution, they are still limited by their pre-training data and inference cost, particularly for complex queries, making them not always effective for query correction. To tackle these, we propose $\mathrm{Trigger}^3$, a framework that integrates the traditional correction model and LLM for query correction, capable of adaptively choosing the appropriate correction method based on the query and the correction results from the traditional correction model and LLM. $\mathrm{Trigger}^3$ first employs a correction trigger to filter out correct queries. Incorrect queries are then corrected by the traditional correction model. If this fails, an LLM trigger is activated to call the LLM for correction. Finally, for queries that no model can correct, a fallback trigger decides to return the original query. Extensive experiments demonstrate $\mathrm{Trigger}^3$ outperforms correction baselines while maintaining efficiency.

Trigger$^3$: Refining Query Correction via Adaptive Model Selector

Recent large-scale pre-trained diffusion models have demonstrated a powerful generative ability to produce high-quality videos from detailed text descriptions. However, exerting control over the motion of objects in videos generated by any video diffusion model remains
a challenging problem. In this paper, we propose a novel zero-shot moving object trajectory control framework, Motion-Zero, to enable arbitrary single-object-trajectory control for the text-to-video diffusion model.  To this end, an initial noise prior module is designed to provide a position-based prior to improve the stability of the appearance of the moving object and the accuracy of position. In addition, based on the attention map of the U-Net, spatial constraints are directly applied to the denoising process of diffusion models, which further ensures the positional consistency of moving objects during the inference. Furthermore, temporal consistency is guaranteed with a proposed shift temporal attention mechanism. Our method can be flexibly applied to various state-of-the-art video diffusion models without any training process. Extensive experiments demonstrate our proposed method can control the motion trajectories of arbitrary objects while preserving the original ability to generate high-quality videos.

Motion-Zero: A Zero-Shot Trajectory Control Framework of Moving Object for Diffusion-Based Video Generation

View transformation robustness (VTR) is critical for deep-learning-based multi-view 3D object reconstruction models, which indicates the methods' stability under inputs with various view transformations. However, existing research seldom focused on view transformation robustness in multi-view 3D object reconstruction. One direct way to improve the models' VTR is to produce data with more view transformations and add them to model training.  Recent progress on large vision models, particularly Stable Diffusion models, has provided great potential for generating 3D models or synthesizing novel view images with only a single image input. Directly deploying these models at inference consumes heavy computation resources and their robustness to view transformations is not guaranteed either. To fully utilize the power of Stable Diffusion models without extra inference computation burdens, we propose to generate novel views with Stable Diffusion models for better view transformation robustness. Instead of synthesizing random views, we propose a reconstruction error-guided view selection method, which considers the reconstruction errors' spatial distribution of the 3D predictions and chooses the views that could cover the reconstruction errors as much as possible. The methods are trained and tested on sets with large view transformations to validate the 3D reconstruction models' robustness to view transformations. Extensive experiments demonstrate that the proposed method can outperform state-of-the-art 3D reconstruction methods and other view transformation robustness comparison methods.

View Transformation Robustness for Multi-View 3D Object Reconstruction with Reconstruction Error-Guided View Selection

Dynamic quantization has attracted rising attention in image super-resolution (SR) as it expands the potential of heavy SR models onto mobile devices while preserving competitive performance. Most current methods explore layer-to-bit configuration upon varying local regions, adaptively allocating the bit to each layer and patch. Despite the benefits, they still fall short in the tradeoff of SR accuracy and quantization efficiency. Apart from this, adapting the quantization level for each layer individually can disturb the original inter-layer relationships, thus diminishing the representation capability of quantized models. In this work, we propose Granular-DQ, which takes advantage of multi-granularity clues and local patch statistics, achieving a distinctive patch-wise and layer-invariant dynamic quantization paradigm. Specifically, Granular-DQ initiates by developing a granularity-bit controller to apprehend the coarse-to-fine granular representations of local patches, matching their proportional contribution to the entire image to determine the proper bit-width allocation. On this premise, we investigate the interrelationships between bit-width and information density within high-bit patches, establishing a soft gate that enables further fine-grained dynamic bit adaption. Extensive experiments validate the superiority of Granular-DQ in the trade-off between efficiency and accuracy over recent state-of-the-art methods on various SR models. Code is available in our supplementary material.

Thinking in Granularity: Dynamic Quantization for Image Super-Resolution by Intriguing Multi-Granularity Clues

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.

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

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.

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Trigger$^3$: Refining Query Correction via Adaptive Model Selector

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