United States

Geo-entity resolution involves linking records that refer to the same entities across different spatial datasets, which underpins location-based services. Given the varying quality and reliability of geospatial data, this task is known to be challenging, as directly comparing the semantic-centric representations of two entities is no longer reliable. To robustify geo-entity resolution in this context, the main research question is how to effectively extend the current semantics-centric representations of geo-entity with geographical context from its spatial neighbors. Existing methods consider names from neighbor entities, but they struggle to fully utilize the unaligned neighbor attributes. In this paper, we investigate the representation of geographical context for robust geo-entity resolution and propose two adaptations that efficiently leverage unaligned geo-entity attributes across spatial neighbors: (1) A plugin module, namely Unaligned Message-Passing layer (UMP), that propagates unaligned neighbor features to integrate geo-context into the token embeddings output by language model; (2) a contextualized pretraining framework (CP) that allows the former to leverage unlabelled geo-entity data. Experiments show that our method surpasses the baseline methods, achieving higher F1 scores on 8 real-world geo-datasets in terms of robustness, with an improvement of up to 7.9%. The ablation study further justifies our proposal.

AAAI 2025

Unaligned Message-Passing and Contextualized-Pretraining for Robust Geo-Entity Resolution

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
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EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

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.



Existing learning-based stereo image codec adopt sophisticated transformation with simple entropy models derived from single image codecs to encode latent representations. However, those entropy models struggle to effectively capture the spatial-disparity characteristics inherent in stereo images, which leads to suboptimal rate-distortion results. In this paper, we propose a stereo image compression framework, named CAMSIC. CAMSIC independently transforms each image to latent representation and employs a powerful decoder-free Transformer entropy model to capture both spatial and disparity dependencies, by introducing a novel content-aware masked image modeling (MIM) technique. Our content-aware MIM facilitates efficient bidirectional interaction between prior information and estimated tokens, which naturally obviates the need for an extra Transformer decoder. Experiments show that our stereo image codec achieves state-of-the-art rate-distortion performance on two stereo image datasets Cityscapes and InStereo2K with fast encoding and decoding speed.

CAMSIC: Content-aware Masked Image Modeling Transformer for Stereo Image Compression

While existing semi-supervised object detection (SSOD) methods perform well in general scenes, they encounter challenges in handling oriented objects in aerial images. 
We experimentally find three gaps between general and oriented object detection in semi-supervised learning: 
1) Sampling inconsistency: the common center sampling is not suitable for oriented objects with larger aspect ratios when selecting positive labels from labeled data. 
2) Assignment inconsistency: balancing the precision and localization quality of oriented pseudo-boxes poses greater challenges which introduces more noise when selecting positive labels from unlabeled data. 
3) Confidence inconsistency: there exists more mismatch between the predicted classification and localization qualities when considering oriented objects, affecting the selection of pseudo-labels. 
Therefore, we propose a Multi-clue Consistency Learning (MCL) framework to bridge gaps between general and oriented objects in semi-supervised detection. 
Specifically, considering various shapes of rotated objects, the Gaussian Center Assignment is specially designed to select the pixel-level positive labels from labeled data. 
We then introduce the Scale-aware Label Assignment to select pixel-level pseudo-labels instead of unreliable pseudo-boxes, which is a divide-and-rule strategy suited for objects with various scales. 
The Consistent Confidence Soft Label is adopted to further boost the detector by maintaining the alignment of the predicted results. 
Comprehensive experiments on DOTA-v1.5 and DOTA-v1.0 benchmarks demonstrate that our proposed MCL can achieve state-of-the-art performance in the semi-supervised oriented object detection task.

Multi-clue Consistency Learning to Bridge Gaps Between General and Oriented Object in Semi-supervised Detection

Multi-source domain adaptation (MSDA), which utilizes multiple source domains to align the distribution of a single target domain, is a popular and challenging setting in domain adaptation (DA). However, existing MSDA approaches are difficult to obtain sufficient target domain knowledge, which serve as the transfer object. Furthermore, the target distributions are confused in the real world, i.e., the model cannot obtain the domain labels of target domains. To tackle these problems, we consider a more realistic DA setting Multi-Source Blended-Target Domain Adaptation (MBDA) and propose an Invertible Projection and Conditional Alignment (IPCA) method. Specifically, to reduce the impact of the distribution discrepancy, we construct an invertible projection for the source and blended-target domains. Then, we adopt a projection consistency regularization to our model, which makes the model more robust on the domain-specific parts. In addition, because the labels of the blended-target domain are unseen, we introduce conditional discrepancy to obtain the domain-level discriminative information and guide the classifier to serve as the discriminator, which is suitable for MBDA settings. Extensive experiment results on the ImageCLEF-DA, Office-Home, and DomainNet datasets validate the effectiveness of our method.

Invertible Projection and Conditional Alignment for Multi-source Blended-target Domain Adaptation

Visual prompt tuning-based continual learning (CL) methods have shown promising performance in exemplar-free scenarios, where their key component can be viewed as a prompt generator. Existing approaches generally rely on freezing old prompts, slow updating and task discrimination for prompt generators to preserve stability and minimize forgetting. In contrast, we introduce a novel approach that trains a consistent prompt generator to ensure stability during CL. Consistency means that for any instance from an old task, its corresponding instance-ware prompt generated by the prompt generator remains consistent even as the generator continually updates in a new task. This ensures that the representation of a specific instance remains stable across tasks and thereby prevents forgetting. We employ a mixture of experts (MoE) as the prompt generator, which contains a router and multiple experts. By deriving conditions sufficient to achieve the consistency for the MoE prompt generator, we demonstrate that: during training in a new task, if the router and experts update in the directions orthogonal to the subspaces spanned by old input features and gating vectors, respectively, the consistency can be theoretically guaranteed. To implement this orthogonality, we project parameter gradients to those orthogonal directions using the orthogonal projection matrices computed via the null space method. Extensive experiments on four class-incremental learning benchmarks validate the effectiveness and superiority of our approach. Our code is available in the supplementary material.

Training Consistent Mixture-of-Experts-Based Prompt Generator for Continual Learning

This paper presents a comprehensive study on the role of Classifier-Free Guidance (CFG) in text-conditioned diffusion models from the perspective of inference efficiency. In particular, we relax the default choice of applying CFG in all diffusion steps and instead propose to search for more efficient guidance policies. We formulate the discovery of such policies in the framework of differentiable neural architecture search. Our findings suggest that, as denoising progresses, the updates produced by CFG become increasingly aligned with simple conditional steps, which renders CFG's additional neural network evaluation redundant, especially in the second half of the denoising process. Building upon this insight, we propose "Adaptive Guidance" (AG), an efficient variant of CFG that adaptively omits network evaluations when the denoising process displays convergence. Our experiments demonstrate that AG preserves CFG's image quality while reducing computation by 25%. Thus, AG constitutes a plug-and-play alternative to Guidance Distillation, achieving 50% of the speed-ups of the latter, while being training-free and retaining the capacity to handle negative prompts. We conclude by uncovering further redundancies of CFG in the first half of the diffusion process, showing that entire neural network evaluations can be replaced by simple affine transformations of past score estimates.

Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models

Although pre-trained large vision foundation models (VFM) yield superior results on various downstream tasks, full fine-tuning is often impractical due to its high computational cost and storage requirements. Recent advancements in parameter-efficient fine-tuning (PEFT) of VFM for image classification show significant promise. However, the application of PEFT techniques to dense prediction tasks remains largely unexplored. 
Our analysis of existing methods reveals that the underlying premise of utilizing low-rank parameter matrices, despite their efficacy in specific applications, may not be adequately suited for dense prediction tasks.
To this end, we propose a novel PEFT learning approach tailored for dense prediction tasks, namely VFM-Adapter. Specifically, the VFM-Adapter introduces a hybrid operation mapping technique that seamlessly integrates local information with global modeling to the adapter module. It capitalizes on the distinct inductive biases inherent in different operations. Additionally, we dynamically generate parameters for the VFM-Adapter, enabling flexibility of feature extraction given specific inputs. To validate the efficacy of VFM-Adapter, we conduct extensive experiments across object detection, semantic segmentation, and instance segmentation tasks. Results across multiple benchmarks consistently demonstrate the superiority of our method over previous approaches. Notably, with only 3\% of the trainable parameters of the SAM-Base backbone, our approach achieves competitive or even superior performance compared to full fine-tuning. The code will be available.

VFM-Adapter: Adapting Visual Foundation Models for Dense Prediction with Dynamic Hybrid Operation Mapping

Missense mutations could affect the Liquid-Liquid Phase Separation (LLPS) propensity of proteins and lead to aberrant phase-separating behaviours, which are recently found to be associated with many diseases including Alzheimer's and cancer. However, the regulatory role of mutations in LLPS remains unclear due to challenges in accurately characterizing the LLPS ability of mutants, including the high similarity in features, lack of labeled data, and vast amounts of data involved. To bridge this gap and facilitate the discovery of therapeutic strategies, we propose the first machine learning-based guider for protein phase-separating behaviour alteration, PScalpel. PScalpel leverages both structural information and an auxiliary tasks-based graph contrastive learning framework to distinguish the mutants’ LLPS ability, and incorporates a genetic algorithms-based recommendation method to identify mutants with desired LLPS properties. Comprehensive computational and biological experiments validate the effectiveness of PScalpel as a versatile tool for guiding alterations in protein phase separation behavior.

PScalpel: A Machine Learning-based Guider for Protein Phase-Separating Behaviour Alteration

We propose iMoT, an innovative  Transformer-based inertial odometry method that retrieves cross-modal information from motion and rotation modalities for accurate positional estimation. Unlike prior work, during the encoding of the motion context, we introduce Progressive Series Decoupler at the beginning of each encoder layer to stand out critical motion events inherent in acceleration and angular velocity signals. 
To better aggregate cross-modal interactions, we present Adaptive Positional Encoding, which dynamically modifies positional embeddings for temporal discrepancies between different modalities.
During decoding, we introduce a small set of learnable query motion particles as priors to model motion uncertainties within velocity segments. Each query motion particle is intended to draw cross-modal features dedicated to a specific motion mode, all taken together allowing the model to refine its understanding of motion dynamics effectively. Lastly, we design a dynamic scoring mechanism to stabilize iMoT's optimization by considering all aligned motion particles at the final decoding step, ensuring robust and accurate velocity segment estimation. Extensive evaluations on various inertial datasets demonstrate that iMoT significantly outperforms state-of-the-art methods in delivering superior robustness and accuracy in trajectory reconstruction.

iMoT: Inertial Motion Transformer for Inertial Navigation

Vision-based 3D occupancy prediction has become a popular research task due to its versatility and affordability. Nowadays, conventional methods usually project the image-based vision features to 3D space and learn the geometric information through the attention mechanism, enabling the 3D semantic occupancy prediction. However, these works usually face two main challenges: 1) Limited geometric information. Due to the lack of geometric information in the image itself, it is challenging to directly predict 3D space information, especially in large-scale outdoor scenes. 2) Local restricted interaction. Due to the quadratic complexity of the attention mechanism, they often use modified local attention to fuse features, resulting in a restricted fusion. To address these problems, in this paper, we propose a language-assisted 3D semantic occupancy prediction network, named LOMA. In the proposed vision-language framework, we first introduce a VL-aware Scene Generator (VSG) module to generate the 3D language feature of the scene. By leveraging the vision-language model, this module provides implicit geometric knowledge and explicit semantic information from the language. Furthermore, we present a Tri-plane Fusion Mamba (TFM) block to efficiently fuse the 3D language feature and 3D vision feature. The proposed module not only fuses the two features with global modeling but also avoids too much computation costs. Experiments on the SemanticKITTI and SSCBench-KITTI360 datasets show that our algorithm achieves new state-of-the-art performances in both geometric and semantic completion tasks. Our code will be open soon.

LOMA: Language-assisted Semantic Occupancy Network via Triplane Mamba

Video object detection has made significant progress in recent years thanks to convolutional neural networks (CNNs) and vision transformers (ViTs). CNNs excel at capturing local features but struggle to model global representations. Conversely, ViTs can capture long-range global features but face challenges in representing local feature details. Off-the-shelf video object detection methods solely rely on CNNs or ViTs to conduct feature aggregation, which hampers their capability to simultaneously leverage global and local information, thereby resulting in limited detection performance. In this paper, we propose a Transformer-GraphFormer Blender Network (TGBFormer) for video object detection, with three key technical improvements to fully exploit the advantages of transformers and graph convolutional networks while compensating for their limitations. First, we develop a spatial-temporal transformer module to aggregate global contextual information, constituting global representations with long-range feature dependencies. Second, we introduce a spatial-temporal GraphFormer module that utilizes local spatial and temporal relationships to aggregate features, generating new local representations that are complementary to the transformer outputs. Third, we design a global-local feature blender module to adaptively couple transformer-based global representations and GraphFormer-based local representations. Extensive experiments demonstrate that our TGBFormer establishes new state-of-the-art results on the ImageNet VID dataset. Particularly, our TGBFormer achieves 86.5% mAP while running at around 41.0 FPS on a single Tesla A100 GPU. The code will be available at Github.

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