Singapore

3D Gaussian Splatting (3DGS) has emerged as a powerful representation for 3D scenes, widely adopted due to its exceptional efficiency and high-fidelity visual quality. Given the significant value of 3DGS assets, recent works have introduced specialized watermarking schemes to ensure copyright protection and ownership verification. However, can existing 3D Gaussian watermarking approaches genuinely guarantee robust protection of the 3D assets? In this paper, for the first time, we systematically explore and validate possible vulnerabilities of 3DGS watermarking frameworks. We demonstrate that conventional watermark removal techniques designed for 2D images do not effectively generalize to the 3DGS scenario due to the specialized rendering pipeline and unique attributes of each gaussian primitives. Motivated by this insight, we propose GSPure, the first watermark purification framework specifically for 3DGS watermarking representations. By analyzing view-dependent rendering contributions and exploiting geometrically accurate feature clustering, GSPure precisely isolates and effectively removes watermark-related Gaussian primitives while preserving scene integrity. Extensive experiments demonstrate that our GSPure achieves the best watermark purification performance, reducing watermark PSNR by up to 16.34dB while minimizing degradation to original scene fidelity with less than 1dB PSNR loss. Moreover, it consistently outperforms existing methods in both effectiveness and generalization.

AAAI 2026

Can Protective Watermarking Safeguard the Copyright of 3D Gaussian Splatting?

poster

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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-26 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.<br><br>

To access this event page, you need to log in with the **email address you registered with**. <br>Access credentials will be sent to your email from Underline -  subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Large vision-language models (VLMs) for autonomous driving (AD) are evolving beyond perception and cognition tasks toward motion planning. However, we identify two critical challenges in this direction: (1) VLMs tend to learn shortcuts by relying heavily on history input information, achieving seemingly strong planning results without genuinely understanding the visual inputs; and (2) the chain-of-thought (COT) reasoning processes are always misaligned with the motion planning outcomes, and how to effectively leverage the complex reasoning capability to enhance planning remains largely underexplored. In this paper, we start from a small-scale domain-specific VLM and propose **Drive-R1**, designed to bridge the scenario reasoning and motion planning for AD. **Drive-R1** first undergoes the supervised finetuning on an elaborate dataset containing both long and short COT data. **Drive-R1** is encouraged to reason step-by-step from visual input to final planning decisions. Subsequently, **Drive-R1** is trained within a reinforcement learning framework that incentivizes the discovery of reasoning paths that are more informative for planning, guided by rewards based on predicted trajectories and meta actions. Experimental evaluations on the nuScenes and DriveLM-nuScenes benchmarks demonstrate that **Drive-R1** achieves superior performance compared to existing state-of-the-art VLMs. We believe that **Drive-R1**presents a promising direction for bridging reasoning and planning in AD, offering methodological insights for future research and applications.

Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning

Self-supervised graph representation learning (GRL) typically generates paired graph augmentations from each graph to infer similar representations for augmentations of the same graph, but distinguishable representations for different graphs. While effective augmentation requires both semantics-preservation and dataperturbation, most existing GRL methods focus solely on data-perturbation, leading to suboptimal solutions. To fill the gap, in this paper, we propose a novel method, Explanation-Preserving Augmentation (EPA), which leverages graph explanation for semantics-preservation. EPA first uses a small number of labels to train a graph explainer, which infers the subgraphs that explain the graph’s label. Then these explanations are used for generating semantics-preserving augmentations for boosting self-supervised GRL. Thus, the entire process, namely EPA-GRL, is semi-supervised. We demonstrate theoretically, using an analytical example, and through extensive experiments on a variety of benchmark datasets, that EPA-GRL outperforms the state-of-the-art (SOTA) GRL methods with semantics-agnostic augmentations.

Explanation-Preserving Augmentation for Semi-Supervised Graph Representation Learning

3D full-scene segmentation technology has demonstrated great potential driven by large models, but it often faces challenges of incomplete scenes and identification of invisible classes in practical applications. To address this, we propose the LR-AdaInSeg method, which significantly enhances the model’s generalization ability in incomplete scenes through two key innovations: First, we design a Bayesian Low-Rank Module, which effectively solves the problem of feature space redundancy through dynamic optimization of the network structure, improving adaptability to incomplete scenes. Second, we combine graph contrastive clustering with the Low-Rank module, leveraging its robust feature representation capability to achieve accurate differentiation of invisible classes. In terms of implementation, we build a multi-scale feature extraction framework based on the 3D U-Net and utilize the 3D prompt points and their 2D masks as supervisory signals to achieve effective fusion of geometric and semantic information. Experiments show that our method achieves advanced performance on multiple benchmarks such as ScanNet, particularly excelling in handling incomplete scenes and invisible class objects.

LR-AdaInSeg:Adaptive Instance Segmentation of Incomplete 3D Scenes Driven by Low-Rank Networks

Noisy correspondence, characterized by mismatches in cross-modal data pairs, presents a significant challenge for real-world applications. Current approaches primarily rely on direct cross-modal pairwise similarity metrics, which suffer from two critical limitations: noise sensitivity, where direct similarity calculations are easily corrupted by noisy or ambiguous instances, and contextual blindness, where isolated pairwise comparisons fail to exploit the rich semantic context embedded in neighboring instances. To address this issue, we propose to improve noise correspondence discrimination through a well-designed \textbf{D}ynamic \textbf{N}eighborhood \textbf{S}emantic association verification paradigm, namely \textit{\textbf{DNS}}. Specifically, we hypothesize that the matching degree of current samples can be quantified through the interrelationships among their respective semantic neighbors. For this reason, we develop a novel semantic drift distance and local relation proximity based on dynamic neighborhood association. Furthermore, beyond implicit approaches to semantic gap modeling in cross-modal data, we introduce an explicit decomposition framework that disentangles the gap into the semantic orientation and scalar magnitude. Through the strategic integration of these proposed mechanisms, \textit{\textbf{DNS}} achieves substantial enhancement in noisy correspondence discrimination, yielding remarkable performance gains. Extensive experiments on three widely-used benchmark datasets, including Flickr30K, MS-COCO, and Conceptual Captions, demonstrate the superiority of \textit{\textbf{DNS}} over state-of-the-art methods.

Boosting Noisy Correspondence Discrimination via Dynamic Neighborhood Semantic Verification

Large language models (LLMs) perform in-context learning (ICL) with minimal supervised examples, which benefits various natural language processing (NLP) tasks. One of the critical research focus is the selection of prompt demonstrations. Current approaches typically employ retrieval models to select the top-K most semantically similar examples as demonstrations. However, we argue that existing methods are limited since the label consistency is not guaranteed during demonstration selection. Our cognition derives from the Bayesian view of ICL and our rethinking of ICL from the transductive label propagation perspective. We treat ICL as a transductive learning method and incorporate latent concepts from Bayesian view and deduce that similar demonstrations guide the concepts of query, with consistent labels serving as estimates. Based on this understanding, we establish a label propagation framework to link label consistency with propagation error bounds. To model label consistency, we propose a data synthesis method, leveraging both semantic and label information, and use TopK sampling with Synthetic Data (TopK-SD) to acquire demonstrations with consistent labels. TopK-SD outperforms original TopK sampling on multiple benchmarks. Our work provides a new perspective for understanding the working mechanisms within ICL.

Rethinking Label Consistency of In-Context Learning: An Implicit Transductive Label Propagation Perspective

Sparse neural systems are gaining traction for efficient continual learning due to their modularity and low interference. Architectures like Sparse Distributed Memory Multi-Layer Perceptrons (SDMLP) construct task-specific subnetworks via Top-K activation and have shown resilience against catastrophic forgetting. However, their rigid modularity poses two fundamental challenges: (1) the isolation of sparse subnetworks severely limits cross-task knowledge reuse; and (2) increased sparsity reduces interference but often degrades performance due to constrained feature sharing.
We propose Selective Subnetwork Distillation (SSD), a structurally guided continual learning framework that treats distillation not as a regularizer, but as a topology-aligned information conduit. By identifying neurons with high activation frequency, SSD selectively distills knowledge within previous Top-K subnetworks and output logits—without requiring replay or task labels—preserving both sparsity and functional specialization.Unlike conventional distillation, SSD operates under hard modular constraints and enables structural realignment without altering the sparse architecture.While our method is validated on SDMLP, its structure-aligned mechanism has the potential to generalize to other sparse networks as a plug-in module for promoting representation sharing.Comprehensive experiments on Split CIFAR-10, CIFAR-100, and MNIST demonstrate that SSD improves accuracy, retention, and manifold coverage, offering a structurally grounded solution to sparse continual learning.

Distillation-Guided Structural Transfer for Continual Learning Beyond Sparse Distributed Memory

Recent advances in generative AI have accelerated the production of ultra-high-resolution visual content. However, traditional image formats face significant limitations in efficient compression and real-time decoding, which restricts their applicability on end-user devices. Inspired by 3D Gaussian Splatting, 2D Gaussian image models have achieved notable progress in enhancing image representation efficiency and quality. Nevertheless, existing methods struggle to balance compression ratios and reconstruction fidelity in ultra-high-resolution scenarios. To address these challenges, we propose SmartSplat, a highly adaptive and feature-aware GS-based image compression framework that effectively supports arbitrary image resolutions and compression ratios. By leveraging image-aware features such as gradients and color variances, SmartSplat introduces a Gradient-Color Guided Variational Sampling strategy alongside an Exclusion-based Uniform Sampling scheme, significantly improving the non-overlapping coverage of Gaussian primitives in pixel space. Additionally, a Scale-Adaptive Gaussian Color Sampling method is proposed to enhance the initialization of Gaussian color attributes across scales. Through joint optimization of spatial layout, scale, and color initialization, SmartSplat can efficiently capture both local structures and global textures of images using a limited number of Gaussians, achieving superior reconstruction quality under high compression ratios. Extensive experiments on DIV8K and a newly created 16K dataset demonstrate that SmartSplat significantly outperforms state-of-the-art methods at comparable compression ratios and surpasses their compression limits, exhibiting strong scalability and practical applicability. This framework can effectively alleviate the storage and transmission burdens of ultra-high-resolution images, providing a robust foundation for future high-efficiency visual content processing.

SmartSplat: Feature-Smart Gaussians for Scalable Compression of Ultra-High-Resolution Images

Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from low-resolution (LR) inputs. While recent methods have advanced this task through architectural innovations and generative modeling, but they often leads to semantically inconsistent structures and unrealistic textures, particularly under high magnification. To mitigate these limitations, we draw inspiration from the human artistic process of “structuring before detailing” and propose a progressive prior-guided restoration strategy. Specifically, we first introduce a Sketching Structure Prior (SSP) module that embeds global semantics and refines local geometry through implicit parsing guidance and explicit spatial modulation. Then, a Associative Texture Prior (ATP) module leverages a High-Quality Dictionary (HD) learned from high-quality reconstruction to guide fine-grained detail recovery. Finally, to unify structure and detail features, we design a Holistic Prior Fusion (HPF) module that adaptively integrates them within semantically consistent facial regions. Extensive evaluations on CelebA and Helen datasets demonstrate that our method achieves superior performance in both structural fidelity and texture realism compared to existing state-of-the-art approaches.

PortraitSR: Artist-Inspired Prior Learning for Progressive Face Super-Resolution

Parameter-efficient fine-tuning (PEFT) has become a popular way to adapt large pre-trained models to new tasks. Most PEFT methods update only a small subset of parameters while freezing the rest, avoiding redundant computation. As they maximize the absolute size of the updates without regard to the parameters’ original scale, the resulting changes in model behavior can be minimal. In contrast, we maximize updates relative to each parameter’s scale, yielding more meaningful downstream adaptation. We propose Gradient-to-Weight Ratio and Entropy-guided Masking (GEM), a parameter scale-aware, distribution-sensitive sparse fine-tuning framework. GEM prioritizes parameters whose updates are significant in proportion to their initial pre-trained values. It also adaptively determines how many parameters to tune at each layer based on the entropy of parameter values, thereby making the most effective use of the computational budget in PEFT. Our empirical study demonstrates the efficacy of GEM on both general-domain tasks (GLUE and SuperGLUE) and domain-specific tasks (GSM8k and MBPP), achieving up to a 1.6% improvement in fine-tuning accuracy over full fine-tuning while updating only 0.1% of model parameters.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation

Large language models have demonstrated remarkable capabilities in complex mathematical reasoning tasks, but they inevitably generate errors throughout multi-step solutions. Process-level Reward Models (PRMs) have shown great promise by providing supervision and evaluation at each intermediate step, thereby effectively improving the models’ reasoning abilities. However, training effective PRMs requires high-quality process reward data, yet existing methods for constructing such data are often labour-intensive or inefficient. In this paper, we propose an uncertainty-driven framework for automated process reward data construction, encompassing both data generation and annotation processes for PRMs. Additionally, we identify the limitations of both majority vote and PRMs, and introduce two generic uncertainty-aware output aggregation methods: Hybrid Majority Reward Vote and Weighted Reward Frequency Vote, which combine the strengths of majority vote with PRMs. Extensive experiments on ProcessBench, MATH, and GSMPlus show the effectiveness and efficiency of the proposed PRM data construction framework, and demonstrate that the two output aggregation methods further improve the mathematical reasoning abilities across diverse PRMs.

Content not yet available

Downloads

Next from AAAI 2026

Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Content not yet available

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from AAAI 2026

Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads