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Intent-based grasp generation inherently involves challenges such as manipulation ambiguity and modality gaps. To address these, we propose a novel Retrieval-Augmented Grasp Generation model (RAGG). Our key insight is that when humans manipulate new objects, they initially mimic the interaction patterns observed in similar objects, then progressively adjust hand-object contact. Consequently, we develop RAGG as a two-stage approach, encompassing retrieval-guided generation and structurally stable grasp refinement. In the first stage, we propose a Retrieval-Augmented Diffusion Model (ReDim), which identifies the most relevant interaction instance from a knowledge base to explicitly guide grasp generation, thereby mitigating ambiguity and bridging modality gaps to ensure semantically correct manipulation. In the second stage, we introduce a Progressive Refinement Network (PRN) with Kolmogorov-Arnold Network (KAN) layers to refine the generated coarse grasp, employing a Structural Similarity Index loss to constrain the spatial relationship between the hand and the object, thus ensuring the stability of the grasp. Extensive experiments on the OakInk and GRAB benchmarks demonstrate that RAGG achieves superior results compared to state-of-the-art approach, indicating not only better physical feasibility and controllability but also strong generalization and interpretability for unseen objects.

AAAI 2025

RAGG: Retrieval-Augmented Grasp Generation Model

mult modal vision

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.

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AAAI 2026

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.

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.



Adversarial training incurs significant computational overhead, leading to growing interest in designing inherently robust architectures. We demonstrate that a carefully designed first layer of the neural network can serve as an implicit adversarial noise filter (ANF). This filter is created using a combination of large kernel size, increased convolution filters, and a maxpool operation. We show that integrating this filter as the first layer in architectures such as ResNet, VGG, and EfficientNet results in adversarially robust networks. Our approach achieves higher adversarial accuracies than existing natively robust architectures without adversarial training and is competitive with adversarial-trained architectures across a wide range of datasets. Supporting our findings, we show that (a) the decision regions for our method have better margins, (b) the visualized loss surfaces are smoother, (c) the modified peak signal-to-noise ratio (mPSNR) values at the output of the ANF are higher, (d) high-frequency components are more attenuated, and (e) architectures incorporating ANF exhibit better denoising in Gaussian noise compared to baseline architectures.

First Line of Defense: A Robust First Layer Mitigates Adversarial Attacks

Point cloud segmentation has a wide range of applications in autonomous driving, augmented reality and virtual reality. Multi-modal fusion strategies have received increasing attention in point cloud segmentation recently. Despite the success, existing methods usually generate unnecessary information loss or redundancy. In this paper, we propose $\textbf{FEAST-Mamba}$, a novel $\textbf{FEA}$ture and $\textbf{S}$pa$\textbf{T}$ial aware $\textbf{Mamba}$ network to tackle multi-modal point cloud segmentation. To exploit the complementarity between different modals, we propose a bidirectional orthogonal attention module, where features are first bidirectionally interacted with each other through cross-modal attention, and then orthogonal fusion is used to reduce feature redundancy. Furthermore, a reordering strategy is proposed for the Mamba architecture that takes into account both spatial and semantic information during cross-modal feature ordering. Experiments on two indoor datasets, S3DIS and ScanNet, and three outdoor datasets, Waymo, nuScenes and SemanticKITTI, show that the proposed method achieves state-of-the-art performances.

FEAST-Mamba: FEAture and SpaTial Aware Mamba Network with Bidirectional Orthogonal Fusion for Cross-Modal Point Cloud Segmentation

LIDAR-based 3D object detection and semantic segmentation are critical tasks in 3D scene understanding. Traditional detection and segmentation methods supervise their models through bounding box labels and semantic mask labels. However, these two independent labels inherently contain significant redundancy. This paper aims to eliminate the redundancy by supervising 3D object detection using only semantic labels. However, the challenge arises due to the incomplete geometry structure and boundary ambiguity of point cloud instances, leading to inaccurate pseudo-labels and poor detection results. To address these challenges, we propose a novel method, named Seg2Box. We first introduce a Multi-Frame Multi-Scale Clustering (MFMS-C) module, which leverages the spatio-temporal consistency of point clouds to generate accurate box-level pseudo-labels. Additionally, the Semantic-Guiding Iterative-Mining Self-Training (SGIM-ST) module is proposed to enhance the performance by progressively refining the pseudo-labels and mining the instances without generating pseudo-labels. Experiments on the Waymo Open Dataset and nuScenes Dataset show that our method significantly outperforms other competitive methods by 23.7% and 10.3% in mAP, respectively. The results demonstrate the great label-efficient potential and advancement of our method.

Seg2Box: 3D Object Detection by Point-Wise Semantics Supervision

The advent of Spatial Transcriptomics (ST) has revolutionized understanding of tissue architecture by creating high-resolution maps of gene expression patterns. However, the low capture rate of ST leads to significant sparsity. The aim of imputation is to recover biological signals by imputing the dropouts in ST data to approximate the true expression values. In this paper, we introduce a Spatial Gene Expression Imputation Diffusion model to facilitate ST data imputation, and our model is referred to as SpotDiff. Specifically, we incorporate a spot-gene prompt learning module to capture the association between spots and genes. Further, SpotDiff integrates single-cell RNA sequencing data to impute gene expression at each spot. The proposed approach is able to reduce the uncertainty in the imputation process, since the aggregation of multiple single-cell measurements yield a stable representation of the corresponding spot expression profile. Extensive experiments have been performed to demonstrate that SpotDiff outperforms existing imputation methods across multiple benchmarks in terms of yielding more accurate and biologically relevant gene expression profiles, particularly in highly sparse scenarios.

SpotDiff: Spatial Gene Expression Imputation Diffusion with Single-cell RNA Sequencing Data Integration

The rapid advancement of 3D Generative Adversarial Networks (GANs) has significantly enhanced the diversity and quality of generated 3D images. Despite these breakthroughs, the manipulation capabilities of 3D GANs remain unexplored, presenting substantial challenges for practical applications where user interaction and modification are essential. Current manipulation methods often lack the precision needed for fine-grained attribute manipulation, and struggle to maintain multi-view consistency during the editing process. To address these limitations, we propose 3DHumanEdit, a novel approach for 3D human body part-aware manipulation. 3DHumanEdit leverages multi-modal feature fusion and body part-aware feature alignment to achieve precise manipulation of individual body parts based on detailed text inputs and segmentation images. By exploring 3D prior for accurate editing and enforcing correspondence in latent space, 3DHumanEdit ensures coherence across multiple views. Experiments demonstrate that 3DHumanEdit outperforms existing methods in both editing fidelity and multi-view consistency, offering a robust solution for fine-grained 3D manipulation.

3DHumanEdit: Multi-modal Body Part-aware Conditioning Information Integration for 3D Human Manipulation

Information diffusion prediction aims to predict the next infected user in the information diffusion, which is a critical task for understanding information spread on social platforms. The existing methods mainly focus on the sequences or topology structure in Euclidean space. However, they fail to sufficiently consider the hierarchical structure or power-law structure of the underlying topology from information cascades graphs and social networks, resulting in the distortion of user features. To tackle these issues, we propose an innovative Constrained Temporal Hypergraphs and Graph Neural Networks in Hyperbolic Space (THGNets) framework that is tailored for information diffusion prediction. Specifically, we introduce hyperbolic temporal hypergraphs to alleviate the distortion of user features by hyperbolic hierarchical learning in information cascades. Additionally, it learns the high-order dynamic interaction patterns between users and further integrates the time-consistency constraint to mitigate the instability and non-smoothness of user features in latent space. In parallel, we devise the hyperbolic GCN to investigate the hierarchical structure and user homogeneity on social networks, enhancing the understanding of social relationships. Moreover, the hyperbolic GRU is is employed to capture the potential dependency relationships between the contextual users. Experiments conducted on four public datasets demonstrate that the proposed THGNets significantly outperforms the state-of-the-art methods, thereby validating the superiority and rationality of our approach.

THGNets: Constrained Temporal Hypergraphs and Graph Neural Networks in Hyperbolic Space for Information Diffusion Prediction

In recent years, there have been a growing number of works studying the generalization properties of stochastic gradient descent (SGD) from the perspective of algorithmic stability. However, few of them devote to simultaneously studying the generalization and optimization for the non-convex setting, especially pairwise SGD with heavy-tailed gradient noise. This paper considers the impact of the heavy-tailed gradient noise obeying sub-Weibull distribution on the stability-based learning guarantees for non-convex pairwise SGD by investigating its generalization and optimization jointly. Specifically, based on two novel pairwise $\ell_1$ uniform model stability tools, we firstly bound the generalization error of pairwise SGD in the general non-convex setting after bridging the quantitative relationships between stability and generalization error. Then, we further consider the practical heavy-tailed sub-Weibull gradient noise condition to establish a refined generalization bound without the bounded gradient condition. Finally, sharper error bounds for generalization and optimization are built by introducing the gradient dominance condition. Comparing these results reveals that sub-Weibull gradient noise brings some positive dependencies on the heavy-tailed strength $\theta$ for generalization and optimization. Furthermore, we extend our analysis to the corresponding pairwise minibatch SGD and derive the first stability-based near-optimal generalization and optimization bounds which are consistent with many empirical observations.

Error Analysis Affected by Heavy-Tailed Gradients for Non-Convex Pairwise Stochastic Gradient Descent

In the field of audio-visual learning, most research tasks focus exclusively on short videos. This paper focuses on the more practical Dense Audio-Visual Event Localization (DAVEL) task, advancing audio-visual scene understanding for longer, untrimmed videos.This task seeks to identify and temporally pinpoint all events simultaneously occurring in both audio and visual streams. Typically, each video encompasses dense events of multiple classes, which may overlap on the timeline, each exhibiting varied durations.Given these challenges, effectively exploiting the audio-visual relations and the temporal features encoded at various granularities becomes crucial.To address these challenges, we introduce a novel CCNet, comprising two core modules: the Cross-Modal Consistency Collaboration (CMCC) and the Multi-Temporal Granularity Collaboration (MTGC).Specifically, the CMCC module contains two branches: a cross-modal interaction branch and a temporal consistency-gated branch.The former branch facilitates the aggregation of consistent event semantics across modalities through the encoding of audio-visual relations, while the latter branch guides one modality's focus to pivotal event-relevant temporal areas as discerned in the other modality.The MTGC module includes a coarse-to-fine collaboration block and a fine-to-coarse collaboration block, providing bidirectional support among coarse- and fine-grained temporal features.Extensive experiments on the UnAV-100 dataset validate our module design, resulting in a new state-of-the-art performance in dense audio-visual event localization.

Dense Audio-Visual Event Localization Under Cross-Modal Consistency and Multi-Temporal Granularity Collaboration

Multi-view tensor clustering (MVTC) has gained much attention for its effectiveness in capturing global high-order correlations across views. However, current MVTC methods suffer from two limitations: 1) adopting a two-stage process to learn the latent features for clustering, and 2) either ignoring local similarities within views or treating local similarities and global high-order correlations equally.
In this paper, we propose a smooth low-rank MVTC (SLR-MVTC) method, which aims to extract latent features that are smooth within each view and low-rank across views, enhancing clustering performance. Specifically, we first learn latent features from each view using orthogonal projection and then construct the latent feature tensor by concatenation and rotation. Then, we introduce a new smooth tensor nuclear norm to depict the low-rank components of the low-frequency parts in the feature tensor. Benefiting from the fast Fourier transform along the sample dimension, the obtained low-frequency components effectively capture local smoothness within views, while their low-rank parts further explore global correlations across views. Experimental results on six multi-view datasets demonstrate that SLR-MVTC outperforms state-of-the-art algorithms in terms of clustering performance and CPU time.

SLR-MVTC: Smooth Low-Rank Multi-View Tensor Clustering

The diffusion model has lately been shown to achieve remarkable performances through its ability of generating high quality images. However, current diffusion model studies consider only the static learning of a data distribution from a single data source, which is resulting in catastrophic forgetting when attempting to learn new data. In this paper, we explore a more realistic learning scenario where the training data is continuously acquired for training the model successively. We define first a dynamic diffusion model, under the challenging Online Task-Free Continual Learning (OTFCL) paradigm, and then we propose the Dynamic Expansion Diffusion Model (DEDM) for addressing catastrophic forgetting and data distribution shifts under OTFCL. We propose to add new  diffusion components to a mixture of diffusion models following the evaluation of a criterion which compares the probabilistic representation of the new data with the DEDM model's existing knowledge. In addition, for maintaining an optimal architecture, we propose a component-discarding approach ensuring knowledge diversity while minimizing the total number of parameters in the DEDM. Furthermore, we show how the proposed DEDM can be implemented as a teacher module in a unified framework for representation learning, in which a knowledge distillation approach is proposed for training a student module aiming to compress the teacher's knowledge into a latent space. Our model can be trained in a completely unsupervised learning fashion while ensuring continual data generation and representation learning.

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