United States

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.

AAAI 2025

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

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

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.



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.

Dynamic Expansion Diffusion Learning For Lifelong Generative Modelling

Point cloud data labeling is considered a time-consuming and expensive task in autonomous driving, whereas unsupervised learning can avoid it by learning point cloud representations from unannotated data. In this paper, we propose UOV, a novel 3D unsupervised framework assisted by 2D open-vocabulary segmentation models. It consists of two stages: In the first stage, we innovatively integrate high-quality textual and image features of 2D open-vocabulary models and propose the Tri-Modal contrastive Pre-training (TMP). In the second stage, spatial mapping between point clouds and images is utilized to generate pseudo-labels, enabling cross-modal knowledge distillation. Besides, we introduce the Approximate Flat Interaction (AFI) to address the noise during alignment and label confusion. To validate the superiority of UOV, extensive experiments are conducted on multiple related datasets. We achieved a record-breaking 47.73% mIoU on the annotation-free 3D segmentation task in nuScenes, surpassing the previous best model by 3.13% mIoU. Meanwhile, the performance of fine-tuning with 1% data on nuScenes and SemanticKITTI reached a remarkable 51.75% mIoU and 48.14% mIoU, outperforming all previous pre-trained models.

3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous Driving

Diffusion models have exhibited impressive prowess in the text-to-image task. Recent methods add image-level structure controls, e.g., edge and depth maps, to manipulate the generation process together with text prompts to obtain desired images. This controlling process is globally operated on the entire image, which limits the flexibility of control regions. In this paper, we explore a novel and practical task setting: \textbf{local control}. It focuses on controlling specific local region according to user-defined image conditions, while the remaining regions are only conditioned by the original text prompt. However, it is non-trivial to achieve this goal. The naive manner of directly adding local conditions may lead to the local control dominance problem, which forces the model to focus on the controlled region and neglect object generation in other regions. To mitigate this problem, we propose Regional Discriminate Loss to update the noised latents, aiming at enhanced object generation in non-control regions. Furthermore, the proposed Focused Token Response suppresses weaker attention scores which lack the strongest response to enhance object distinction and reduce duplication. Lastly, we adopt Feature Mask Constraint to reduce quality degradation in images caused by information differences across the local control region. All proposed strategies are operated at the inference stage. Extensive experiments demonstrate that our method can synthesize high-quality images aligned with the text prompt under local control conditions.

Local Conditional Controlling for Text-to-Image Diffusion Models

Refusal-Aware Instruction Tuning (RAIT) enables Large Language Models (LLMs) to refuse to answer unknown questions. By modifying responses of unknown questions in the training data to refusal responses such as ``I don't know", RAIT enhances the reliability of LLMs and reduces their hallucination.
Generally, RAIT modifies training samples base on the correctness of initial LLM's response. However, this crude approach can cause LLMs to excessively refuse answering questions they could have correctly addressed, a problem we call over-refusal.
In this paper, we explore two primary causes of over-refusal:
Static conflict emerges when RAIT data is constructed solely on correctness criteria, causing similar samples in the LLM's feature space to be assigned different labels (original vs. modified "I don't know").
Dynamic conflict occurs due to changes in LLM's knowledge state during fine-tuning, which transforms previous unknown questions into knowns, while the training data settled by initial LLM remains unchanged, resulting in conflicts.
These conflicts cause the trained LLM to misclassify known questions as unknown, resulting in over-refusal.
To address this issue, we introduce Certainty Represented Knowledge Flow for Refusal-Aware Instructions Construction (CRaFT). CRaFT centers on two main contributions: First, we additionally incorporate response certainty to selectively filter and modify data, reducing static conflicts. Second, we implement preliminary rehearsal training to characterize changes in the LLM's knowledge state, which helps mitigate dynamic conflicts during the fine-tuning process. 
We conducted extensive experiments on open-ended question answering and multiple-choice question task. Experiment results show that CRaFT can improve LLM's reliability during the RAIT process. Source code and training data will be released at Github.

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