United States

In safety-critical domains such as medical diagnostics and autonomous driving, single-image evidence is sometimes insufficient to reflect the inherent ambiguity of vision problems. Therefore, multiple plausible assumptions that match the image semantics may be needed to reflect the actual distribution of targets and support downstream tasks. However, balancing and improving the diversity and consistency of segmentation predictions under the high-dimensional output spaces and potential multimodal distributions is still challenging. This paper presents Hierarchical Self-Regulation Diffusion (HSRDiff), a unified framework that simulates joint probability distribution over entire labels. Our model self-regulates the balance between the two modes of predicting the label and noise in a novel ``differentiation to unification&quot; pipeline and dynamically fits the optimal path to model the aleatoric uncertainty rooted in observations. In addition, we preserve the high-fidelity reconstruction of the delicate structure in images by leveraging the hierarchical multi-scale condition priors. We validate HSRDiff in three different semantic scenarios. The experimental results show that HSRDiff is superior to the comparison method with a considerable performance gap. Our code is attached to the supplementary material.

AAAI 2025

HSRDiff: A Hierarchical Self-Regulation Diffusion Model for Stochastic Semantic Segmentation

segmentation

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 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.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

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. 

In safety-critical domains such as medical diagnostics and autonomous driving, single-image evidence is sometimes insufficient to reflect the inherent ambiguity of vision problems. Therefore, multiple plausible assumptions that match the image semantics may be needed to reflect the actual distribution of targets and support downstream tasks. However, balancing and improving the diversity and consistency of segmentation predictions under the high-dimensional output spaces and potential multimodal distributions is still challenging. This paper presents Hierarchical Self-Regulation Diffusion (HSRDiff), a unified framework that simulates joint probability distribution over entire labels. Our model self-regulates the balance between the two modes of predicting the label and noise in a novel ``differentiation to unification" pipeline and dynamically fits the optimal path to model the aleatoric uncertainty rooted in observations. In addition, we preserve the high-fidelity reconstruction of the delicate structure in images by leveraging the hierarchical multi-scale condition priors. We validate HSRDiff in three different semantic scenarios. The experimental results show that HSRDiff is superior to the comparison method with a considerable performance gap. Our code is attached to the supplementary material.

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.



We consider the challenge of black-box optimization within hybrid discrete-continuous and variable-length spaces, a problem that arises in various applications, such as decision tree learning and symbolic regression. We propose DisCo-DSO (Discrete-Continuous Deep Symbolic Optimization), a novel approach that uses a generative model to learn a joint distribution over discrete and continuous design variables to sample new hybrid designs. In contrast to standard decoupled approaches, in which the discrete and continuous variables are optimized separately, our joint optimization approach uses fewer objective function evaluations, is robust against non-differentiable objectives, and learns from prior samples to guide the search, leading to significant improvement in performance and sample efficiency. Our experiments on a diverse set of optimization tasks demonstrate that the advantages of DisCo-DSO become increasingly evident as problem complexity grows. In particular, we illustrate DisCo-DSO's superiority over the state-of-the-art methods for interpretable reinforcement learning with decision trees.

DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces

We propose SMMF (Square-Matricized Momentum Factorization), a memory-efficient optimizer that reduces the memory requirement of the widely used adaptive learning rate optimizers, such as Adam, by up to 96%. SMMF enables flexible and efficient factorization of an arbitrary rank (shape) of the first and second momentum tensors during optimization, based on the proposed square-matricization and one-time single matrix factorization. From this, it becomes effectively applicable to any rank (shape) of momentum tensors, i.e., bias, matrix, and any rank-d tensors, prevalent in various deep model architectures, such as CNNs (high rank) and Transformers (low rank), in contrast to existing memory-efficient optimizers that applies only to a particular (rank-2) momentum tensor, e.g., linear layers. We conduct a regret bound analysis of SMMF, which shows that it converges similarly to non-memory-efficient adaptive learning rate optimizers, such as AdamNC, providing a theoretical basis for its competitive optimization capability. In our experiment, SMMF takes up to 96% less memory compared to state-of-the-art memoryefficient optimizers, e.g., Adafactor, CAME, and SM3, while achieving comparable model performance on various CNN and Transformer tasks. The code implementation is available at an anonymous GitHub1 and on the supplementary file.

SMMF: Square-Matricized Momentum Factorization for Memory-Efficient Optimization

The extraordinary ability of generative models emerges as a new trend in image editing and generating realistic images, posing a serious threat to the trustworthiness of multimedia data and driving the research of image manipulation detection and location (IMDL). However, the lack of a large-scale data foundation makes IMDL task unattainable. In this paper, a local manipulation pipeline is designed, incorporating the powerful SAM, ChatGPT and generative models. Upon this basis, we propose the GIM  dataset, which has the following advantages: 1) Large scale,  GIM includes over one million pairs of AI-manipulated images and real images. 2) Rich image content, GIM encompasses a broad range of image classes. 3) Diverse generative manipulation, the images are manipulated images with state-of-the-art generators and various manipulation tasks. The aforementioned advantages allow for a more comprehensive evaluation of IMDL methods, extending their applicability to diverse images. We introduce two benchmark settings to evaluate the existing IMDL methods. In addition, we propose a novel IMDL framework, termed GIMFormer, which consists of a ShadowTracer, Frequency-Spatial block (FSB), and a Multi-Window Anomalous Modeling (MWAM) module. Extensive experiments on the GIM demonstrate that GIMFormer surpasses the previous state-of-the-art approach on two different benchmarks.

GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization

Graph transfer learning endeavors to develop a Graph Neural Network (GNN) model in a fully-labeled source domain, with the intention of deploying it on a target domain that has limited labeled data for inference. We reveal that prevalent graph transfer learning methods are susceptible to the homophily shift problem. This issue arises from the divergence in homophily structures between the source and target graphs, leading to a notable deterioration in the performance of GNN models. In this paper, we introduce a novel Contextual Structural Graph Neural Network (CS-GNN) method, leveraging a tailored attention mechanism to apprehend a variety of local structural cues, facilitating structural knowledge transfer across domains. It features an ego-network module to distill local structural diversity and a moment-based approach to gauge structural patterns without needing ground-truth labels. CS-GNN crafts a feature smoothness matrix from node attributes, guiding a customized attention mechanism for feature aggregation. A group-wise fairness loss is employed to balance learning across various structural patterns, enhancing the model's ability to transfer knowledge across domains.  Comprehensive experiments conducted on six benchmark datasets substantiate the superiority of CS-GNN over the state-of-the-art methods, demonstrating significant improvements in accuracy and robustness against homophily shifts. The source code for CS-GNN is publicly available at https://anonymous.4open.science/r/CS-GNN-ECF6/.

Contextual Structure Knowledge Transfer for Graph Neural Networks

This study challenges strictly guaranteeing ``dissipativity'' of a dynamical system represented by neural networks learned from given time-series data.
Dissipativity is a crucial indicator for dynamical systems that generalizes stability and input-output stability, known to be valid across various systems including robotics, biological systems, and molecular dynamics.
By analytically proving the general solution to the nonlinear Kalman–Yakubovich–Popov (KYP) lemma, which is the necessary and sufficient condition for dissipativity, we propose a differentiable projection that transforms any dynamics represented by neural networks into dissipative ones and a learning method for the transformed dynamics.
Utilizing the generality of dissipativity, our method strictly guarantee  stability, input-output stability, and energy conservation of trained dynamical systems.
Finally, we demonstrate the robustness of our method against out-of-domain input through applications to robotic arms and fluid dynamics.

Learning Deep Dissipative Dynamics

Generating Chain-of-Thought (CoT) before deriving the answer can effectively improve the reasoning capabilities of large language models (LLMs) and significantly improve the accuracy of the generated answer. However, in most cases, the length of the generated CoT is much longer than the desired final answer, which results in additional decoding costs. Furthermore, existing research has discovered that shortening the reasoning steps in CoT, even while preserving the key information, diminishes LLMs' abilities. These phenomena make it difficult to use LLMs and CoT in many real-world applications that only require the final answer and are sensitive to latency, such as search and recommendation. To reduce the costs of model decoding and shorten the length of the generated CoT, this paper presents $\textbf{C}$onditioned $\textbf{C}$ompressed $\textbf{C}$hain-of-$\textbf{T}$hought (C3oT), a CoT compression framework that involves a compressor to compress an original longer CoT into a shorter CoT while maintaining key information and interpretability, a conditioned training method to train LLMs with both longer CoT and shorter CoT simultaneously to learn the corresponding relationships between them, and a conditioned inference method to gain the reasoning ability learned from longer CoT by generating shorter CoT. We conduct experiments over four datasets from arithmetic and commonsense scenarios, showing that the proposed method is capable of compressing the length of generated CoT by up to more than 50% without compromising its effectiveness.

C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness

Modern methods for autonomous driving perception widely adopt multi-modal fusion to enhance 3D scene understanding. However, existing methods suffer from inferior semantic extraction in image encoders that treat all pixels equally, ignoring contextual differences. The generated multi-modal representations also typically lack comprehensive semantic and spatial geometry information, which is crucial for the 3D panoptic segmentation task. In this paper, we propose a novel Semantic-Geometry Fusion Transformer (SGFormer) that extracts adaptive semantic contexts, aggregates geometric information and captures the semantic-geometry fusion. First,  in the Image Branch, we tailor semantic contexts for each pixel with context-guided attention and spatial context alignment to refine semantic details. Second, we transform image and voxel features into point-pixel geometry representations, simultaneously learning  semantic category priors as embeddings to better represent scene geometry and semantics. Finally, to aggregate semantic information with related geometry, we design a semantic-geometry fusion that combines the transformer, effectively capturing semantic-geometry relationships into multi-modal panoptic representations. Notably, SGFormer achieves the state-of-the-art (SOTA)  results on the nuScenes and SemanticPOSS, as well as yielding competitive performance on the SemanticKITTI. Moreover, SGFormer exhibits superior robustness  compared to leading methods, marking an improvement of 2% to 10%.

SGFormer: Semantic-Geometry Fusion Transformer for Multi-modal 3D Panoptic Segmentation

Recently, learning-based Underwater Image Enhancement (UIE) methods have demonstrated promising performance. However, existing learning-based methods still face two challenges for high-fidelity and high-efficiency UIE. 1) They rarely consider the inconsistent degradation levels in different spatial regions and spectral bands simultaneously. 2) They treat all regions equally, ignoring that the regions with high-frequency details are more difficult to reconstruct. To address these challenges, we propose a novel UIE method based on spatial-spectral dual-domain adaptive learning, termed SS-UIE. Specifically, we first introduce a spatial-wise Multi-scale Cycle Selective Scan (MCSS) module and a Spectral-Wise Self-Attention (SWSA) module, both with linear complexity, and combine them in parallel to form a basic Spatial-Spectral block (SS-block). Benefiting from the global receptive field of MCSS and SWSA, SS-block can effectively model the degradation levels of different spatial regions and spectral bands, thereby enabling degradation level-based dual-domain adaptive UIE. By stacking multiple SS-blocks, we build our SS-UIE network. Additionally, a Frequency-Wise Loss (FWL) is introduced to narrow the frequency-wise discrepancy and reinforce the model's attention on the regions with high-frequency details. Extensive experiments validate that the SS-UIE technique significantly outperforms state-of-the-art UIE methods while requiring cheaper computational and memory costs. The code will be publicly available.

Adaptive Dual-domain Learning for Underwater Image Enhancement

Modern Hopfield networks (MHNs) have recently gained significant attention in the field of artificial intelligence because they can store and retrieve large sets of patterns with an exponentially large memory capacity. Recently, it has been proven that MHNs can be understood as a dynamical system defined with Lagrangian functions of memory and feature neurons, where memories associated with in-distribution (ID) training samples are represented as attractors in the feature space. However, one of the primary challenges remains in managing out-of-distribution (OOD) samples because MHNs are formulated under the assumption that all data samples are ID samples. To address this challenge, we propose the rectified Lagrangian (RegLag), a new Lagrangian function for memory neurons that explicitly incorporates an attractor for OOD samples in the dynamical system of MHNs. RecLag is designed to create a trivial point attractor for any interaction matrix, enabling OOD detection by identifying samples that fall into this attractor as OOD. Furthermore, for training MHNs with RecLag, we devise a method based on probabilistic interaction, by which data samples with low probability density values fall into the created attractor. In experiments, we demonstrated the effectiveness of RecLag-based MHNs compared to energy-based OOD detection methods, including those using state-of-the-art Hopfield energies, across nine image datasets.

Rectified Lagrangian for Out-of-Distribution Detection in Modern Hopfield Networks

Generalizable Deepfake Detection aims to develop universal detectors capable of identifying diverse types of forgery images using limited training samples. Recent research has uncovered that the features of large pre-trained models, such as CLIP, can be effectively utilized for deepfake detection via linear classifiers, even on unseen sources. However, two critical issues remain unresolved: 1) understanding why CLIP features are effective for deepfake detection through a linear classifier; and 2) determining how to simply and effectively improve the detection performance of CLIP. In this study, to elucidate the underlying mechanism of CLIP's detection capabilities, we decode the detection features of CLIP into text and perform word frequency analysis. Our findings indicate that CLIP performs deepfake detection by identifying similar concepts (Fig. \ref{fig:fig1} a). Building on this insight, we introduce Category Common Prompt CLIP, called C2P-CLIP, a novel and effective approach designed to augment detection performance. This method employs the category common prompt on the text encoder to inject category concepts into the vision encoder, thereby enhancing its discrimination ability between real and fake items (Fig. \ref{fig:fig1} b). Our C2P-CLIP method achieves a 12.41\% improvement in detection performance compared to the original CLIP, without adding additional parameters during testing. Comprehensive experiments conducted on two widely-used datasets, encompassing 20 generation models, validate the efficacy of our proposed method, demonstrating state-of-the-art performance. The code will be released.

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