AAAI 2025 Papers

Existing hands datasets are largely short-range and the interaction is weak due to the self-occlusion and self-similarity of hands, which can not yet fit the need for interacting hands motion generation. To rescue the data scarcity, we propose HandDiffuse12.5M, a novel and real dataset that consists of temporal sequences with strong two-hand interactions. HandDiffuse12.5M has the largest scale and richest interactions among the existing two-hand datasets. We further present a strong baseline method HandDiffuse for the controllable motion generation of interacting hands using various controllers. Specifically, we apply the diffusion model as the backbone and design two motion representations for different controllers. To reduce artifacts, we also propose Interaction Loss which explicitly quantifies the dynamic interaction process. Our HandDiffuse enables various applications, i.e., motion in-betweening and trajectory controled generation. Experiments show that our method outperforms the state-of-the-art techniques in motion generation. The vivid two-hand motions generated by our method can also construct synthetic datasets and enhances the accuracy of existing hand motion capture algorithms.

Text-to-image generation models have achieved remarkable advancements in recent years, aiming to produce realistic images from textual descriptions. However, these models often struggle with generating anatomically accurate representations of human hands. The resulting images frequently exhibit issues such as incorrect numbers of fingers, unnatural twisting or interlacing of fingers, or blurred and indistinct hands. These issues stem from the inherent complexity of hand structures and the difficulty in aligning textual descriptions with precise visual depictions of hands. To address these challenges, we propose a novel approach named Hand1000 that enables the generation of realistic hand images with target gesture using only 1,000 training samples. The training of Hand1000 is divided into three stages with the first stage aiming to enhance the model’s understanding of hand anatomy by using a pre-trained hand gesture recognition model to extract gesture representation. The second stage further optimizes text embedding by incorporating the extracted hand gesture representation, to improve alignment between the textual descriptions and the generated hand images. The third stage utilizes the optimized embedding to fine-tune the Stable Diffusion model to generate realistic hand images. In addition, we construct the first publicly available dataset specifically designed for text-to-hand image generation. Based on the existing hand gesture recognition dataset, we adopt advanced image captioning models and LLaMA3 to generate high-quality textual descriptions enriched with detailed gesture information. Extensive experiments demonstrate that Hand1000 significantly outperforms existing models in producing anatomically correct hand images while faithfully representing other details in the text, such as faces, clothing and colors.

Vertical federated learning (VFL) trains model when the features of data samples are scattered over multiple clients. To improve efficiency, a promising approach is to find a coreset of the data samples and use it as a smaller training set. However, existing methods produce a large coreset when there are many clients and have long running time. To address these problems, we propose HaCore for efficient coreset construction in VFL setting. HaCore first employs locality sensitive hashing (LSH) to map features to bit signatures locally on the clients, and then merges the local signatures for k-medoids clustering. Data samples that correspond to the medoids are added to the coreset. The core idea is that the distance of original data samples can be approximated by the Hamming distance between their LSH-based bit signatures. To accelerate k-medoids, we utilize an inverted index to search the nearest medoid and a bit-counting method to quickly compute the aggregate distance from many signatures to a medoid. We evaluate HaCore on 5 datasets and compare with state-of-the-art coreset construction methods for VFL. The results show that HaCore accelerates the best-performing baseline by over 45x and matches the accuracy of training with all samples.

Hypergraphs are powerful mathematical structures that can model complex, high-order relationships in various domains, including social networks, bioinformatics, and recommender systems. However, generating realistic and diverse hypergraphs remains challenging due to their inherent complexity and lack of effective generative models. In this paper, we introduce a diffusion-based Hypergraph Generation (HYGENE) method that addresses these challenges through a progressive local expansion approach. HYGENE works on the bipartite representation of hypergraphs, starting with a single pair of connected nodes and iteratively expanding it to form the target hypergraph. At each step, nodes and hyperedges are added in a localized manner using a denoising diffusion process, which allows for the construction of the global structure before refining local details. Our experiments demonstrated the effectiveness of HYGENE, proving its ability to closely mimic a variety of properties in hypergraphs. To the best of our knowledge, this is the first attempt to employ diffusion models for hypergraph generation.

We propose HYBOOD, a hybrid out-of-distribution model based on normalizing flow followed by a simple linear classification model. In real-world settings, it is known that data corruption has a strong influence on model degradation; for example image quality like noise, blur and image geometry like translation, scaling, rotation. MNIST-C, CIFAR10-C are the general synthesized datasets to measure model performance and corruption difficulty in terms of covariate and semantic shifts. HYBOOD shows that the separability between in-distribution, covariate shift, and semantic shift can be represented by distribution distance and log-scale density. We also find out the attributes of covariate shifts are ordered by corruption difficulty ranking (CDR) for the datasets. To the best of our knowledge, this is the first method to measure data corruption difficulty with generative models using Wasserstein Distance, Mutual Information and Minimal Description Length. In this paper, we pose interesting experimental results that the MNIST-C trained generative model is most deteriorated by fog, impulse noise and stripe corruption types. This can be interpreted that those attributes are challenging corruptions to the generative model in uncertainty and complexity. By training in-distribution data only, HYBOOD achieves out-of-distribution detection performance for distinguishable covariate and semantic shifts, and quantifying covariate shift ranking.

Grasping the intricacies of human motion, which involve perceiving spatio-temporal dependence and multi-scale effects, is essential for predicting human motion. While humans inherently possess the requisite skills to navigate this issue, it proves to be markedly more challenging for machines to emulate. To bridge the gap, we propose the Human-like Vision and Inference System (HVIS) for human motion prediction, which is designed to emulate human observation and forecast future movements. HVIS comprises two components: the human-like vision encode (HVE) module and the human-like motion inference (HMI) module. The HVE module mimics and refines the human visual process, incorporating a retina-analog component that captures spatiotemporal information separately to avoid unnecessary crosstalk. Additionally, a visual cortex-analogy component is designed to hierarchically extract and treat complex motion features, focusing on both global and local features of human poses. The HMI is employed to simulate the multi-stage learning model of the human brain. The spontaneous learning network simulates the neuronal fracture generation process for the adversarial generation of future motions. Subsequently, the deliberate learning network is optimized for hard-to-train joints to prevent misleading learning. Experimental results demonstrate that our method achieves new state-of-the-art performance, significantly outperforming existing methods by 19.8 % on Human3.6M, 15.7 % on CMU Mocap, and 11.1 % on G3D.

Capturing images under different color temperatures can result in color casts, causing the color presented in photos to differ from what is perceived by the human eye. Correcting these color temperature shifts to achieve White Balance (WB) is a challenging task, requiring the identification of variations in color tones from diverse light sources and the removal of color casts. The advent of deep neural networks has significantly advanced the progress of WB methods, evolving from simply identifying the scene illumination color to directly producing a color-corrected image from the color-shifted input. To better map color distributions and scene information from the input to the WB image, we propose HVDualformer, an end-to-end histogram-vision dual transformer architecture that can rectify color temperature features from WB color histograms and exploit them to adjust image features to yield accurate WB results. Extensive experimental results on public benchmark datasets demonstrate that the proposed model performs favorably against state-of-the-art methods.

Adaptive optimizers such as Adam and RMSProp have gained attraction in complex neural networks, including generative adversarial networks (GANs) and Transformers, thanks to their stable performance and fast convergence compared to non-adaptive optimizers. A frequently overlooked limitation of adaptive optimizers is that adjusting the learning rate of each dimension individually would ignore the knowledge of the whole loss landscape, resulting in slow updates of parameters, invalidating the learning rate adjustment strategy and eventually leading to widespread insufficient convergence of parameters. In this paper, we propose HVAdam, a novel optimizer that associates all dimensions of the parameters to find a new parameter update direction, leading to a refined parameter update strategy for an increased convergence rate. We validated HVAdam in extensive experiments, showing its faster convergence, higher accuracy, and more stable performance on image classification, image generation, and natural language processing tasks. Particularly, HVAdam achieves a significant improvement on GANs compared with other state-of-the-art methods, especially in Wasserstein-GAN (WGAN) and its improved version with gradient penalty (WGAN-GP).

Deep hashing models have achieved great success in retrieval tasks due to their powerful representation and strong information compression capabilities. However, they inherit the vulnerability of deep neural networks to adversarial perturbations. Attackers can severely impact the retrieval capability of hashing models by adding subtle, carefully crafted adversarial perturbations to benign images, transforming them into adversarial images. Most existing adversarial attacks target image classification models, with few focusing on retrieval models. We propose HUANG, the first targeted adversarial attack algorithm to leverage a diffusion model for hashing retrieval in black-box scenarios. In our approach, adversarial denoising uses adversarial perturbations and residual image to guide the shift from benign to adversarial distribution. Extensive experiments demonstrate the superiority of HUANG across different datasets, achieving state-of-the-art performance in black-box targeted attacks. Additionally, the dynamic interplay between denoising and adding adversarial perturbations in adversarial denoising endows HUANG with exceptional robustness and transferability.

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. Experimental results show that HSRDiff is superior to the comparison method with a considerable performance gap.

Salient Object Detection (SOD) is crucial in computer vision, yet RGB-based methods face limitations in challenging scenes, such as small objects and similar color features. Hyperspectral images provide a promising solution for more accurate Hyperspectral Salient Object Detection (HSOD) by abundant spectral information, while HSOD methods are hindered by the lack of extensive and available datasets. In this context, we introduce HSOD-BIT-V2, the largest and most challenging HSOD benchmark dataset to date. Five distinct challenges focusing on small objects and foreground-background similarity are designed to emphasize spectral advantages and real-world complexity. To tackle these challenges, we propose Hyper-HRNet, a high-resolution HSOD network. Hyper-HRNet effectively extracts, integrates, and preserves effective spectral information while reducing dimensionality by capturing the self-similar spectral features. Additionally, it conveys fine details and precisely locates object contours by incorporating comprehensive global information and detailed object saliency representations. Experimental analysis demonstrates that Hyper-HRNet outperforms existing models, especially in challenging scenarios.

Automatic Heuristic Design (AHD) is an active research area due to its utility in solving complex search and NP-hard combinatorial optimization problems in the real world. The recent advancements in Large Language Models (LLMs) introduce new possibilities by coupling LLMs with evolutionary computation to automatically generate heuristics, known as LLM-based Evolutionary Program Search (LLM-EPS). While previous LLM-EPS studies obtained great performance on various tasks, there is still a gap in understanding the properties of heuristic search spaces and achieving a balance between exploration and exploitation, which is a critical factor in large heuristic search spaces. In this study, we address this gap by proposing two diversity measurement metrics and perform an analysis on previous LLM-EPS approaches, including FunSearch, EoH, and ReEvo. Results on black-box AHD problems reveal that while EoH demonstrates higher diversity than FunSearch and ReEvo, its objective score is unstable. Conversely, ReEvo's reflection mechanism yields good objective scores but fails to optimize diversity effectively. With this finding in mind, we introduce HSEvo, an adaptive LLM-EPS framework that maintains a balance between diversity and convergence with a harmony search algorithm. Through experimentation, we find that HSEvo achieved high diversity indices and good objective scores while remaining cost-effective. These results underscore the importance of balancing exploration and exploitation and understanding heuristic search spaces in designing frameworks in LLM-EPS.

The introduction of Feature Pyramid Network (FPN) has significantly improved object detection performance. However, substantial challenges remain in detecting tiny objects, as their features occupy only a very small proportion of the feature maps. Although FPN integrates multi-scale features, it does not directly enhance or enrich the features of tiny objects. Furthermore, FPN lacks spatial perception ability. To address these issues, we propose a novel High Frequency and Spatial Perception Feature Pyramid Network (HS-FPN) with two innovative modules. First, we designed a high frequency perception module (HFP) that generates high frequency responses through high pass filters. These high frequency responses are used as mask weights from both spatial and channel perspectives to enrich and highlight the features of tiny objects in the original feature maps. Second, we developed a spatial dependency perception module (SDP) to capture the spatial dependencies that FPN lacks. Our experiments demonstrate that detectors based on HS-FPN exhibit competitive advantages over state-of-the-art models on the AI-TOD dataset for tiny object detection.

Human-object interaction (HOI) detection aims to detect the spatial positions of human-object pairs and recognize their interactions. Existing single-branch, two-branch, and three-branch methods are challenging to make an appropriate trade-off on efficiency, multi-task decoupling, and collaborative learning, while they fail to identify rare and complex interaction categories effectively as well. In this work, we propose a novel Efficient Mamba-based Disentangled Progressive Learning (HOIMamba) for HOI Detection to absorb the advantages of the existing three approaches and adaptively aggregate multi-level interaction semantics guided by cross-task bidirectional information contexts. Specifically, HOIMamba builds an efficient and effective decoder through cascaded Low-Rank Adaptations (LoRAs), with high efficiency, thorough decoupling of tasks, and good multi-task collaborative learning. Furthermore, to alleviate the recognition problem of interactions in difficult HOI samples, a novel Mamba-based comprehensive progressive learning strategy with Cross-enhance Mamba (CEM) blocks and Detection Context Propagation (DCP) blocks is designed to gradually excavate interaction-related discriminative cues from four levels. CEM blocks automatically aggregate context to generate diverse task-shared semantics and simultaneously realize the cross-task interaction between human and object branches, guiding the interaction branch to extract more expressive HOI representation. DCP blocks further transfer the comprehensive interaction context to human and object branches to achieve rich and effective information exchange, facilitating the model to discover more HOI instances. Extensive experimental results on two standard benchmarks demonstrate the effectiveness of our HOIMamba.

Understanding of bimanual hand-object interaction plays an important role in robotics and virtual reality. However, due to significant occlusions between hands and object as well as the high degree-of-freedom motions, it is challenging to collect and annotate a high-quality, large-scale dataset, which prevents further improvement of bimanual hand-object interaction-related baselines. In this work, we propose a new 3D Gaussian Splatting based data augmentation framework for bimanual hand-object interaction, which is capable of augmenting existing dataset to large-scale photorealistic data with various hand-object pose and viewpoints. First, we use mesh-based 3DGS to model objects and hands, and to deal with the rendering blur problem due to multi-resolution input images used, we design a super-resolution module. Second, we extend the single hand grasping pose optimization module for the bimanual hand object to generate various poses of bimanual hand-object interaction, which can significantly expand the pose distribution of the dataset. Third, we conduct an analysis for the impact of different aspects of the proposed data augmentation on the understanding of the bimanual hand-object interaction. We perform our data augmentation on two benchmarks, H2O and Arctic, and verify that our method can improve the performance of the baselines.

Entity alignment (EA) is crucial for integrating knowledge graphs (KGs) constructed from diverse sources. Conventional unsupervised EA approaches attempt to eliminate human intervention but often suffer from accuracy limitations. With the rise of large language models (LLMs), leveraging their capabilities for EA presents a promising direction. However, it introduces new challenges: formulating the LLM-based EA problem and extracting the background knowledge in LLMs to realize EA without human intervention. This paper proposes HLMEA, a novel hybrid language model-based unsupervised EA method. HLMEA formulates the EA task into a filtering and single-choice problem and synergistically integrates small language models (SLMs) and LLMs. Specifically, SLMs filter candidate entities based on textual representations generated from KG triples. Then, LLMs refine this selection to identify the most semantically aligned entities. An iterative self-training mechanism allows SLMs to distill knowledge from LLM outputs, enhancing the EA ability of hybrid language models in subsequent rounds cooperatively. We also conducted extensive experiments on benchmark datasets to evaluate HLMEA's performance. The results demonstrate that HLMEA significantly outperforms unsupervised and even supervised EA baselines, proving its potential for scalable and effective EA across large KGs. The code and data are available at \url{https://github.com/xnjin-ai/HLMEA}.

We focus on the medication recommendation problem aiming to recommend accurate medications for a patient’s current visit. Most existing methods for this problem utilize the patient’s current health status, medications prescribed at her past visits, and an Electronic Health Records (EHR) graph which represents whether medications have been co-prescribed. However, we point out their two limitations: (1) they have difficulty in utilizing only the medications which have been prescribed in health status similar to the patient’s current health status, regardless of whether they are prescribed at her past visits or at other patients’ visits; (2) for two medications that have ever been co-prescribed, their EHR graph does not consider the degree to which one medication is prescribed together when the other is prescribed. To address these two limitations, we propose a novel medication recommendation framework, named HI-DR (pronounced as ‘Hi Doctor’), composed of following two core ideas: (Idea 1) Health status-aware attentIon; (Idea 2) an electronic health recorDs gRaph+. Extensive experiments on real-world datasets demonstrate the significant superiority of HI-DR (up to 18.69% higher accuracy than the best competitor) and the effectiveness of two core ideas in HI-DR.

Infectious diseases have historically had profound effects on global health, economies, and social structures. Effective tracing of infectious diseases is essential not only for immediate public health responses but also for shaping future prevention strategies. Traditional tracing methods often emphasize homogeneous networks, overlooking the diverse transmission characteristics of heterogeneous populations. This research addresses two critical challenges: the heterogeneity of transmission across various media and modes, and the significant yet underexplored influence of community structures on epidemic spread and tracing.We propose a Heterogeneous Hypergraph Attention Network (HHAN) modelthat accounts for multiple transmission pathways and patterns within heterogeneous networks. HHAN integrates a heterogeneous graph neural network module to handle the complexity of communication among different populations, and an Agent-Based Modeling Module that combines agent-based ideas to model individual behaviors. This approach effectively captures complex interactions within community structures and addresses individual variability. Experimental results on three real-world datasets demonstrate that the HHAN model significantly outperforms other state-of-the-art methods in tackling the complex challenge of tracing infectious diseases in heterogeneous populations.

Millimeter-wave radar plays a vital role in 3D object detection for autonomous driving due to its all-weather and all-lighting-condition capabilities for perception. However, radar point clouds suffer from pronounced sparsity and unavoidable angle estimation errors. To address these limitations, incorporating a camera may partially help mitigate the shortcomings. Nevertheless, the direct fusion of radar and camera data can lead to negative or even opposite effects due to the lack of depth information in images and low-quality image features under adverse lighting conditions. Hence, in this paper, we present the radar-camera fusion network with Hybrid Generation and Synchronization (HGSFusion), designed to better fuse radar potentials and image features for 3D object detection. Specifically, we propose the Radar Hybrid Generation Module (RHGM), which fully considers the Direction-Of-Arrival (DOA) estimation errors in radar signal processing. This module generates denser radar points through different Probability Density Functions (PDFs) with the assistance of semantic information. Meanwhile, we introduce the Dual Sync Module (DSM), comprising spatial sync and modality sync, to enhance image features with radar positional information and facilitate the fusion of distinct characteristics in different modalities. Extensive experiments demonstrate the effectiveness of our approach, outperforming the state-of-the-art methods in the VoD and TJ4DRadSet datasets by 6.53% and 2.03% in RoI AP and BEV AP, respectively.