Association for the Advancement of Artificial Intelligence: AAAI Publications
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Feature Denoising Diffusion Model for Blind Image Quality Assessment
No full textBlind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Currently, deep learning BIQA methods typically depend on using features from high-level tasks for transfer learning. However, the inherent differences between BIQA and these high-level tasks inevitably introduce noise into the quality-aware features. In this paper, we take an initial step toward exploring the diffusion model for feature denoising in BIQA, namely Perceptual Feature Diffusion for IQA (PFD-IQA), which aims to remove noise from quality-aware features. Specifically, 1) we propose a Perceptual Prior Discovery and Aggregation module to establish two auxiliary tasks to discover potential low-level features in images that are used to aggregate perceptual textual prompt conditions for the diffusion model. 2) we propose a Perceptual Conditional Feature Refinement strategy, which matches noisy features to predefined denoising trajectories and then performs exact feature denoising based on textual prompt conditions. By incorporating a lightweight denoiser and requiring only a few feature denoising steps (e.g., just five iterations), our PFD-IQA framework achieves superior performance across eight standard BIQA datasets, validating its effectiveness
OAMaskFlow: Occlusion-Aware Motion Mask for Scene Flow
No full textThe scene flow estimation methods make significant progress by estimating pixel-wise 3D motion on implicitly learning a motion embedding using an end-to-end differentiable optimization framework. However, the motion embedding learned implicitly is insufficient for grouping pixels into rigid object in challenging regions, such as occlusion and inconsistent multi-view geometric properties. To address this issue, we propose a novel method for estimating scene flow called OAMaskFlow, which has three novelties. Firstly, we propose the concept of occlusion-aware motion (OAM) mask and generate the ground truth annotation through the photo-metric and geometry consistency. Secondly, we propose to supervise the motion embedding with the OAM mask to learn informative and reliable motion representation of the scene. Finally, a 3D motion propagation module is proposed to propagate high-quality 3D motion from reliable pixels to the challenging occluded regions. Experiments show that our proposed OAMaskFlow has reduced the EPE3D metric by 21.0% on the FlyingThings3D dataset and decreased SF-all metric by 24.3% on the KITTI scene flow benchmark than the baseline method RAFT-3D. Furthermore, we apply our proposed OAM mask in simultaneous localization and mapping (SLAM) to improve a state-of-the-art method DROID-SLAM. In comparison, the ATE metric has decreased by 65.7% and 58.3% on the TartanAir monocular and stereo datasets respectively
HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based Data Augmentation
No full textUnderstanding 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
GHOST: Gaussian Hypothesis Open-Set Technique
No full textEvaluations of large-scale recognition methods typically focus on overall performance. While this approach is common, it often fails to provide insights into performance across individual classes, which can lead to fairness issues and misrepresentation.
Addressing these gaps is crucial for accurately assessing how well methods handle novel or unseen classes and ensuring a fair evaluation. To address fairness in Open-Set Recognition (OSR), we demonstrate that per-class performance can vary dramatically. We introduce Gaussian Hypothesis Open Set Technique (GHOST), a novel hyperparameter-free algorithm that models deep features using class-wise multivariate Gaussian distributions with diagonal covariance matrices. We apply Z-score normalization to logits to mitigate the impact of feature magnitudes that deviate from the model’s expectations, thereby reducing the likelihood of the network assigning a high score to an unknown sample. We evaluate GHOST across multiple ImageNet-1K pre-trained deep networks and test it with four different unknown datasets. Using standard metrics such as AUOSCR, AUROC and FPR95, we achieve statistically significant improvements, advancing the state-of-the-art in large-scale OSR. Source code is provided online
Topology-Aware 3D Gaussian Splatting: Leveraging Persistent Homology for Optimized Structural Integrity
No full textGaussian Splatting (GS) has emerged as a crucial technique for representing discrete volumetric radiance fields. It leverages unique parametrization to mitigate computational demands in scene optimization. This work introduces Topology-Aware 3D Gaussian Splatting (Topology-GS), which addresses two key limitations in current approaches: compromised pixel-level structural integrity due to incomplete initial geometric coverage, and inadequate feature-level integrity from insufficient topological constraints during optimization. To overcome these limitations, Topology-GS incorporates a novel interpolation strategy, Local Persistent Voronoi Interpolation (LPVI), and a topology-focused regularization term based on persistent barcodes, named PersLoss. LPVI utilizes persistent homology to guide adaptive interpolation, enhancing point coverage in low-curvature areas while preserving topological structure. PersLoss aligns the visual perceptual similarity of rendered images with ground truth by constraining distances between their topological features. Comprehensive experiments on three novel-view synthesis benchmarks demonstrate that Topology-GS outperforms existing methods in terms of PSNR, SSIM, and LPIPS metrics, while maintaining efficient memory usage. This study pioneers the integration of topology with 3D-GS, laying the groundwork for future research in this area
Fine-Grained Perception in Panoramic Scenes: A Novel Task, Dataset, and Method for Object Importance Ranking
No full textExisting Salient Object Ranking (SOR) aims to infer ranking of salient objects based on their saliency degree. However, it tends to only focus on salient objects while neglecting non-salient ones. This coarse-grained ranking limits the performance of downstream tasks. For instance, in image retrieval tasks, focusing solely on the relationship between salient objects is insufficient for achieving fine-grained scene analysis, which may result in retrieved results that do not satisfy user requirements. High-quality retrieval requires fine-grained analysis, making it essential to rank non-salient objects. Based on this need, we propose a new task: Fine-grained Object Importance Ranking in 360 Scenes (FOIR-360), which focus on predicting the relative importance of "ALL objects'' at the instance-level. Our task takes into account all objects, allowing us to refine the original "coarse-grained'' to a "fine-grained'' level. Currently, the main challenge for this new task is the lack of supervised data for model training or even for model testing. Therefore, we propose a novel weakly supervised method to address the shortage of datasets. Furthermore, to the best of our knowledge, there is no existing suitable annotation protocol for this new task. The main reason is that annotating fine-grained rankings is extremely difficult, especially in panoramic scenes that contain numerous instances where even humans are unable to determine which one is more important than others. As the first attempt, we introduce a new annotation protocol designed to highlight the ranking of objects that are non-salient yet still important. Based on this protocol, we construct the first fine-grained 360Rank dataset. In summary, all these new task, weakly supervised method, annotation protocol, and dataset have the potential to drive advancements in the field
CtrlAvatar: Controllable Avatars Generation via Disentangled Invertible Networks
No full textAs virtual experiences grow in popularity, the demand for realistic, personalized, and animatable human avatars increases. Traditional methods, relying on fixed templates, often produce costly avatars that lack expressiveness and realism. To overcome these challenges, we introduce Controllable Avatars generation via disentangled invertible
networks (CtrlAvatar), a real-time framework for generating lifelike and customizable avatars.
CtrlAvatar uses disentangled invertible networks to separate the deformation process into implicit body geometry and explicit texture components. This approach eliminates the need for repeated occupancy reconstruction, enabling detailed and coherent animations. The body geometry component ensures anatomical accuracy, while the texture component allows for complex, artifact-free clothing customization. This architecture ensures smooth integration between body movements and surface details.
By optimizing transformations with position-varying offsets from the avatar’s initial Linear Blend Skinning vertices, CtrlAvatar achieves flexible, natural deformations that adapt to various scenarios. Extensive experiments show that CtrlAvatar outperforms other methods in quality, diversity, controllability, and cost-efficiency, marking a significant advancement in avatar generation
LiD-FL: Towards List-Decodable Federated Learning
No full textFederated learning is often used in environments with many unverified participants. Therefore, federated learning under adversarial attacks receives significant attention. This paper proposes an algorithmic framework for list-decodable federated learning, where a central server maintains a list of models, with at least one guaranteed to perform well. The framework has no strict restriction on the fraction of honest clients, extending the applicability of Byzantine federated learning to the scenario with more than half adversaries. Assuming the variance of gradient noise in stochastic gradient descent is bounded, we prove a convergence theorem of our method for strongly convex and smooth losses. Experimental results, including image classification tasks with both convex and non-convex losses, demonstrate that the proposed algorithm can withstand the malicious majority under various attacks
Federated Graph-Level Clustering Network
No full textFederated graph learning (FGL), which excels in analyzing non-IID graphs as well as protecting data privacy, has recently emerged as a hot topic. Existing FGL methods usually train the client model using labeled data and then collaboratively learn a global model without sharing their local graph data. However, in real-world scenarios, the lack of data annotations impedes the negotiation of multi-source information at the server, leading to sub-optimal feedback to the clients. To address this issue, we propose a novel unsupervised learning framework called Federated Graph-level Clustering Network (FedGCN), which collects the topology-oriented features of non-IID graphs from clients to generate global consensus representations through multi-source clustering structure sharing. Specifically, in the client, we first preserve the prototype features of each cluster from the structure-oriented embedding through clustering and then upload the learned multiple prototypes that are hard to be reconstructed into the raw graph data. In the server, we generate consensus prototypes from multiple condensed structure-oriented signals through Gaussian estimation, which are subsequently transferred to each client to promote the great encoding capacity of the local model for better clustering. Extensive experiments across multiple non-IID graph datasets have demonstrated the effectiveness and superiority of FedGCN against its competitors
Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning
No full textFew-shot learning (FSL) aims to recognize new concepts using a limited number of visual samples. Existing methods attempt to incorporate semantic information into the limited visual data for category understanding. However, these methods often enrich class-level feature representations with abstract category names, failing to capture nuanced features essential for effective generalization. To address this issue, we propose a novel framework for FSL, which incorporates both the abstract class semantics and the concrete class entities extracted from Large Language Models (LLMs), to enhance the representation of the class prototypes. Specifically, our framework composes a Semantic-guided Visual Pattern Extraction (SVPE) module and a Prototype-Calibration (PC) module, where the SVPE meticulously extracts semantic-aware visual patterns across diverse scales, while the PC module seamlessly integrates these patterns to refine the visual prototype, enhancing its representativeness. Extensive experiments on four few-shot classification benchmarks and the BSCD-FSL cross-domain benchmark showcase remarkable advancements over the current state-of-the-art methods. Notably, for the challenging one-shot setting, our approach, utilizing the ResNet-12 backbone, achieves an impressive average improvement of 1.95% over the second-best competitor