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Mesh is a powerful data structure for 3D shapes. Representation learning for 3D meshes is important in many computer vision and graphics applications. The recent success of convolutional neural networks (CNNs) for structured data (e.g., images) suggests the value of adapting insight from CNN for 3D shapes. However, 3D shape data are irregular since each node&#39;s neighbors are unordered. Various graph neural networks for 3D shapes have been developed with isotropic filters or predefined local coordinate systems to overcome the node inconsistency on graphs. However, isotropic filters or predefined local coordinate systems limit the representation power. In this paper, we propose a local structure-aware anisotropic convolutional operation (LSA-Conv) that learns adaptive weighting matrices for each node according to the local neighboring structure and performs shared anisotropic filters. In fact, the learnable weighting matrix is similar to the attention matrix in random synthesizer -- a new Transformer model for natural language processing (NLP). Comprehensive experiments demonstrate that our model produces significant improvement in 3D shape reconstruction compared to state-of-the-art methods.

AAAI 2021

Learning Local Neighboring Structure for Robust 3D Shape Representation

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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Mesh is a powerful data structure for 3D shapes. Representation learning for 3D meshes is important in many computer vision and graphics applications. The recent success of convolutional neural networks (CNNs) for structured data (e.g., images) suggests the value of adapting insight from CNN for 3D shapes. However, 3D shape data are irregular since each node's neighbors are unordered. Various graph neural networks for 3D shapes have been developed with isotropic filters or predefined local coordinate systems to overcome the node inconsistency on graphs. However, isotropic filters or predefined local coordinate systems limit the representation power. In this paper, we propose a local structure-aware anisotropic convolutional operation (LSA-Conv) that learns adaptive weighting matrices for each node according to the local neighboring structure and performs shared anisotropic filters. In fact, the learnable weighting matrix is similar to the attention matrix in random synthesizer -- a new Transformer model for natural language processing (NLP). Comprehensive experiments demonstrate that our model produces significant improvement in 3D shape reconstruction compared to state-of-the-art methods.

technical paper

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. 

In this paper, we study the problem of how to aggregate pairwise personalized attributes (PA) annotations (e.g., Shoes A is more comfortable than B) from different annotators on the crowdsourcing platforms, which is an emerging topic gaining increasing attention in recent years. Given the crowdsourced annotations, the majority of the traditional literature assumes that all the pairs in the collected dataset are distinguishable. However, this assumption is incompatible with how humans perceive attributes since indistinguishable pairs are ubiquitous for the annotators due to the limitation of human perception. To attack this problem, we propose a novel deep prediction model that could simultaneously detect the indistinguishable pairs and aggregate ranking results for distinguishable pairs. First of all, we represent the pairwise annotations as a multi-graph. Based on such data structure, we propose an end-to-end partial ranking model which consists of a deep backbone architecture and a probabilistic model that captures the generative process of the partial rank annotations. Specifically, to recognize the indistinguishable pairs, the probabilistic model we proposed is equipped with an adaptive perception threshold, where indistinguishable pairs could be automatically detected when the absolute value of the score difference is below the learned threshold. In our empirical studies, we perform a series of experiments on three real-world datasets: LFW-10, Shoes, and Sun. The corresponding results consistently show the superiority of our proposed model.

Deep Partial Rank Aggregation for Personalized Attributes

This paper observes that there is an issue of high frequencies missing in the discriminator of standard GAN, and we reveal it stems from downsampling layers employed in the network architecture. This issue makes the generator lack the incentive from the discriminator to learn high-frequency content of data, resulting in a significant spectrum discrepancy between generated images and real images. Since the Fourier transform is a bijective mapping, we argue that reducing this spectrum discrepancy would boost the performance of GANs. To this end, we introduce SSD-GAN, an enhancement of GANs to alleviate the spectral information loss in the discriminator. Specifically, we propose to embed a frequency-aware classifier into the discriminator to measure the realness of the input in both the spatial and spectral domains. With the enhanced discriminator, the generator of SSD-GAN is encouraged to learn high-frequency content of real data and generate exact details. The proposed method is general and can be easily integrated into most existing GANs framework without excessive cost. The effectiveness of SSD-GAN is validated on various network architectures, objective functions, and datasets. Code is available at https://github.com/cyq373/SSD-GAN.

SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains

Interpretability has been regarded as an essential component for deploying deep neural networks, in which the saliency-based method is one of the most prevailing interpretable approaches since it can generate individually intuitive heatmaps that highlight parts of the input image that are most important to the decision of the deep networks on a particular classification target. However, heatmaps generated by existing methods either contain little information to represent objects (perturbation-based methods) or cannot effectively locate multi-class objects (activation-based approaches). To address this issue, a two-stage framework for visualizing the interpretability of deep neural networks, called Activation Optimized with Perturbation (AOP), is designed to optimize activation maps generated by general activation-based methods with the help of perturbation-based methods. Finally, in order to obtain better explanations for different types of images, we further present an instance of the AOP framework, Smooth Integrated Gradient-based Class Activation Map (SIGCAM), which proposes a weighted GradCAM by applying the feature map as weight coefficients and employs I-GOS to optimize the base-mask generated by weighted GradCAM. Experimental results on common-used benchmarks, including deletion and insertion tests on ImageNet-1k, and pointing game tests on COCO2017, show that the proposed AOP and SIGCAM outperform the current state-of-the-art methods significantly by generating higher quality image-based saliency maps.

A Novel Visual Interpretability for Deep Neural Networks by Optimizing Activation Maps with Perturbation

Scene graphs are a powerful structured representation of the underlying content of images, and embeddings derived from them have been shown to be useful in multiple downstream tasks. In this work, we employ a graph convolutional network to exploit structure in scene graphs and produce image embeddings useful for semantic image retrieval. Different from classification-centric supervision traditionally available for learning image representations, we address the task of learning from relative similarity labels in a ranking context. Rooted within the contrastive learning paradigm, we propose a novel loss function that operates on pairs of similar and dissimilar images and imposes relative ordering between them in embedding space. We demonstrate that this Ranking loss, coupled with an intuitive triple sampling strategy, leads to robust representations that outperform well-known contrastive losses on the retrieval task. In addition, we provide qualitative evidence of how retrieved results that utilize structured scene information capture the global context of the scene, different from visual similarity search.

Scene Graph Embeddings Using Relative Similarity Supervision

Recent advancements in the field of deep learning have dramatically improved the performance of machine learning models in a variety of applications, including computer vision, text mining, speech processing and fraud detection among others. Mini-batch gradient descent is the standard algorithm to train deep models, where mini-batches of a fixed size are sampled randomly from the training data and passed through the network sequentially. In this paper, we present a novel algorithm to generate a deterministic sequence of mini-batches to train a deep neural network (rather than a random sequence). Our rationale is to select a mini-batch by minimizing the Maximum Mean Discrepancy (MMD) between the already selected mini-batches and the unselected training samples. We pose the mini-batch selection as a constrained optimization problem and derive a linear programming relaxation to determine the sequence of mini-batches. To the best of our knowledge, this is the first research effort that uses the MMD criterion to determine a sequence of mini-batches to train a deep neural network. The proposed mini-batch sequencing strategy is deterministic and independent of the underlying network architecture and prediction task. Our extensive empirical analyses on three challenging datasets corroborate the merit of our framework over competing baselines. We further study the performance of our framework on two other applications besides classification (regression and semantic segmentation) to validate its generalizability.

Deterministic Mini-Batch Sequencing for Training Deep Neural Networks

In a Green Pickup-and-Delivery problem (GPD), vehicles traveling in a transport network achieving pickup-and-delivery tasks are in particular subject to the two \textit{green} constraints: limited vehicle fuel capacity thus short vehicle traveling range, and limited availability of refueling infrastructure for the vehicles. GPD adds additional but probably insignificant computational complexity to the classic and already NP-hard Pickup-and-Delivery problem and Vehicle Routing Problem. Nevertheless, we demonstrate in this paper an inherent intractability of these green components themselves. More precisely, we show that GPD problems whose total constraints are reduced to almost the green ones only, remain to be NP-complete in the strong sense. We figure out a specifically constrained variant of GPD that, however, is weakly NP-complete -- a practical pseudo-polynomial time algorithm solving the variant problem is identified. Insight obtained from this complexity-theoretic analysis would shed light for a deeper understanding of GPDs, and on better development of heuristics for solving these problems, leading to promisingly many real-world applications.

A Complexity-Theoretic Analysis of Green Pickup-and-Delivery Problems

Temporal Action Segmentation (TAS) has achieved great success in many fields such as exercise rehabilitation, movie editing, etc. Currently, task-driven TAS is a central topic in human action analysis. However, motion-centered TAS, as an important topic, is little researched due to unavailable datasets. In order to explore more models and practical applications of motion-centered TAS, we introduce a Motion-Centered Figure Skating (MCFS) dataset in this paper. Compared with existing temporal action segmentation datasets, the MCFS dataset is fine-grained semantic, specialized and motion-centered. Besides, RGB-based and Skeleton-based features are provided in the MCFS dataset. Experimental results show that existing state-of-the-art methods are difficult to achieve excellent segmentation results (including accuracy, edit and F1 score) in the MCFS dataset. This indicates that MCFS is a challenging dataset for motion-centered TAS. The latest dataset can be downloaded at https://shenglanliu.github.io/mcfs-dataset/.

Temporal Segmentation of Fine-Gained Semantic Action: A Motion-Centered Figure Skating Dataset

We extend DatalogMTLâ€”Datalog with operators from metric temporal logicâ€”by adding stratified negation as failure. The new language provides additional expressive power for representing and reasoning about temporal data and knowledge in a wide range of applications. We consider models over the rational timeline, study their properties, and establish the computational complexity of reasoning. We show that, as in negation-free DatalogMTL, fact entailment in our language is PSPACE-complete in data and EXPSPACE-complete in combined complexity. Thus, the extension with stratified negation does not lead to higher complexity.

Stratified Negation in Datalog with Metric Temporal Operators

The creation of Bayesian networks often requires the specification of a large number of parameters, making it highly desirable to be able to learn these parameters from historical data. In many cases, such data has uncertainty associated with it, including cases in which this data comes from unstructured analysis or from sensors. When creating diagnosis networks, for example, unstructured analysis algorithms can be run on the historical text descriptions or images of previous cases so as to extract data for learning Bayesian network parameters, but such derived data has inherent uncertainty associated with it due to the nature of such algorithms. 
Because of the inability of current Bayesian network parameter learning algorithms to incorporate such uncertainty, common approaches either ignore this uncertainty, thus reducing the resulting accuracy, or completely disregard such data. We present an approach for learning Bayesian network parameters that explicitly incorporates such uncertainty, and which is a natural extension of the Bayesian network formalism.  We present a generalization of the Expectation Maximization parameter learning algorithm that enables it to handle any historical data with likelihood-evidence-based uncertainty, as well as an empirical validation demonstrating the improved accuracy and convergence enabled by our approach. We also prove that our extended algorithm maintains the convergence and correctness properties of the original EM algorithm, while explicitly incorporating data uncertainty in the learning process.

Learning the Parameters of Bayesian Networks from Uncertain Data

This paper explores a novel setting of temporal sentence grounding for the first time, dubbed as dense events grounding. Given an untrimmed video and a paragraph description, dense events grounding aims to jointly localize temporal moments of multiple events described in the paragraph. Our main motivating fact is that multiple events to be grounded in a video are often semantically related and temporally coordinated according to their order appearing in the paragraph. This fact sheds light on devising more accurate visual grounding model. In this work, we propose Dense Events Propagation Network (DepNet) for this novel task. DepNet first adaptively aggregates temporal and semantic information of dense events into a compact set through a second-order attention pooling, then selectively propagates the aggregated information to each single event with soft attention. Based on such aggregation-and-propagation mechanism, DepNet can effectively exploit both the temporal order and semantic relations of dense events. We conduct comprehensive experiments on large-scale datasets ActivityNet Captions and TACoS. For fair comparisons, our evaluations include both state-of-art single-event grounding methods and their natural extensions to the dense-events grounding setting implemented by us. All experiments clearly shows the performance superiority of the proposed DepNet by significant margins.

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