Longin Jan Latecki

IROS Conference 2025 Conference Paper

VAPO: Visibility-Aware Keypoint Localization for Efficient 6DoF Object Pose Estimation

• Ruyi Lian
• Yuewei Lin
• Longin Jan Latecki
• Haibin Ling

Localizing predefined 3D keypoints in a 2D image is an effective way to establish 3D-2D correspondences for instance-level 6DoF object pose estimation. However, unreliable localization results of invisible keypoints degrade the quality of correspondences. In this paper, we address this issue by localizing the important keypoints in terms of visibility. Since keypoint visibility information is currently missing in the dataset collection process, we propose an efficient way to generate binary visibility labels from available object-level annotations, for keypoints of both asymmetric objects and symmetric objects. We further derive real-valued visibility-aware importance from binary labels based on the PageRank algorithm. Taking advantage of the flexibility of our visibility-aware importance, we construct VAPO (Visibility-Aware POse estimator) by integrating the visibility-aware importance with a state-of-the-art pose estimation algorithm, along with additional positional encoding. VAPO can work in both CAD-based and CAD-free settings. Extensive experiments are conducted on popular pose estimation benchmarks including Linemod, Linemod-Occlusion, and YCB-V, demonstrating that VAPO clearly achieves state-of-the-art performances. Project page: https://github.com/RuyiLian/VAPO.

ECAI Conference 2024 Conference Paper

FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding

• Huitong Pan
• Qi Zhang
• Cornelia Caragea
• Eduard C. Dragut
• Longin Jan Latecki

Flowcharts are graphical tools for representing complex concepts in concise visual representations. This paper introduces the FlowLearn dataset, a resource tailored to enhance the understanding of flowcharts. FlowLearn contains complex scientific flowcharts and simulated flowcharts. The scientific subset contains 3, 858 flowcharts sourced from scientific literature and the simulated subset contains 10, 000 flowcharts created using a customizable script. The dataset is enriched with annotations for visual components, OCR, Mermaid code representation, and VQA question-answer pairs. Despite the proven capabilities of Large Vision-Language Models (LVLMs) in various visual understanding tasks, their effectiveness in decoding flowcharts—a crucial element of scientific communication—has yet to be thoroughly investigated. The FlowLearn test set is crafted to assess the performance of LVLMs in flowchart comprehension. Our study thoroughly evaluates state-of-the-art LVLMs, identifying existing limitations and establishing a foundation for future enhancements in this relatively underexplored domain. For instance, in tasks involving simulated flowcharts, GPT-4V achieved the highest accuracy (58%) in counting the number of nodes, while Claude recorded the highest accuracy (83%) in OCR tasks. Notably, no single model excels in all tasks within the FlowLearn framework, highlighting significant opportunities for further development.

IJCAI Conference 2024 Conference Paper

Image Retrieval with Self-Supervised Divergence Minimization and Cross-Attention Classification

• Vivek Trivedy
• Longin Jan Latecki

Common approaches to image retrieval include contrastive methods and specialized loss functions such as ranking losses and entropy regularizers. We present DMCAC (Divergence Minimization with Cross-Attention Classification), a novel image retrieval method that offers a new perspective on this training paradigm. We use self-supervision with a novel divergence loss framework alongside a simple data flow adjustment that minimizes a distribution over a database directly during training. We show that jointly learning a query representation over a database is a competitive and often improved alternative to traditional contrastive methods for image retrieval. We evaluate our method across several model configurations and four datasets, achieving state-of-the-art performance in multiple settings. We also conduct a thorough set of ablations that show the robustness of our method across full vs. approximate retrieval and different hyperparameter configurations.

IJCAI Conference 2017 Conference Paper

Affinity Learning for Mixed Data Clustering

• Nan Li
• Longin Jan Latecki

In this paper, we propose a novel affinity learning based framework for mixed data clustering, which includes: how to process data with mixed-type attributes, how to learn affinities between data points, and how to exploit the learned affinities for clustering. In the proposed framework, each original data attribute is represented with several abstract objects defined according to the specific data type and values. Each attribute value is transformed into the initial affinities between the data point and the abstract objects of attribute. We refine these affinities and infer the unknown affinities between data points by taking into account the interconnections among the attribute values of all data points. The inferred affinities between data points can be exploited for clustering. Alternatively, the refined affinities between data points and the abstract objects of attributes can be transformed into new data features for clustering. Experimental results on many real world data sets demonstrate that the proposed framework is effective for mixed data clustering.

AAAI Conference 2017 Conference Paper

Multidimensional Scaling on Multiple Input Distance Matrices

• Song Bai
• Xiang Bai
• Longin Jan Latecki
• Qi Tian

Multidimensional Scaling (MDS) is a classic technique that seeks vectorial representations for data points, given the pairwise distances between them. In recent years, data are usually collected from diverse sources or have multiple heterogeneous representations. However, how to do multidimensional scaling on multiple input distance matrices is still unsolved to our best knowledge. In this paper, we first define this new task formally. Then, we propose a new algorithm called Multi-View Multidimensional Scaling (MVMDS) by considering each input distance matrix as one view. The proposed algorithm can learn the weights of views (i. e. , distance matrices) automatically by exploring the consensus information and complementary nature of views. Experimental results on synthetic as well as real datasets demonstrate the effectiveness of MVMDS. We hope that our work encourages a wider consideration in many domains where MDS is needed.

AAAI Conference 2017 Conference Paper

Regularized Diffusion Process for Visual Retrieval

• Song Bai
• Xiang Bai
• Qi Tian
• Longin Jan Latecki

Diffusion process has advanced visual retrieval greatly owing to its capacity in capturing the geometry structure of the underlying manifold. Recent studies (Donoser and Bischof 2013) have experimentally demonstrated that diffusion process on the tensor product graph yields better retrieval performances than that on the original affinity graph. However, the principle behind this kind of diffusion process remains unclear, i. e. , what kind of manifold structure is captured and how it is reflected. In this paper, we propose a new variant of diffusion process, which also operates on a tensor product graph. It is defined in three equivalent formulations (regularization framework, iterative framework and limit framework, respectively). Based on our study, three insightful conclusions are drawn which theoretically explain how this kind of diffusion process can better reveal the intrinsic relationship between objects. Besides, extensive experimental results on various retrieval tasks testify the validity of the proposed method.

IJCAI Conference 2017 Conference Paper

Restart and Random Walk in Local Search for Maximum Vertex Weight Cliques with Evaluations in Clustering Aggregation

• Yi Fan
• Nan Li
• Chengqian Li
• Zongjie Ma
• Longin Jan Latecki
• Kaile Su

The Maximum Vertex Weight Clique (MVWC) problem is NP-hard and also important in real-world applications. In this paper we propose to use the restart and the random walk strategies to improve local search for MVWC. If a solution is revisited in some particular situation, the search will restart. In addition, when the local search has no other options except dropping vertices, it will use random walk. Experimental results show that our solver outperforms state-of-the-art solvers in DIMACS and finds a new best-known solution. Also it is the unique solver which is comparable with state-of-the-art methods on both BHOSLIB and large crafted graphs. Furthermore we evaluated our solver in clustering aggregation. Experimental results on a number of real data sets demonstrate that our solver outperforms the state-of-the-art for solving the derived MVWC problem and helps improve the final clustering results.

ECAI Conference 2016 Conference Paper

Semi-Supervised Learning on an Augmented Graph with Class Labels

• Nan Li 0021
• Longin Jan Latecki

In this paper, we propose a novel graph-based method for semi-supervised learning. Our method runs a diffusion-based affinity learning algorithm on an augmented graph consisting of not only the nodes of labeled and unlabeled data but also artificial nodes representing class labels. The learned affinities between unlabeled data and class labels are used for classification. Our method achieves superior results on many standard data sets.

AAAI Conference 2014 Conference Paper

Locality Preserving Projection for Domain Adaptation with Multi-Objective Learning

• Le Shu
• Tianyang Ma
• Longin Jan Latecki

In many practical cases, we need to generalize a model trained in a source domain to a new target domain. However, the distribution of these two domains may differ very significantly, especially sometimes some crucial target features may not have support in the source domain. This paper proposes a novel locality preserving projection method for domain adaptation task, which can find a linear mapping preserving the ’intrinsic structure’ for both source and target domains. We first construct two graphs encoding the neighborhood information for source and target domains separately. We then find linear projection coefficients which have the property of locality preserving for each graph. Instead of combing the two objective terms under compatibility assumption and requiring the user to decide the importance of each objective function, we propose a multiobjective formulation for this problem and solve it simultaneously using Pareto optimization. The Pareto frontier captures all possible good linear projection coefficients that are preferred by one or more objectives. The effectiveness of our approach is justified by both theoretical analysis and empirical results on real world data sets. The new feature representation shows better prediction accuracy as our experiments demonstrate.

AAAI Conference 2011 Conference Paper

Size Adaptive Selection of Most Informative Features

• Si Liu
• Hairong Liu
• Longin Jan Latecki
• Shuicheng Yan
• Changsheng Xu
• Hanqing Lu

In this paper, we propose a novel method to select the most informative subset of features, which has little redundancy and very strong discriminating power. Our proposed approach automatically determines the optimal number of features and selects the best subset accordingly by maximizing the average pairwise informativeness, thus has obvious advantage over traditional filter methods. By relaxing the essential combinatorial optimization problem into the standard quadratic programming problem, the most informative feature subset can be obtained efficiently, and a strategy to dynamically compute the redundancy between feature pairs further greatly accelerates our method through avoiding unnecessary computations of mutual information. As shown by the extensive experiments, the proposed method can successfully select the most informative subset of features, and the obtained classification results significantly outperform the state-of-the-art results on most test datasets.

ICRA Conference 2006 Conference Paper

Extended EM for Planar Approximation of 3D Data

• Rolf Lakaemper
• Longin Jan Latecki

ICRA Conference 2006 Conference Paper

Polygonal Approximation of Laser Range Data based on Perceptual Grouping and EM

• Longin Jan Latecki
• Rolf Lakaemper

Our goal is polygonal approximation of laser range data points obtained by a mobile robot. The proposed approach provides a precise estimation of the number of model components (line segments) and their initial parameters independent of their initial values. We use principles of perceptual grouping to evaluate the approximation quality obtained in each expectation maximization (EM) step. By evaluating EM approximation quality we are able to recognize a locally optimal solution, and modify the number of model components and their parameters. Consequently, EM can converge only to a globally optimal solution independent of the initial number of model components and their initial parameters

IROS Conference 2005 Conference Paper

Incremental multi-robot mapping

• Rolf Lakaemper
• Longin Jan Latecki
• Diedrich Wolter

The purpose of this paper is to present a technique to create a global map of robots' surroundings by converting the raw data acquired from a scanning sensor to a compact map composed of just a few generalized polylines (polygonal curves). We propose a new approach to merging robots' maps that is composed of a local geometric process of merging similar line segments (termed discrete segment evolution) with a global statistical control process. In the case of single robot, we are able to incrementally build a map showing the environment the robot has traveled through by merging its polygonal map with actual scans. In the case of a robot team, we are able to identify common parts of their partial maps and if common parts are present construct a joint map of the explored environment.

ECAI Conference 1992 Conference Paper

On Hybrid Reasoning for Processing Spatial Expressions

• Longin Jan Latecki
• Simone Pribbenow