Automated pavement distress detection across large-scale roadway networks generates massive volumes of multimodal image data that must be efficiently indexed, searched, and analyzed within infrastructure monitoring and multimedia retrieval pipelines. Leveraging paired intensity–range imagery is attractive because intensity encodes appearance and texture, while range captures complementary three-dimensional geometry for robust 2D–3D distress understanding. However, modality utility is highly class-dependent, and naive early fusion can entangle modalities and suppress class-critical evidence, degrading detection and downstream retrieval performance. We study nine asphalt concrete pavement (ACP) distress types in the TxDOT 2D/3D pavement dataset, observe heterogeneous modality preferences across categories, and propose a decoupled multimodal YOLO variant with a full-backbone split design that keeps intensity and range feature streams separate in the backbone and fuses them only in the neck. Building on this principle, we further develop a class- and modality-aware online augmentation strategy: geometric transforms are synchronized across channels to preserve 2D/3D alignment, while pixel-level perturbations are applied per modality, with strengths selected based on each class’s modality dominance. On the ACP benchmark, the split backbone improves validation AP50 from 0.766 to 0.812, and the proposed augmentation further boosts it to 0.869. On the held-out test set, our system achieves 0.833 AP50 and 0.428 AP50–95, outperforming early fusion and uniform two-channel augmentation baselines under the same sensing configuration. Overall, our results indicate that late-fusion split learning with modality-aligned augmentation is a simple, data-centric design pattern for robust multimodal detection on large-scale intensity–range imagery.

In this paper, we present a novel method for efficient and effective retrieval of similar deep descriptors. Our new hybrid method for indexing and searching for the approximate nearest neighbors in high-dimensional large deep-descriptor databases retrieves truly similar items in the database, even if the retrieval set is large. The proposed solution —- hybrid approximate nearest neighbor indexing and search (HANNIS) —- partitions the whole data space using the kmeans++ algorithm and then indexes each cluster using adapted hierarchical navigable graphs. This approach enables us to load items that are truly close to the incoming query at retrieval time. HANNIS outperforms all state-of-the-art methods in terms of recall at depths of up to 100 and offers consistent index loading and retrieval performance.

In this paper, we evaluate the performance of a novel method for efficient and effective retrieval of similar high-dimensional image features. The proposed solution —- hybrid approximate nearest neighbor indexing and search (HANNIS) —-retrieves truly similar items in the database, even if the retrieval set is large. This approach enables us to load items that are truly close to the incoming query at retrieval time. HANNIS outperforms all state-of-the-art methods in terms of recall, precision, and F1 score at depths of up to 100 and offers the fastest index loading and consistent retrieval performance.

On-board video sensors on large ships capture video data at high rates and provide real-time object tracking for maritime applications such as piracy and illegal fishing. High volumes of collected video and imagery data require advanced technology to analyze the video feeds, reduce data smog, and alert the crew on ships or coastal guard when unusual activities are detected. We present an integrated end-to-end system that analyzes multi-camera ship video feed; localizes maritime vessels in the video feed; identifies the maritime vessel over multiple cameras; maps the vessel track onto an overhead plane; and identifies anomalous vessel movement around the ship. In this paper, we focus on a specific activity detection approach in maritime vessel overhead tracks and on synthetic data generation to realistically model maritime boat movements around onboard ship cameras using real-world examples. We propose and compare three novel modes of trajectory analysis and activity classification, using Computing with Words (CWW), a Markov trajectory feature classifier (MTFC), and Naïve Bayes Radial Classifier (NBRC) to detect the activity of vessel approaching the ship, vessel chasing another vessel, and vessel circling around the ship.

Abstract Attitudinal network graphs are signed graphs where edges capture an expressed opinion; two vertices connected by an edge can be agreeable (positive) or antagonistic (negative). A signed graph is called balanced if each of its cycles includes an even number of negative edges. Balance is often characterized by the frustration index or by finding a single convergent balanced state of network consensus. In this paper, we propose to expand the measures of consensus from a single balanced state associated with the frustration index to the set of nearest balanced states. We introduce thefrustration cloudas a set of all nearest balanced states and use a graph-balancing algorithm to find all nearest balanced states in a deterministic way. Computational concerns are addressed by measuring consensus probabilistically, and we introduce new vertex and edge metrics to quantifystatus,agreement, andinfluence. We also introduce a new global measure of controversy for a given signed graph and show that vertex status is a zero-sum game in the signed network. We propose an efficient scalable algorithm for calculating frustration cloud-based measures in social network and survey data of up to 80,000 vertices and half-a-million edges. We also demonstrate the power of the proposed approach to provide discriminant features for community discovery when compared to spectral clustering and to automatically identify dominant vertices and anomalous decisions in the network.

Typical approaches to the multi-label classification problem require learning an independent classifier for every label from all the examples and features. This can become a computational bottleneck for sizeable datasets with a large label space. In this paper, we propose an efficient and effective multi-label learning algorithm called model-shared subspace boosting (MSSBoost) as an attempt to reduce the information redundancy in the learning process. This algorithm automatically finds, shares and combines a number of base models across multiple labels, where each model is learned from random feature subspace and boots trap data samples. The decision functions for each label are jointly estimated and thus a small number of shared subspace models can support the entire label space. Our experimental results on both synthetic data and real multimedia collections have demonstrated that the proposed algorithm can achieve better classification performance than the non-ensemble baselineclassifiers with a significant speedup in the learning and prediction processes. It can also use a smaller number of base models to achieve the same classification performance as its non-model-shared counterpart.