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Minh Sao Khue Luu, Bair N. Tuchinov

We introduce a single–backbone foundation model for brain MRI that supports dynamic modality integration: it operates with arbitrary, possibly unseen, combinations of MRI sequences at pretrain and transfer. The encoder is conditioned by text-derived modality embeddings via conditional layer normalization, while a variance–covariance penalty discourages feature collapse. Unlike expert-based designs that grow with each new sequence, our approach scales without adding modality-specific branches. Pretrained self-supervised on ∼60,000 heterogeneous MRIs, the model learns modality-aware yet modality-agnostic features. We outline evaluation on segmentation and classification under missing/unseen modalities and cross-center shifts, and present early feasibility on multiple sclerosis lesion segmentation under limited data. This work moves toward robust, protocol-agnostic MRI foundation models suited to real clinical variability.

Jingcheng Li, Ye Qiao, Sitao Huang

Current video understanding models struggle with temporal reasoning and efficient processing while balancing detail preservation with computational efficiency. We propose a hierarchical memory system that segments videos into action and scene units, combined with question-aware agentic keyframe selection. Our method achieves 70.3% overall accuracy on VideoMME short video benchmarks.

Suin Kim, Jihoon Lee, Moonsung Kang, Doheun Cha, Sangtae Ahn

Recent advances in diffusion-based models have significantly broadened their scope, extending well beyond image generation to encompass zero-shot segmentation tasks. In this work, we introduce a novel, training-free approach that harnesses both self- and cross-attention maps to achieve highly detailed hair segmentation. Our method demonstrates remarkable efficacy in producing fine-grained results without the need for additional training.

Ashim Dhor, Smily Bharadwaj

Automated cancer segmentation in Whole Slide Images (WSIs) has been dominated by a paradigm of static pattern recognition, where even advanced methods leveraging Transformers, Multiple Instance Learning, or topology-aware losses remain fundamentally descriptive and correlational. To address this limitation, we reframe WSI segmentation from a descriptive task to one of causal process modeling. We introduce Topo-GraT, a novel framework featuring a Causal Growth Field (CGF) to model tumor invasion dynamics and a Causal Flow Attention (CFA) mechanism that embeds this field as an architectural prior. This causal engine is integrated within an iterative graph refinement loop that uses segmentation uncertainty to dynamically focus computational resources on the most ambiguous tissue regions. Our comprehensive experiments on multiple WSI datasets demonstrate that Topo-GraT establishes a new state-of-the-art, significantly outperforming existing methods and reducing the 95% Hausdorff Distance, a key boundary metric, by over 15%. Crucially, our framework yields the CGF as a rich, interpretable output whose structure correlates with tumor aggressiveness, positioning it as a novel biomarker for downstream prognostic tasks. By shifting the paradigm from static recognition to causal reasoning, Topo-GraT offers a more robust, efficient, and clinically insightful approach, setting a new direction for the causally-aware medical image analysis.

Depanshu Sani

Nature is inherently structured! The entities in the real world are naturally organized in rich relationships. For example, dolphins and sharks, despite their striking visual resemblance in body shape and fins, are actually from entirely different branches of the animal hierarchy, i.e., mammals and fishes, respectively. This remarkable similarity is a prime example of ‘convergent evolution’, where unrelated species develop similar features because they face similar environmental challenges. This illustrates how nature’s underlying organization often transcends superficial visual resemblances. Although humans intuitively grasp and utilize these profound natural constraints, they are typically underutilized in most AI systems. As a result, trained AI models tend to align with statistical patterns in the data, such as sampling biases or class imbalance, rather than adhering to the underlying relational consistency. This thesis argues that AI systems must evolve beyond learning “flat” feature representations, which are domain-agnostic and derived purely from data correlations, to “explicitly model the domain-specific structural relationships”. A key benefit of encoding relational priors in the learning process is that it can inject domain knowledge as an inductive bias, leading to more robust and reliable models. My research investigates incorporating domain knowledge by leveraging “graph-based structural priors” that explicitly model relational constraints in various visual recognition tasks. This work spans three distinct dimensions of visual recognition, progressing from coarse-level (image-level) to fine-grained (scene-level) understanding. My research highlights a crucial limitation in existing AI models: they often fail to incorporate real-world constraints, leading to significant errors. I show that even powerful, pre-trained neural networks can make severe mistakes due to a lack of domain knowledge. I argue that standard metrics like top-1 accuracy, precision, and recall are insufficient for evaluating model robustness, and propose a new metric based on rank order of the predictions as a better indicator of reliability. The benchmark on various large-scale datasets confirms that existing solutions do not sufficiently capture the domain knowledge, which is often available as a taxonomy tree, motivating our design of better learning frameworks. I also examine complex visual re-identification (Re-ID) tasks, such as monitoring animals in the wild. I find that existing foundational models struggle with new species and environments. This challenge is compounded by the high cost of manual annotation for adapting these systems to new settings. While existing unsupervised learning methods can help reduce the need for extensive labeling, they often suffer from under- and over-segmentation errors, which led me to develop more effective active learning strategies. Finally, I address the limitations of the classic Kalman filter, a widely used tool for dynamic systems. I point out that this filter makes a flawed assumption that the movement of each individual object is independent of its dynamic surroundings. In the real world, this is rarely the case. I demonstrate the need for a new filtering mechanism that not only considers an object’s past movements but also its spatial relationship with other dynamic entities in its environment. In my analysis, I observed the vision foundation models for all recognition tasks, i.e., classification, detection, and segmentation, lack the domain knowledge. I believe that our learning framework, which was designed specifically for classification, can be adapted for other recognition tasks. I speculate that a unified learning framework can be designed that can be leveraged for making vision foundation models aware of the available taxonomy.

Sandipan Dhar, Srikanth Raj Chetupalli, Preeti Rao

Speaker anonymization aims to modify the speech signal in order to protect the identity of a speaker while preserving the linguistic content. Despite the increasing use of children's voices in educational applications, such as oral reading fluency (ORF) assessment, there is little work on the anonymization aspects. In this work, we investigate the effectiveness of available speaker anonymization methods drawing from traditional speech-production based approaches and a neural codec based method. We investigate the trade-off between privacy protection, measured as the degree of anonymity, and utility preservation, which in the current context of ORF assessment, includes the segmental and suprasegmental features of children’s read speech utterances. We report objective and subjective evaluations using two child-speaker datasets: MPS and SpeechOcean. Our objective evaluation results indicate that the speech-production based method of vocal tract length normalization coupled with pitch-transposition achieves the best balance between privacy and utility. Subjective listening results indicate that naturalness is achievable across methods while the neural method fails to preserve age characteristics, which are more easily controlled by the speech-production driven methods.

Benedikt Schlereth-Groh, Sakir Furkan Yöndem, Ramin Tavakoli Kolagari

Reinforcement Learning (RL) has shown significant promise in developing autonomous navigation algorithms for complex environments. However, the direct application of RL policies trained in simulation to real-world scenarios often faces challenges due to the reality gap. This paper proposes a two-stage system incorporating a segmentation strategy and a bird’s-eye-view (BEV) representation to mitigate the domain gap between simulation and reality. In the first stage, the segmentation transforms sensor data into a simplified and interpretable representation of the surrounding area, facilitating transferability across different deployments. In the second stage, the agent navigates through the BEV map, which can be trained using a vectorized simulation environment---a setup that runs multiple parallel instances of the environment to provide a wide range of training scenarios. This vectorization enables rapid exposure to varied environmental conditions, thereby accelerating and diversifying the training of a deep RL agent to achieve optimal navigation behaviors while maintaining high-speed, in-bound trajectories. The segmentation is crucial because it supports generalization of the learned policy across different robotic platforms. The contribution of this paper lies in combining real-time semantic segmentation with a bird’s-eye-view navigation policy, resulting in a transferable and scalable framework for real-world deployment of RL-based navigation agents. Experimental results demonstrate that agents trained with this methodology exhibit robust navigation performance and adaptability in both simulated and real-world environments, validating the efficacy of combining vectorized simulation with real-world segmentation for practical robotic navigation.

Sibendu Paul, Haotian Jiang, Caren Chen

Automatically extracting engaging and high-quality humorous scenes from cinematic titles is pivotal for creating captivating video previews and snackable content, boosting user engagement on streaming platforms. Long-form cinematic titles, with their extended duration and complex narratives, challenge scene localization, while humor’s reliance on diverse modalities and its nuanced style add further complexity. This paper introduces an end-to-end system for automatically identifying and ranking humorous scenes from long-form cinematic titles, featuring shot detection, multimodal scene localization, and humor tagging optimized for cinematic content. Key innovations include a novel scene segmentation approach combining visual and textual cues, improved shot representations via guided triplet mining, and a multimodal humor tagging framework leveraging both audio and text modalities. Our system achieves an 18.3% AP improvement over state-of-the-art scene detection on the OVSD dataset and an F1 score of 0.834 for detecting humor in long text. Extensive evaluations across five cinematic titles demonstrate 87% of clips extracted by our pipeline are intended to be funny, while 98% of scenes are accurately localized. With successful generalization to trailers, these results showcase the pipeline’s potential to enhance content creation workflows, improve user engagement, and streamline snackable content generation for diverse cinematic media formats.

Qiming Guo, Bishal Khatri, Wenbo Sun, Jinwen Tang, Hua Zhang, Wenlu Wang

Underground pipeline leaks and infiltrations pose significant threats to water security and environmental safety. Traditional manual inspection methods provide limited coverage and delayed response, often missing critical anomalies. This paper proposes AquaSentinel, a novel physics-informed AI system for real-time anomaly detection in urban underground water pipeline networks. We introduce four key innovations: (1) strategic sparse sensor deployment at high-centrality nodes combined with physics-based state augmentation to achieve network-wide observability from minimal infrastructure; (2) the RTCA (Real-Time Cumulative Anomaly) detection algorithm, which employs dual-threshold monitoring with adaptive statistics to distinguish transient fluctuations from genuine anomalies; (3) a Mixture of Experts (MoE) ensemble of spatiotemporal graph neural networks that provides robust predictions by dynamically weighting model contributions; (4) causal flow-based leak localization that traces anomalies upstream to identify source nodes and affected pipe segments. Our system strategically deploys sensors at critical network junctions and leverages physics-based modeling to propagate measurements to unmonitored nodes, creating virtual sensors that enhance data availability across the entire network. Experimental evaluation using 110 leak scenarios demonstrates that AquaSentinel achieves 100% detection accuracy. This work advances pipeline monitoring by demonstrating that physics-informed sparse sensing can match the performance of dense deployments at a fraction of the cost, providing a practical solution for aging urban infrastructure.

Matthias Zeller, Vardeep Singh Sandhu, Benedikt Mersch, Jens Behley, Michael Heidingsfeld, Cyrill Stachniss

The perception of moving objects is crucial for autonomous robots performing collision avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene interpretation but do not provide direct motion information and face limitations under adverse weather. Radar sensors overcome these limitations and provide Doppler velocities, delivering direct information on dynamic objects. In this article, we address the problem of moving instance segmentation in radar point clouds to enhance scene interpretation for safety-critical tasks. Our radar instance transformer enriches the current radar scan with temporal information without passing aggregated scans through a neural network. We propose a full-resolution backbone to prevent information loss in sparse point cloud processing. Our instance transformer head incorporates essential information to enhance segmentation but also enables reliable, class-agnostic instance assignments. In sum, our approach shows superior performance on the new moving instance segmentation benchmarks, including diverse environments, and provides model-agnostic modules to enhance scene interpretation.

Chenyu You

Artificial intelligence is rapidly transforming biomedical imaging, from tumor segmentation to multimodal diagnosis, but reliable deployment in hospitals remains a pressing challenge. Models often struggle with limited labeled data, fail to generalize across scanners and institutions, and lack theoretical guarantees needed for safety-critical decisions. In this talk, I will present our research on overcoming these challenges. I will discuss label-efficient and class-imbalanced learning methods that make effective use of large-scale unlabeled data, theoretical frameworks that embed statistical principles into deep learning for stability and reliability, and medical foundation models that provide universal anatomical priors with lightweight refinement for diverse clinical settings. Together, these advances move biomedical AI beyond benchmark performance toward systems that are robust, generalizable, and clinically actionable.

Yajie Yang, Yuqing Zhao, Xiaochao Xi, Yinan Zhu

Artificial Intelligence Generated Content (AIGC) assisting image production triggers controversy in journalism while attracting attention from media agencies. Key issues involve misinformation, authenticity, semantic fidelity, and interpretability. Most AIGC tools are opaque “black boxes,” hindering the dual demands of content accuracy and semantic alignment and creating ethical, sociotechnical, and trust dilemmas. This paper explores pathways for controllable image production in journalism’s special coverage and conducts two experiments with projects from China’s media agency: (1) Experiment 1 tests cross-platform adaptability via standardized prompts across three scenes, revealing disparities in semantic alignment, cultural specificity, and visual realism driven by training-corpus bias and platform-level filtering. (2) Experiment 2 builds a human-in-the-loop modular pipeline combining high-precision segmentation (SAM, GroundingDINO), semantic alignment (BrushNet), and style regulating (Style-LoRA, Prompt-to-Prompt), ensuring editorial fidelity through CLIP-based semantic scoring, NSFW/OCR/YOLO filtering, and verifiable content credentials. Traceable deployment preserves semantic representation. Consequently, we propose a human-AI collaboration mechanism for AIGC assisted image production in special coverage and recommend evaluating Character Identity Stability (CIS), Cultural Expression Accuracy (CEA), and User-Public Appropriateness (U-PA).

Keshara Weerasinghe, Xueren Ge, Tessa Heick, Lahiru Nuwan Wijayasingha, Anthony Cortez, Abhishek Satpathy, John Stankovic, Homa Alemzadeh

Emergency Medical Services (EMS) are critical to patient survival in emergencies, but first responders often face intense cognitive demands in high-stakes situations. AI cognitive assistants, acting as virtual partners, have the potential to ease this burden by supporting real-time data collection and decision making. In pursuit of this vision, we introduce EgoEMS, the first end-to-end, high-fidelity, multimodal, multiperson dataset capturing over 20 hours of realistic, procedural EMS activities from an egocentric view in 233 simulated emergency scenarios performed by 62 participants, including 46 EMS professionals. Developed in collaboration with EMS experts and aligned with national standards, EgoEMS is captured using an open-source, low-cost, and replicable data collection system and is annotated with keysteps, timestamped audio transcripts with speaker diarization, action quality metrics, and bounding boxes with segmentation masks. Emphasizing realism, the dataset includes responder-patient interactions reflecting real-world emergency dynamics. We also present a suite of benchmarks for real-time multimodal keystep recognition and action quality estimation, essential for developing AI support tools for EMS. We hope EgoEMS inspires the research community to push the boundaries of intelligent EMS systems and ultimately contribute to improved patient outcomes.

Shiyuan Luo, Chonghao Qiu, Runlong Yu, Yiqun Xie, Xiaowei Jia

Environmental modeling faces critical challenges in predicting ecosystem dynamics across unmonitored regions due to limited and geographically imbalanced observation data. This challenge is compounded by spatial heterogeneity, causing models to learn spurious patterns that fit only local data. Unlike conventional domain generalization, environmental modeling must preserve invariant physical relationships and temporal coherence during augmentation. In this paper, we introduce Generalizable Representation Enhancement via Auxiliary Transformations (GREAT), a framework that effectively augments available datasets to improve predictions in completely unseen regions. GREAT guides the augmentation process to ensure that the original governing processes can be recovered from the augmented data, and the inclusion of the augmented data leads to improved model generalization. Specifically, GREAT learns transformation functions at multiple layers of neural networks to augment both raw environmental features and temporal influence. They are refined through a novel bi-level training process that constrains augmented data to preserve key patterns of the original source data. We demonstrate GREAT's effectiveness on stream temperature prediction across six ecologically diverse watersheds in the eastern U.S., each containing multiple stream segments. Experimental results show that GREAT significantly outperforms existing methods in zero-shot scenarios. This work provides a practical solution for environmental applications where comprehensive monitoring is infeasible.

Sumin Lee, Sungwon Park, Jeasurk Yang, Jihee Kim, Meeyoung Cha

Satellite-based slum segmentation holds significant promise in generating global estimates of urban poverty. However, the morphological heterogeneity of informal settlements presents a major challenge, hindering the ability of models trained on specific regions to generalize effectively to unseen locations. To address this, we introduce a large-scale high-resolution dataset and propose GRAM (Generalized Region-Aware Mixture-of-Experts), a two-phase test-time adaptation framework that enables robust slum segmentation without requiring labeled data from target regions. We compile a million-scale satellite imagery dataset from 12 cities across four continents for source training. Using this dataset, the model employs a Mixture-of-Experts architecture to capture region-specific slum characteristics while learning universal features through a shared backbone. During adaptation, prediction consistency across experts filters out unreliable pseudo-labels, allowing the model to generalize effectively to previously unseen regions. GRAM outperforms state-of-the-art baselines in low-resource settings such as African cities, offering a scalable and label-efficient solution for global slum mapping and data-driven urban planning.

Huili Huang, Chengeng Liu, Danrong Zhang, Shail Patel, Anastasiya Masalava, Sagar Sadak, Parisa Babolhavaeji, WeiHong Low, Max Mahdi Roozbahani, J. David Frost

Rapid post‑earthquake damage assessment is crucial for rescue and resource planning. Still, existing remote sensing methods depend on costly aerial images, expert labeling, and produce only binary damage maps for early-stage evaluation. Although ground-level images from social networks provide a valuable source to fill this gap, a large pixel-level annotated dataset for this task is still unavailable. We introduce EIDSeg, the first large-scale semantic segmentation dataset specifically for post-earthquake social media imagery. The dataset comprises 3,266 images from nine major earthquakes (2008–2023), annotated across five classes of infrastructure damage. Undamaged Building, Damaged Building, Destroyed Building, Undamaged Road, and Damaged Road. We propose a practical three-phase cross-disciplinary annotation protocol with labeling guidelines that enables consistent segmentation by non-expert annotators, achieving over 70% inter-annotator agreement. We benchmark several state-of-the-art segmentation models, identifying Encoder-only Mask Transformer (EoMT) as the top-performing method with a Mean Intersection over Union (mIoU) of 80.8%. By unlocking social networks' rich, ground-level perspective, our work paves the way for a faster, finer-grained damage assessment in the post-earthquake scenario.

Qinghong Guo, Yu Wang, Ji Cao, Tongya Zheng, Junshu Dai, Bingde Hu, Shunyu Liu, Canghong Jin

Road network representation learning (RNRL) has attracted increasing attention from both researchers and practitioners as various spatiotemporal tasks are emerging. Recent advanced methods leverage Graph Neural Networks (GNNs) and contrastive learning to characterize the spatial structure of road segments in a self-supervised paradigm. However, spatial heterogeneity and temporal dynamics of road networks raise severe challenges to the neighborhood smoothing mechanism of self-supervised GNNs. To address these issues, we propose a Dual-branch Spatial-Temporal self-supervised representation framework for enhanced road representations, termed as DST. On one hand, DST designs a mix-hop transition matrix for graph convolution to incorporate dynamic relations of roads from trajectories. Besides, DST contrasts road representations of the vanilla road network against that of hypergraphs in a spatial self-supervised way. The hypergraph is newly built based on three types of hyperedges to capture long-range relations. On the other hand, DST performs next token prediction as the temporal self-supervised task on the sequences of traffic dynamics based on a causal Transformer, which is further regularized by differentiating traffic modes of weekdays from those of weekends. Extensive experiments against state-of-the-art methods verify the superiority of our proposed framework. Moreover, the comprehensive spatiotemporal modeling facilitates DST to excel in zero-shot learning scenarios.

Adnan Firoze, Raymond A. Yeh, Daniel Aliaga

About 25% of the world’s population live in informal urban settlements containing densely packed buildings (approximately 8,000 houses per square-km) which do not lend themselves favorably to state-of-the-art satellite-based building segmentation methods due to, for example, occlusion, vegetation, shadows and low resolution. To address these challenges, we introduce a novel instance segmentation and counting approach for dense buildings. Our system first extracts a conservative set of tentative building center points using a deep network for jumpstarting a Segment Anything Model 2 (SAM2) module to produce an initial over-segmentation. Second, we use a graph neural network to refine the over-segmented regions into polygons representing accurate building masks. Experiments show that our approach achieves higher accuracy in instance segmentation and counting especially in challenging densely packed building areas in Brazil, Mexico, India, Pakistan, and Kenya, for instance.

Ankit Bhardwaj, Rohail Asim, Sachin Kumar Chauhan, Yasir Zaki, Lakshmi Subramanian

Free-flow road networks, such as suburban highways, are increasingly experiencing traffic congestion due to growing commuter inflow and limited infrastructure. Traditional control mechanisms—traffic signals or local heuristics—are ineffective or infeasible in these high-speed, signal-free environments. We introduce self-regulating cars, a reinforcement learning-based traffic control protocol that dynamically modulates vehicle speeds to optimize throughput and prevent congestion, without requiring new physical infrastructure. Our approach integrates classical traffic flow theory, gap acceptance models, and microscopic simulation into a physics-informed RL framework. By abstracting roads into super-segments, the agent captures emergent flow dynamics and learns robust speed modulation policies from instantaneous traffic observations. Evaluated in the high-fidelity PTV Vissim simulator on a real-world highway network, our method improves total throughput by 5%, reduces average delay by 13%, and decreases total stops by 3% compared to the no-control setting. It also achieves smoother, congestion-resistant flow while generalizing across varied traffic patterns—demonstrating its potential for scalable, ML-driven traffic management.

Saman Ahmadi, Mahdi Jalili

We study the energy-optimal shortest path problem for electric vehicles (EVs) in large-scale road networks, where recuperated energy along downhill segments introduces negative energy costs. While traditional point-to-point pathfinding algorithms for EVs assume a known initial energy level, many real-world scenarios involving uncertainty in available energy require planning optimal paths for all possible initial energy levels, a task known as energy-optimal profile search. Existing solutions typically rely on specialized profile-merging procedures within a label-correcting framework that results in searching over complex profiles. In this paper, we propose a simple yet effective label-setting approach based on multi-objective A* search, which employs a novel profile dominance rule to avoid generating and handling complex profiles. We develop four variants of our method and evaluate them on real-world road networks enriched with realistic energy consumption data. Experimental results demonstrate that our energy profile A* search achieves performance comparable to energy-optimal A* with a known initial energy level.