Publications

ECUAS: A family of metrics for principled evaluation of uncertainty-augmented systems
Lautaro Estienne
Erik Ernst
Matias Vera
Pablo Piantanida
LUCIANA FERRER
In high-stakes automated decision-making, access to predictive uncertainty is essential for enabling users -- human or downstream systems --… (see more) to accept or reject predictions based on application-specific cost trade-offs. Such uncertainty-augmented (UA) systems -- i.e., systems that output both predictions and uncertainty scores -- are currently being assessed in the literature in a variety of ways, using separate metrics to evaluate the predictions and the uncertainty scores, setting a cost function with a fixed rejection cost or integrating over a coverage-risk curve. We argue that these evaluation approaches are inadequate for assessing overall performance of the UA system for decision making under uncertainty and propose a novel family of metrics, ECUAS
2026-12-30
OpenReview (published)
openreview.net
openreview.net
Generative Cutout Animation
Ivan Puhachov
Noam Aigerman
Thibault Groueix
Mikhail Bessmeltsev
Abstract Cutout animation is one of the earliest forms of animation, and to this day remains a popular technique featured in numerous films … (see more)including Monty Python and South Park series. Most computer animation systems, however, focus on different styles, including cel animation, making cutout animation somewhat underexplored. As creating cutouts is meticulous, we propose a novel generative cutout animation system. Taking a skeletal animation and a text prompt as input, we automatically generate a 2.5D cutout rig ready for production in films and games. Our system optimizes cutout images with an SDS (Score Distillation Sampling) loss with a LoRA (Low‐Rank Adaptation) prior, in multiple target poses. Naïvely optimizing an SDS loss, however, would lead to inconsistent target pose images, and, as a result, blurry or transparent cutouts. To address this, we introduce a novel optimization with techniques targeting pose and noise consistency, resulting in coherent target images and sharp cutouts. We validate our system by demonstrating a gallery of results, comparing with previous works, ablations, and other analyses. Once generated, our cutout rigs can be used both for the given input animation and repurposed for other animations or edited as independent assets.
2026-12-30
OpenReview (published)
doi.org
openreview.net
QueStER: Query Specification for Generative Keyword-Based Retrieval
Arthur SATOUF
Yuxuan Zong
Habiboulaye Amadou Boubacar
Pablo Piantanida
Benjamin Piwowarski
Generative retrieval (GR) differs from the traditional index–then–retrieve pipeline by storing relevance in model parameters and generat… (see more)ing retrieval cues directly from the query, but it can be brittle out of domain and expensive to scale. We introduce QueStER (QUEry SpecificaTion for gEnerative Keyword-Based Retrieval), which bridges GR and query reformulation by learning to generate explicit keyword-based search specifications. Given a user query, a lightweight LLM produces a keyword query that is executed by a standard retriever (BM25), combining the generalization benefits of generative query rewriting with the efficiency and scalability of lexical indexing. We train the rewriting policy with reinforcement learning techniques. Across in- and out-of-domain evaluations, QueStER consistently improves over BM25 and is competitive with neural IR baselines, while maintaining strong efficiency.
2026-12-30
OpenReview (published)
openreview.net
openreview.net
Examining the relationship between secondary students’ ICT engagement and computational thinking achievement: Does gender make a difference?
Echo Zexuan Pan
Matthew D. Johnson
Maria Cutumisu
2026-11-30
Studies in Educational Evaluation (published)
doi.org
Cross-component causal coupling modeling and multi-order spectral representation for interpretable mechanical fault diagnosis
Yi Gao
Haidong Shao
Jun Ding
Jiarui Liu
Mingze Xu
Jiale Zhang
Daolin Xu
2026-10-31
Engineering Applications of Artificial Intelligence (published)
doi.org
Improved mapping of Arctic fractional land cover and land cover change from multi-resolution optical remote sensing
Xiaoran Zhu
Jonathan A. Wang
Oliver Sonnentag
Isla H. Myers-Smith
Daryl Yang
Kathleen M. Orndahl
Leon Nill
Antoine Caron-Guay
Étienne Laliberté
Mark A. Friedl
Changes in Arctic tundra vegetation, driven by climate change, may be inducing major shifts in ecosystem services and the Arctic carbon budg… (see more)et, and altering high latitude feedbacks to the climate system. Field-based studies have documented warming-induced shrub expansion, and remote sensing has revealed heterogeneous, but primarily positive, trends in peak summer greenness across the Arctic. However, efforts to move beyond remotely sensed measures of spectral greening to quantify the spatial extent and rate of shrub expansion have been constrained by spectral similarities among tundra vegetation types, limited ground truth data, low revisit frequency of satellite observations, and sub-pixel heterogeneity of land cover at medium spatial resolution (30 m). To address these challenges, we developed a methodology that integrates high spatial resolution (2 m) commercial satellite imagery with Harmonized Landsat and Sentinel-2 observations in a machine learning framework, and used it to produce annual maps for 2016 to 2023 of sub-pixel land cover fractions at 30-m spatial resolution across three Arctic tundra ecoregions spanning 3.35 × 105 km2 between the Seward and Tuktoyaktuk Peninsulas. Uncertainty was quantified at each pixel via Monte Carlo resampling. Independent accuracy assessments yielded good accuracies (mean squared errors of 15.98% and 11.89% for low-stature vegetation and erect shrub cover, respectively), that were comparable to or exceeded previous mapping efforts. Further, repeat commercial satellite image pairs enabled the first assessment of mapped fractional cover change in Arctic tundra (R2 of 0.46 and 0.55, change direction accuracies of 77% and 78% for low-stature vegetation and erect shrub cover, respectively). This novel, scalable, multi-sensor approach to fractional land cover mapping produced the first annual maps of land cover fractions in the Arctic tundra, which support more accurate representation of vegetation dynamics and their linkages to climate change and disturbance processes.
2026-09-30
Remote Sensing of Environment (published)
doi.org
Debiased One-Shot NAS Via Density-Aware Sampling
Mehraveh Javan Roshtkhari
Matthew Toews
Marco Pedersoli
One-shot Neural Architecture Search (NAS) is based on training a supernet, a single model from which many different architectures with share… (see more)d weights are sampled and updated during training. Training the shared weights of the supernet is much more computationally efficient than training each architecture independently. However, during the supernet training, architectures with similar gradients (dense regions in the gradient space) would cooperate with each other increasing their training, while architectures in sparse(low-density) regions would not receive as much training benefit from others. This does not allow all architectures to be trained with the same amount of effective updates, producing a training bias that favors architectures in denser regions. As a consequence, the correlation between the supernet estimations and the actual performance of models independently trained is reduced. This negatively affects the supernet's ability to select good architectures once trained. In this paper, we propose two computationally feasible ways for different computational budgets and search spaces to approximate the architecture densities and implement a density-aware debiasing mechanism for supernet training. We propose a fine-grained density calculation for numerable smaller search spaces and an online density approximation based on density prototypes from a clustering algorithm for larger spaces. We validate our method on CIFAR10, CIFAR100 and ImageNet datasets using various search strategies and show consistently improved results compared to several single-path one-shot supernet training methods.
2026-09-20
IEEE/CVF Conference on Computer Vision and Pattern Recognition (published)
openreview.net
openreview.net
FACT-GS: Frequency-Aligned Complexity-Aware Texture Reparameterization for 2D Gaussian Splatting
Tianhao Xie
Linlian Jiang
Xinxin Zuo
Yang Wang
Tiberiu Popa
Realistic scene appearance modeling has advanced rapidly with Gaussian Splatting, which enables real-time, high-quality rendering. Recent ad… (see more)vances introduced per-primitive textures that incorporate spatial color variations within each Gaussian, improving their expressiveness. However, texture-based Gaussians parameterize appearance with a uniform per-Gaussian sampling grid, allocating equal sampling density regardless of local visual complexity, which leads to inefficient texture space utilization. We introduce FACT-GS, a Frequency-Aligned Complexity-Aware Texture Gaussian Splatting framework that allocates texture sampling density according to local visual frequency. Grounded in adaptive sampling theory, FACT-GS reformulates texture parameterization as a differentiable sampling-density allocation problem, replacing the uniform textures with a learnable frequency-aware allocation strategy implemented via a deformation field whose Jacobian modulates local sampling density. Built on 2D Gaussian Splatting, FACT-GS performs non-uniform sampling on fixed-resolution texture grids, preserving real-time performance while recovering sharper high-frequency details under the same parameter budget.
2026-09-20
IEEE/CVF Conference on Computer Vision and Pattern Recognition (published)
doi.org
openreview.net
Learning Through Creation: A Hash-Free Framework for On-the-Fly Category Discovery
Bohan Zhang
Weidong Tang
Zhixiang Chi
Yi Jin
Zhenbo Li
Yang Wang
Yanan Wu
On-the-Fly Category Discovery (OCD) aims to recognize known classes while simultaneously discovering emerging novel categories during infere… (see more)nce, using supervision only from known classes during offline training. Existing approaches rely either on fixed label supervision or on diffusion-based augmentations to enhance the backbone, yet none of them explicitly train the model to perform the discovery task required at test time. It is fundamentally unreasonable to expect a model optimized on limited labeled data to carry out a qualitatively different discovery objective during inference. This mismatch creates a clear optimization misalignment between the offline learning stage and the online discovery stage. In addition, prior methods often depend on hash-based encodings or severe feature compression, which further limits representational capacity. To address these issues, we propose Learning through Creation (LTC), a fully feature-based and hash-free framework that injects novel-category awareness directly into offline learning. At its core is a lightweight, online pseudo-unknown generator driven by kernel-energy minimization and entropy maximization (MKEE). Unlike previous methods that generate synthetic samples once before training, our generator evolves jointly with the model's dynamics and synthesizes pseudo-novel instances on-the-fly at negligible cost. These samples are incorporated through a dual max-margin objective with adaptive thresholding, strengthening the model's ability to delineate and detect unknown regions through explicit creation. Extensive experiments across seven benchmarks show that LTC consistently outperforms prior work, achieving improvements ranging from 1.5% to 13.1% in all-class accuracy.
2026-09-20
IEEE/CVF Conference on Computer Vision and Pattern Recognition (published)
openreview.net
openreview.net
Paired neuronal-haemodynamic neurovascular coupling measures and cognitive performance in vascular pathology: protocol for a systematic review and meta-analysis
V. D. Abramova
Marta Estrada
Veronica Egovtseva
Ksenya Pronyaeva
Rustam Talybov
Taleb M. Almansoori
Mohammad I. K. Hamad
Milos Ljubisavljevic
Danilo Bzdok
Yauhen Statsenko
Abstract Background Neurovascular coupling (NVC) links neuronal activity to haemodynamic responses. Altered NVC may contribute to cognitive … (see more)impairment in vascular pathology, but evidence is limited by inconsistent definitions and by the conflation of paired neuronal–haemodynamic NVC measures with haemodynamic-only, model-derived, or imaging-derived proxy measures. Aim and objectives The primary aim is to evaluate the association between paired neuronal–haemodynamic NVC measures and cognitive performance in middle-aged and older adults with vascular pathology. Secondarily, we will compare paired neuronal–haemodynamic NVC measures between patients with vascular pathology and age-matched controls without vascular pathology, determine whether predefined pathology strata show distinct impairment profiles, and examine whether the NVC–cognition association varies across cognitive domains. As an exploratory objective, we will synthesise evidence on the prognostic utility of paired neuronal–haemodynamic NVC measures in predicting future cognitive decline. Collectively, these objectives address critical gaps in a fragmented literature characterised by heterogeneous methodologies, populations, and assessment techniques, as well as methodological limitations including small samples, cross-sectional designs, and inadequate confounder control. This review will systematically synthesise existing findings, identify remaining knowledge gaps, and outline priorities for future research. Methods and analysis This protocol follows PRISMA-P guidance and is registered with PROSPERO (CRD420261351976). We searched five electronic databases from inception to 28 July 2026 for peer-reviewed English-language studies. Eligible studies include middle-aged and older adults with vascular pathology and/or participants from relevant comparator groups and report paired neuronal–haemodynamic NVC measures based on independently acquired neuronal and haemodynamic or vascular signals. The primary synthesis will evaluate associations between paired neuronal–haemodynamic NVC measures and cognitive performance, with meta-analysis conducted only for synthesis strata that meet prespecified feasibility criteria.
2026-09-20
Systematic Reviews (published)
doi.org
Revisiting Forward-Looking Sonar Image Registration
Arturo Del Castillo Bernal
Kyungmin Jung
Junha Yoo
James Richard Forbes
Forward-looking sonar is a common underwater sensing modality. Sonar-image registration is a crucial process in many sonar-aided navigation … (see more)tasks. Since the last comparison made between forward-looking sonar image registration methods, different methods have been proposed in the literature, as well as modern point-feature extractors that can be used for registration. An updated comparison is performed both on simulated and experimental sonar-image registration tasks. It is shown that Radon-transformation-based registration outperforms all other registration methods in terms of accuracy and robustness, especially when there is a large change in position between sonar images.
2026-09-20
WPNMR @ IEEE/RSJ International Conference on Intelligent Robots and Systems (poster)
openreview.net
openreview.net
BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings
Alexandre Andre
Shivashriganesh P. Mahato
Vinam Arora
Keshav Balaji
Divyansha Lachi
Nanda H. Krishna
Jingyun Xiao
Yizi Zhang
Ximeng Mao
Wenrui Ma
Han Yu
International Brain Laboratory
Daniel Birman
Niccolò Bonacchi
Gaelle A. Chapuis
Joana A. Catarino
Felicia Davatolhagh
Mayo Faulkner
Laura Freitas-Silva
Fei Hu … (see 22 more)
Julia M. Huntenburg
Anup Khanal
Inês Laranjeira
Petrina Lau
Guido T. Meijer
Nathaniel J. Miska
Jean-Paul Noel
Alejandro Pan-Vazquez
Georg Raiser
Cyrille Rossant
Karolina Z. Socha
Anne E. Urai
Miles J. Wells
Steven J. West
Olivier Winter
Blake Richards
Guillaume Lajoie
Cole Hurwitz
Mehdi Azabou
Matthew R. Whiteway
Liam Paninski
Eva L. Dyer
Advances in large-scale neural recording have made it possible to collect data across many animals and distributed brain regions, raising th… (see more)e question of whether this scale can be exploited to learn general-purpose neural representations transferable across diverse downstream tasks. Yet, progress toward this goal has been limited by fragmented evaluation protocols and a narrow focus on individual task domains. Here, we present BrainWideBench, a benchmark for evaluating across-animal transfer on multi-region neural recordings, built on the International Brain Laboratory Brainwide Map dataset of neural and behavioral recordings spanning 276 brain regions from 139 mice performing a sensory-guided decision-making task. The benchmark is organized around three complementary task suites that evaluate whether learned representations support downstream decoding of behavior, can predict masked or future neural activity, and can recover biologically meaningful anatomical organization. With this benchmark, we systematically evaluate pretraining methods across transfer settings, including finetuning on downstream objectives and zero-shot generalization to unseen animals. Our results confirm pretraining improves performance over matched single-session baselines, but we show current methods exhibit heterogeneity in transfer capabilities: gains depend strongly on the alignment between pretraining objectives and downstream tasks. No single approach performs uniformly well across all three suites, and most methods are designed to only address a subset of them. Together, these findings suggest that learning representations that jointly generalize across behavior, dynamics, and anatomy remains an open challenge. By providing a unified and reproducible evaluation suite, BrainWideBench establishes a framework for measuring progress toward general-purpose models of the mouse brain.
2026-09-17
arXiv (preprint)
doi.org
arxiv.org