GenAI4ELab/papercli-papers huggingface dataset:
SilverLining: Data-First Mitigation of Spatial and Spectral Shortcuts Without Introducing New Confounders
Authors: Balagopal Unnikrishnan; Michael Brudno; Chris McIntosh
Venue: WACV 2026 (main)

Abstract: Deep neural networks exploit shortcuts--spurious correlations like laterality markers (spatial) or scanner-specific noise (spectral)--that severely compromise generalization. Many healthcare applications face multiple concurrent shortcuts that are both spatial and spectral, which existing methods struggle to handle. We present SilverLining, an attention-based preprocessing framework that simultaneously identifies and mitigates both spatial and spectral shortcuts without introducing new spurious correlations. Our key insight is that naive removal of shortcut features can itself create new shortcuts, where models learn to exploit the removal patterns as new spurious correlations. We address this through a principled confounder-free correction strategy that maintains consistent preprocessing patterns across all classes in both spatial and frequency domains, preventing new confounders. Extensive experiments demonstrate SilverLining's effectiveness: achieving 0.87 AUC on controlled vision tasks and 0.94 AUC on counter-shortcut medical imaging evaluation where shortcuts are reversed; improving cross-institutional chest X-ray classification from 0.72 to 0.77 AUC; and 0.54 mAP on polyp detection despite natural spurious correlations from surgical overlays. Our data-centric approach provides an effective solution for reducing multiple types of data shortcuts without architectural modifications, creating preprocessed datasets that improve model robustness across both classification and detection tasks. Our codebase is available at https://github.com/theidentity/SilverLining_WACV2026/.
Real-Time Tracking of Flexible Markers in Low-Contrast Fluoroscopy Using a Deep Neural Network Trained Solely on Synthetic Data
Authors: Tomoki Uchiyama; Yukinobu Sakata; Ryusuke Hirai; Hitoshi Ishikawa; Shinichiro Mori
Venue: WACV 2026 (main)

Abstract: In radiation therapy, fiducial markers implanted in a patient's body are tracked using X-ray fluoroscopy to estimate tumor positions.However, flexible markers, such as Gold Anchor^ \textregistered  (Naslund Medical AB, Sweden), deform within the body, making conventional template matching challenging.While deep learning offers a promising solution, the extensive collection and annotation of clinical data required for training poses a significant barrier to adoption.To address this, we propose a tracking framework that utilizes a lightweight Siamese CNN trained exclusively on synthetic fluoroscopy images.Our method generates synthetic data simulating diverse marker deformations under low-contrast and high-noise conditions, employs dynamic programming for stable initial detection, and performs real-time tracking with a particle filter.In evaluations using clinical data, our method achieves a tracking accuracy of 0.42 +- 0.12 pixels for prostate cancer cases and 0.97 +- 0.53 pixels for pancreatic cancer cases.This significantly outperforms conventional methods, particularly in challenging low-contrast pancreatic cancer cases.With TensorRT optimization, the framework achieves a processing speed of 3.8 ms/frame.This work presents a practical solution for high-accuracy tracking, reducing data collection costs and facilitating the use of deep learning in clinical applications.
Ordinal-Aware Multimodal Engagement Recognition for Collaborative Learning
Authors: Nha Tran; Dat Ly; Phi Ta; Hung Nguyen; Hien D. Nguyen
Venue: WACV 2026 (main)

Abstract: Assessing student engagement is critical for collaborative learning but remains a challenging task. Existing approaches often rely on controlled laboratory or online settings, which fail to capture the complexity of real-world classrooms. Furthermore, current datasets are scarce and rarely provide both individual- and group-level annotations, limiting the development of robust and generalizable models. To address these gaps, we propose CORE-Net, a multimodal architecture that integrates context modeling to capture group-level dynamics and ordinal supervision to account for the ordinal nature of engagement levels. We also present COLER, a large-scale dataset collected in authentic classroom environments with rich annotations at multiple levels. Experiments demonstrate that CORE- Net achieves 89.63% accuracy and 94.80 QWK, significantly outperforming strong baselines such as BlockGCN and MoViNet. Ablation studies further highlight the critical role of both context modeling and ordinal supervision. Our work establishes a robust and scalable foundation for automated engagement assessment, supporting timely feedback and enhancing the effectiveness of collaborative learning.
AuViRe: Audio-visual Speech Representation Reconstruction for Deepfake Temporal Localization
Authors: Christos Koutlis; Symeon Papadopoulos
Venue: WACV 2026 (main)

Abstract: With the rapid advancement of sophisticated synthetic audio-visual content, e.g., for subtle malicious manipulations, ensuring the perceptual integrity of digital media has become paramount. This work presents a novel approach to temporal localization of deepfakes by leveraging Audio-Visual Speech Representation Reconstruction (AuViRe). Specifically, our approach reconstructs speech representations from one modality (e.g., visual lip movements) based on the other (e.g., audio waveform). This cross-modal reconstruction becomes significantly more challenging, leading to amplified discrepancies, in manipulated regions, thereby providing robust discriminative cues for precise forgery localization. AuViRe outperforms the State-of-the-Art by +8.9 AP@0.95 on LAV-DF, +9.6 AP@0.5 on AV-Deepfake1M, and +5.1 AUC on an in-the-wild experiment. Code will be publicly available upon acceptance.
DOTGraph: CLIP-Driven Feature Disentanglement and Optimal Transport based Graph Learning for Few-Shot Segmentation
Authors: Shreya Biswas; Zhaozheng Yin
Venue: WACV 2026 (main)

Abstract: Few-shot semantic segmentation aims to build robust models that segment unseen objects using only a few labeled examples. Existing FSS approaches, which rely on semantic feature matching, often suffer from Background Bias, Pose-Scale Discrepancy Bias, and the inability to capture fine object details. These limitations hinder their ability to generalize to novel categories, especially in scenarios with high intra-class variability and fine-grained object structures. To overcome these challenges, we propose DOT-Graph, a novel framework that designs CLIP-driven feature Disentanglement and Optimal Transport-based Graph learning for robust few-shot segmentation. We evaluate DOTGraph on PASCAL-5i and COCO-20i, achieving state-of-the-art performance with improvements in various few-shot settings. Our results demonstrate that DOTGraph effectively mitigates background bias, improves feature alignment, and enhances fine-grained segmentation. The code will be released soon.
TA-Prompting: Enhancing Video Large Language Models for Dense Video Captioning via Temporal Anchors
Authors: Wei-Yuan Cheng; Kai-Po Chang; Chi-Pin Huang; Fu-En Yang; Yu-Chiang Frank Wang
Venue: WACV 2026 (main)

Abstract: Dense video captioning aims to interpret and describe all temporally localized events throughout an input video. Recent state-of-the-art methods leverage large language models (LLMs) to provide detailed moment descriptions for video data. However, existing VideoLLMs remain challenging in identifying precise event boundaries in untrimmed videos, causing the generated captions to be not properly grounded. In this paper, we propose TA-Prompting, which enhances VideoLLMs via Temporal Anchors that learn to precisely localize events and prompt the VideoLLMs to perform temporal-aware video event understanding. During inference, in order to properly determine the output caption sequence from an arbitrary number of events presented within a video, we introduce an event coherent sampling strategy to select event captions with sufficient coherence across temporal events and cross-modal similarity with the given video. Through extensive experiments on benchmark datasets, we show that our TA-Prompting is favorable against state-of-the-art VideoLLMs, yielding superior performance on dense video captioning and temporal understanding tasks including moment retrieval and temporalQA.
Predicting Task fMRI Contrasts from Resting-State fMRI Using Sparse 3D Convolutions
Authors: Ivan Sviridov; Maria Boyko; Maksim Sharaev
Venue: WACV 2026 (main)

Abstract: Functional Magnetic Resonance Imaging (fMRI) enables the non-invasive mapping of brain activity during various cognitive processes, and understanding how specific cognitive tasks activate distinct brain regions is crucial for both neuroscience and clinical applications. However, acquiring task-based fMRI (tfMRI) is costly, time-consuming, and often infeasible for individuals unable to perform tasks. Resting-state fMRI (rsfMRI), which is more widely available and does not require task compliance, can instead be leveraged to infer task-related activations. In this work, we propose BrainSparseCNN, a sparse 3D convolutional neural network that exploits the brain's spatial structure to predict tfMRI contrasts from rsfMRI while efficiently processing high-dimensional neuroimaging data. BrainSparseCNN achieves up to 7% higher Pearson correlation than surface-based and dense volumetric competitors, with statistically significant gains (p < 0.01) across all tasks, furthermore improving spatial alignment, subject identification accuracy, and saliency interpretability while maintaining computational efficiency. Overall, BrainSparseCNN provides a more precise, interpretable, and scalable framework for inferring individual functional activation maps from resting-state data, with code publicly available at https://github.com/univanxx/brainsparsecnn.
Causality-Driven Audits of Model Robustness
Authors: Nathan Drenkow; William Paul; Chris Ribaudo; Mathias Unberath
Venue: WACV 2026 (main)

Abstract: Robustness audits of deep neural networks (DNN) provide a means to uncover model sensitivities to the challenging real-world imaging conditions that significantly degrade DNN performance in-the-wild. Such conditions are often the result of multiple interacting factors inherent to the environment, sensor, or processing pipeline and may lead to complex image distortions that are not easily categorized. When robustness audits are limited to a set of isolated imaging effects or distortions, the results cannot be (easily) transferred to real-world conditions where image corruptions may be more complex or nuanced. To address this challenge, we present a new alternative robustness auditing method that uses causal inference to measure DNN sensitivities to the factors of the imaging process that cause complex distortions. Our approach uses causal models to explicitly encode assumptions about the domain-relevant factors and their interactions. Then, through extensive experiments on natural and rendered images across multiple vision tasks, we show that our approach reliably estimates causal effects of each factor on DNN performance using only observational domain data. These causal effects directly tie DNN sensitivities to observable properties of the imaging pipeline in the domain of interest towards reducing the risk of unexpected DNN failures when deployed in that domain.
Harnessing Object Grounding for Time-Sensitive Video Understanding
Authors: Tz-Ying Wu; Sharath Nittur Sridhar; Subarna Tripathi
Venue: WACV 2026 (main)

Abstract: We propose to improve the time-sensitive video understanding (TSV) capability of video large language models (Video-LLMs) with grounded objects (GO). We hypothesize that TSV tasks can benefit from GO within frames, which is supported by our preliminary experiments on LITA, a state-of-the-art Video-LLM for reasoning temporal localization. While augmenting prompts with textual descriptions of these object annotations improves the performance of LITA, it also introduces extra token length and susceptibility to the noise in object-level information. To address this, we propose GO-Tokenizer, a lightweight add-on module for Video-LLMs leveraging off-the-shelf object detectors to encode compact object information on the fly. Experimental results demonstrate that pretraining with GO-Tokenizer outperforms the vanilla Video-LLM and its counterpart, utilizing textual descriptions of objects in the prompt. The gain generalizes across different models, datasets, and video understanding tasks, such as reasoning temporal localization and dense captioning.
Pointmap-Conditioned Diffusion for Consistent Novel View Synthesis
Authors: Thang-Anh-Quan Nguyen; Laurent Caraffa; Jean-Philippe Tarel; Roland Brémond
Venue: WACV 2026 (main)

Abstract: Synthesizing extrapolated views remains a difficult task, especially in urban driving scenes, where the only reliable sources of data are limited RGB captures and sparse LiDAR points. To address this problem, we present PointmapDiff, a framework for novel view synthesis that utilizes pre-trained 2D diffusion models. Our method leverages point maps (i.e., rasterized 3D scene coordinates) as a conditioning signal, capturing geometric and photometric priors from the reference images to guide the image generation process. With the proposed reference attention layers and ControlNet for point map features, PointmapDiff can generate accurate and consistent results across varying viewpoints while respecting geometric fidelity. Experiments on real-life driving data demonstrate that our method achieves high-quality generation with flexibility over point map conditioning signals (e.g., dense depth map or even sparse LiDAR points) and can be used to distill to 3D representations such as 3D Gaussian Splatting for improving view extrapolation.
Reciprocal Teaching: Dynamic Multi-Model Teacher-Student Learning for Multiple Noisy Annotations
Authors: Wenjie Ai; Cuong C. Nguyen; Adrian Hilton; Gustavo Carneiro
Venue: WACV 2026 (main)

Abstract: As datasets grow in size, expert-based annotation becomes increasingly impractical, making crowdsourcing a scalable and cost-effective alternative. In crowdsourcing, samples are typically annotated by multiple workers and aggregated via majority voting, a process that overlooks annotator-specific biases and introduces noisy labels that can impair downstream models. Traditional multi-rater methods attempt to model annotator biases (e.g., with transition matrices) but often overfit when faced with many classes or few, noisy annotators. By contrast, Learning with Noisy Labels (LNL) assumes a single noisy label per sample and has demonstrated that robust strategies (e.g., semi-supervised and multi-model learning) usually outperform bias-estimation methods, though these approaches remain underexplored in multi-annotator settings.To bridge this gap, we propose the Reciprocal Teacher-student Learning from Multi-rater Noisy Annotation (RETINA), a framework that integrates LNL techniques into multi-rater learning. RETINA trains annotator-specific models to capture individual labeling patterns and employs a dynamic teacher-student process, where the teacher identifies clean and noisy samples to guide the student. Experiments on synthetic and real-world benchmarks, including our proposed SynMRL benchmark, show that RETINA outperforms existing multi-rater methods, particularly in high-noise, low-annotator, many-class settings.
OracleGS: Grounding Generative Priors for Sparse-View Gaussian Splatting
Authors: Atakan Topaloğlu; Kunyi Li; Michael Niemeyer; Nassir Navab; A. Murat Tekalp; Federico Tombari
Venue: WACV 2026 (main)

Abstract: Sparse-view novel view synthesis is fundamentally ill-posed due to severe geometric ambiguity. Current methods are caught in a trade-off: regressive models are geometrically faithful but incomplete, whereas generative models can complete scenes but often introduce structural inconsistencies. We propose OracleGS, a novel framework that reconciles generative completeness with regressive fidelity for sparse view Gaussian Splatting. Instead of using generative models to patch incomplete reconstructions, our "propose-and-validate" framework first leverages a pre-trained 3D-aware diffusion model to synthesize novel views to propose a complete scene. We then repurpose a multi-view stereo (MVS) model as a 3D-aware oracle to validate the 3D uncertainties of generated views, using its attention maps to reveal regions where the generated views are well-supported by multi-view evidence versus where they fall into regions of high uncertainty due to occlusion, lack of texture, or direct inconsistency. This uncertainty signal directly guides the optimization of a 3D Gaussian Splatting model via an uncertainty-weighted loss. Our approach conditions the powerful generative prior on multi-view geometric evidence, filtering hallucinatory artifacts while preserving plausible completions in under-constrained regions, outperforming state-of-the-art methods on datasets including Mip-NeRF 360 and NeRF Synthetic.
DICE: Discrete Inversion Enabling Controllable Editing for Masked Generative Models
Authors: Xiaoxiao He; Quan Dao; Ligong Han; Song Wen; Minhao Bai; Di Liu; Han Zhang; Felix Juefei-Xu; Chaowei Tan; Bo Liu; Martin Renqiang Min; Kang Li; Faez Ahmed; Akash Srivastava; Hongdong Li; Junzhou Huang; Dimitris N. Metaxas
Venue: WACV 2026 (main)

Abstract: Recent advances in discrete diffusion models have demonstrated strong performance in image generation and masked language modeling, yet they remain limited in their capacity for controlled content editing. We propose DICE (Discrete Inversion for Controllable Editing), a novel framework that pioneers precise inversion capabilities for discrete diffusion models, including both masked generative and multinomial diffusion variants. Our key innovation lies in capturing noise sequences and masking patterns during reverse diffusion process, enabling both accurate reconstruction and flexible editing without relying on predefined masks or attention-based manipulations. Through comprehensive experiments across image and text modalities using models such as Paella, VQ-Diffusion, RoBERTa and LLaDA, we demonstrate that DICE successfully maintains high fidelity to the original data while significantly expanding editing capabilities. These results establish new possibilities for fine-grained content manipulation in discrete spaces
Human Pose Aggregation for Multi-View Temporal Video Alignment
Authors: Fabien Delattre; Tsung-Wei Huang; Guan-Ming Su; Erik Learned-Miller
Venue: WACV 2026 (main)

Abstract: When multiple videos of a scene are taken from differing viewpoints without precise synchronization, it can be difficult to temporally align them after the fact. Often the metadata or audio needed to do so is missing or inaccurate. But human motion in such videos can provide a strong signal for identifying matching time points across videos, through analysis of pose and movement. In this work, we leverage view-invariant human pose features to synchronize videos. Unlike previous human pose-based alignment techniques, our method can align videos containing multiple people without performing tracking or re-identification across views. We achieve this by aggregating pose information from multiple people into a single frame descriptor. This also enables fast \mathcal O (n\log n) search for the optimal alignment. This simple but effective strategy leads to major and consistent improvements over existing human-based and visual feature temporal alignment techniques.
RampWatch: An In-the-Wild Dataset and Text-Guided Detection Framework for Recreational Vessels
Authors: Malik Muhammad Asim; Claire B. Smallwood; Abdullah Tariq; Johnny Lo; Syed Zulqarnain Gilani
Venue: WACV 2026 (main)

Abstract: Detecting small, recreational vessels in coastal environments remains a persistent challenge due to complex backgrounds, dynamic lighting conditions, and the scarcity of annotated data for non-commercial maritime traffic. Despite their socio-economic significance, recreational boats are underrepresented in existing datasets and are poorly detected by standard object detectors, particularly in open-vocabulary scenarios. To address this gap, we present RampWatch, an in-the-wild dataset curated from surveillance footage at multiple boat ramps. RampWatch provides instance annotations across seven categories of recreational vessels, captured under diverse weather, lighting, and occlusion conditions. To benchmark detection in this domain, we introduce YOLO-TG, a novel detection framework that augments YOLOv11 with a text encoder for open-vocabulary recognition and a self-attention module for enhanced spatial reasoning. YOLO-TG adopts a dual-stream design: visual features are extracted via a hierarchical YOLO backbone, while semantic embeddings from natural language prompts are encoded by a frozen language encoder. These modalities are fused via lightweight cross-modal attention, enabling text-guided detection without retraining. YOLO-TG achieves a +12% relative improvement in mAP@50-95 over strong YOLOv11 baselines on RampWatch, and demonstrates robust cross-domain generalization, with gains of +22% on the Singapore Maritime Dataset and +4.3% on the Split Port Ship Classification Dataset. These results highlight the effectiveness of cross-modal grounding and domain-specific datasets for advancing open-world maritime surveillance.
How to Design and Train Your Implicit Neural Representation for Video Compression
Authors: Matthew Gwilliam; Roy Zhang; Namitha Padmanabhan; Hongyang Du; Abhinav Shrivastava
Venue: WACV 2026 (main)

Abstract: Implicit neural representation (INR) methods for video compression have recently achieved visual quality and compression ratios that are competitive with traditional pipelines. However, due to the need for per-sample network training, the encoding speeds of these methods are too slow for practical adoption. We develop a library to allow us to disentangle and review the components of methods from the NeRV family, reframing their performance in terms of not only size-quality trade-offs, but also impacts on training time. We uncover principles for effective video INR design and propose a state-of-the-art configuration of these components, Rabbit NeRV (RNeRV). When all methods are given equal training time (equivalent to 300 NeRV epochs) for 7 different UVG videos at 1080p, RNeRV achieves +1.27% PSNR on average compared to the best-performing alternative for each video in our NeRV library. We then tackle the encoding speed issue head-on by investigating the viability of hyper-networks, which predict INR weights from video inputs, to disentangle training from encoding to allow for real-time encoding. We propose masking the weights of the predicted INR during training to allow for variable, higher quality compression, resulting in 1.7% improvements to both PSNR and MS-SSIM at 0.037 bpp on the UCF-101 dataset, and we increase hyper-network parameters by 0.4% for 2.5%/2.7% improvements to PSNR/MS-SSIM with equal bpp and similar speeds.