GenAI4ELab/papercli-papers huggingface dataset:
Photo Dating by Facial Age Aggregation
Authors: Jakub Paplhám; Vojtěch Franc
Venue: WACV 2026 (main)

Abstract: We introduce a novel method for Photo Dating which estimates the year a photograph was taken by leveraging information from the faces of people present in the image. To facilitate this research, we publicly release CSFD-1.6M, a new dataset containing over 1.6 million annotated faces, primarily from movie stills, with identity and birth year annotations. Uniquely, our dataset provides annotations for multiple individuals within a single image, enabling the study of multi-face information aggregation. We propose a probabilistic framework that formally combines visual evidence from modern face recognition and age estimation models, and career-based temporal priors to infer the photo capture year. Our experiments demonstrate that aggregating evidence from multiple faces consistently improves the performance and the approach significantly outperforms strong, scene-based baselines, particularly for images containing several identifiable individuals.
Test-Time Consistency in Vision Language Models
Authors: Shih-Han Chou; Shivam Chandhok; James J. Little; Leonid Sigal
Venue: WACV 2026 (main)

Abstract: Vision-Language Models (VLMs) have achieved impressive performance across a wide range of multimodal tasks, yet they often exhibit inconsistent behavior when faced with semantically equivalent inputs--undermining their reliability and robustness. Recent benchmarks, such as MM-R3, highlight that even state-of-the-art VLMs can produce divergent response across semantically equivalent inputs, despite maintaining high average accuracy. Prior work addresses this issue by modifying model architectures or conducting large-scale fine-tuning on curated datasets. In contrast, we propose a simple and effective test-time consistency framework that enhances semantic consistency without supervised re-training. Our method is entirely post-hoc, model-agnostic, and applicable to any VLM with access to its weights. Given a single test point, we enforce consistent predictions via two complementary objectives: (i) a Cross-Entropy Agreement Loss that aligns predictive distributions across semantically equivalent inputs, and (ii) a Pseudo-Label Consistency Loss that draws outputs toward a self-averaged consensus. Our method is plug-and-play, and leverages information from a single test-input itself to improve consistency. Experiments on the MM-R3 benchmark show that our framework yields substantial gains in consistency across state-of-the-art models, establishing a new direction for inference-time adaptation in VLMs.
CSGaussian: Progressive Rate-Distortion Compression and Segmentation for 3D Gaussian Splatting
Authors: Yu-Jen Tseng; Chia-Hao Kao; Jing-Zhong Chen; Alessandro Gnutti; Shao-Yuan Lo; Yen-Yu Lin; Wen-Hsiao Peng
Venue: WACV 2026 (main)

Abstract: We present the first unified framework for rate-distortion-optimized compression and segmentation of 3D Gaussian Splatting (3DGS). While 3DGS has proven effective for both real-time rendering and semantic scene understanding, prior works have largely treated these tasks independently, leaving their joint consideration unexplored. Inspired by recent advances in rate-distortion-optimized 3DGS compression, this work integrates semantic learning into the compression pipeline to support decoder-side applications--such as scene editing and manipulation--that extend beyond traditional scene reconstruction and view synthesis. Our scheme features a lightweight implicit neural representation-based hyperprior, enabling efficient entropy coding of both color and semantic attributes while avoiding costly grid-based hyperprior as seen in many prior works. To facilitate compression and segmentation, we further develop compression-guided segmentation learning, consisting of quantization-aware training to enhance feature separability and a quality-aware weighting mechanism to suppress unreliable Gaussian primitives. Extensive experiments on the LERF and 3D-OVS datasets demonstrate that our approach significantly reduces transmission cost while preserving high rendering quality and strong segmentation performance.
Conversational Image Generation: Towards Multi-Round Personalized Generation with Multi-Modal Language Models
Authors: Haochen Zhang; Animesh Sinha; Felix Juefei-Xu; Haoyu Ma; Kunpeng Li; Zhipeng Fan; Xiaoliang Dai; Tingbo Hou; Peizhao Zhang; Zecheng He
Venue: WACV 2026 (main)

Abstract: Recent advancements in diffusion models have significantly enhanced personalized image generation, enabling high-fidelity synthesis of human-subject-specific images. However, existing approaches are constrained by the inherent limitations of diffusion models, which lack conversational capabilities, and operate in a single-round setting, restricting user interaction. In this work, we propose a novel framework that integrates multi-modal large language models (MLLMs) for multi-round conversational personalization. To achieve this, we identified a performance bottleneck in the detokenizer of current MLLMs, which struggles to reconstruct fine-grained facial identity details. Thus, we enhance the detokenizer with a personalization-enhaced Diffusion Transformer (DiT). We also introduce a multi-stage instruction fine-tuning strategy to balance face preservation and prompt alignment effectively. To support multi-round generation, we implement a chat-history caching mechanism and construct the first multi-round personalization dataset from video clips. Experimental results demonstrate that our approach achieves state-of-the-art performance among MLLM-based personalization methods. To the best of our knowledge, this is the first work to enable conversational personalization, unlocking new capabilities for MLLMs in personalized image generation.
PhyEduVideo: A Benchmark for Evaluating Text-to-Video Models for Physics Education
Authors: Megha Mariam K.M; Aditya Arun; Zakaria Laskar; C.V. Jawahar
Venue: WACV 2026 (main)

Abstract: Generative AI models, particularly Text-to-Video (T2V) systems, offer a promising avenue for transforming science education by automating the creation of engaging and intuitive visual explanations. In this work, we take a first step toward evaluating their potential in physics education by introducing a dedicated benchmark for explanatory video generation. The benchmark is designed to assess how well T2V models can convey core physics concepts through visual illustrations. Each physics concept in our benchmark is decomposed into granular teaching points, with each point accompanied by a carefully crafted prompt intended for visual explanation of the teaching point. T2V models are evaluated on their ability to generate accurate videos in response to these prompts. Our aim is to systematically explore the feasibility of using T2V models to generate high-quality, curriculum-aligned educational content--paving the way toward scalable, accessible, and personalized learning experiences powered by AI. Our evaluation reveals that current models produce visually coherent videos with smooth motion and minimal flickering, yet their conceptual accuracy is less reliable. Performance in areas such as mechanics, fluids, and optics is encouraging, but models struggle with electromagnetism and thermodynamics, where abstract interactions are harder to depict. These findings underscore the gap between visual quality and conceptual correctness in educational video generation. We hope this benchmark helps the community close that gap and move toward T2V systems that can deliver accurate, curriculum-aligned physics content at scale. The benchmark and accompanying codebase are publicly available at https://github.com/meghamariamkm/PhyEduVideo.
PEaRL: Pathway-Enhanced Representation Learning for Gene and Pathway Expression Prediction from Histology
Authors: Sejuti Majumder; Saarthak Kapse; Moinak Bhattacharya; Xuan Xu; Alisa Yurovsky; Prateek Prasanna
Venue: WACV 2026 (main)

Abstract: Integrating histopathology with spatial transcriptomics (ST) provides a powerful opportunity to link tissue morphology with molecular function. Yet most existing multimodal approaches rely on a small set of highly variable genes, which limits predictive scope and overlooks the coordinated biological programs that shape tissue phenotypes. We present PEaRL (Pathway Enhanced Representation Learning), a multimodal framework that represents transcriptomics through pathway activation scores computed with ssGSEA. By encoding biologically coherent pathway signals with a transformer and aligning them with histology features via contrastive learning, PEaRL reduces dimensionality, improves interpretability, and strengthens cross-modal correspondence. Across three cancer ST datasets--breast, skin, and lymph node--PEaRL consistently outperforms state-of-the-art methods, yielding higher accuracy for both gene- and pathway-level expression prediction (up to 58.9% and 20.4% increase in Pearson correlation coefficient compared to SOTA). These results demonstrate that grounding transcriptomic representation in pathways produces more biologically faithful and interpretable multimodal models, advancing computational pathology beyond gene-level embeddings.
Automated Pore Detection from In-Situ FDM 3D Printing Video: A Comparative Evaluation of Modern Segmentation Models
Authors: Abdullah Al Ahad Khan; Md Shariful Islam; Lin Li; Lai Jiang; Noushin Ghaffari
Venue: WACV 2026 (main)

Abstract: In extrusion-based fused deposit modeling (FDM) 3D printing, porosity weakens layer adhesion and compromises the mechanical reliability of printed parts, making it one of the most critical defects. While porosity in additive manufacturing has been studied extensively, the pixel-level segmentation of pores has received little attention. To address this gap, we collected a new dataset based on in-situ video of FDM 3D printing with biofiber-reinforced thermoplastic biopolymers. After manually annotating the frames with polygon-level pore masks, we used the dataset to benchmark four widely used segmentation models YOLOv8-seg, YOLOv11-seg, Mask R-CNN, and DeepLabV3+ under a consistent training and evaluation protocol. Our results show that YOLOv11-seg achieves the highest segmentation accuracy with a mask mAP@50 of 92.9%, while YOLOv8-seg delivers a comparable accuracy of 92.6% but with the fastest throughput at nearly 60 FPS, making it particularly suited for real-time monitoring. DeepLabV3+ and Mask RCNN provide useful baselines but lag in either efficiency or stability. This work introduces the first annotated dataset and baseline comparison for segmentation of small, irregular defects in FDM, establishing a benchmark relevant both for additive manufacturing and for broader computer vision research on challenging low-contrast defect segmentation.
AD2: Analysis and Detection of Adversarial Threats in Visual Perception for End-to-End Autonomous Driving Systems
Authors: Ishan Sahu; Somnath Hazra; Somak Aditya; Soumyajit Dey
Venue: WACV 2026 (main)

Abstract: End-to-end autonomous driving systems have achieved significant progress, yet their adversarial robustness remains largely underexplored. In this work, we conduct a closed-loop evaluation of state-of-the-art autonomous driving agents under black-box adversarial threat models in CARLA. Specifically, we consider three representative attack vectors on the visual perception pipeline: (i) a physics-based blur attack induced by acoustic waves, (ii) an electromagnetic interference attack that distorts captured images, and (iii) a digital attack that adds ghost objects as carefully crafted bounded perturbations on images. Our experiments on two advanced agents, Transfuser and Interfuser, reveal severe vulnerabilities to such attacks, with driving scores dropping by up to 99% in the worst case, raising valid safety concerns. To help mitigate such threats, we further propose a lightweight Attack Detection model for Autonomous Driving systems (AD^2) based on attention mechanisms that capture spatial-temporal consistency. Comprehensive experiments across multi-camera inputs on CARLA show that our detector achieves superior detection capability and computational efficiency compared to existing approaches.
FastPose-ViT: A Vision Transformer for Real-Time Spacecraft Pose Estimation
Authors: Pierre Ancey; Andrew Price; Saqib Javed; Mathieu Salzmann
Venue: WACV 2026 (main)

Abstract: Estimating the 6-degrees-of-freedom (6DoF) pose of a spacecraft from a single image is critical for autonomous operations like in-orbit servicing and space debris removal. Existing state-of-the-art methods often rely on iterative Perspective-n-Point (PnP)-based algorithms, which are computationally intensive and ill-suited for real-time deployment on resource-constrained edge devices. To overcome these limitations, we propose FastPose-ViT, a Vision Transformer (ViT)-based architecture that directly regresses the 6DoF pose. Our approach processes cropped images from object bounding boxes and introduces a novel mathematical formalism to map these localized predictions back to the full-image scale. This formalism is derived from the principles of projective geometry and the concept of "apparent rotation", where the model predicts an apparent rotation matrix that is then corrected to find the true orientation. We demonstrate that our method outperforms other non-PnP strategies and achieves performance competitive with state-of-the-art PnP-based techniques on the SPEED dataset. Furthermore, we validate our model's suitability for real-world space missions by quantizing it and deploying it on power-constrained edge hardware. On the NVIDIA Jetson Orin Nano, our end-to-end pipeline achieves a latency of  75 ms per frame under sequential execution, and a non-blocking throughput of up to 33 FPS when stages are scheduled concurrently.
BiPO: Bidirectional Partial Occlusion Network for Text-to-Motion Synthesis
Authors: Seong-Eun Hong; SooBin Lim; JuYeong Hwang; Minwook Chang; HyeongYeop Kang
Venue: WACV 2026 (main)

Abstract: Text-to-motion generation allows language-driven animation, yet current models struggle to deliver long-range coherence and fine-grained limb coordination. A competitive system must (i) preserve temporal consistency across hundreds of frames, (ii) synchronize limb motions, and (iii) align nuanced sentences with a spectrum of plausible trajectories. We introduce BiPO, the first part-based bidirectional autoregressive network trained with a lightweight Partial Occlusion regulariser. Each limb attends to both past and future frames for anticipatory coordination, while stochastic masking weakens spurious cross-part dependencies and encourages varied solutions. On HumanML3D and KIT-ML, BiPO lowers FID by 15-30% relative to MoMask and BAMM, secures the highest human-perceived realism scores, and sets new state-of-the-art results on motion-editing tasks requiring infill from partial sequences. These findings demonstrate that bidirectional reasoning coupled with Partial Occlusion yields a length-agnostic, high-fidelity framework for expressive, language-conditioned motion synthesis.
Improvise, Adapt, Overcome -- Telescopic Adapters for Efficient Fine-tuning of Vision Language Models in Medical Imaging
Authors: Ujjwal Mishra; Vinita Shukla; Praful Hambarde; Amit Shukla
Venue: WACV 2026 (main)

Abstract: Adapting Vision Language Segmentation Models (VLSMs) to medical imaging domains requires significant computational overhead when using conventional fine-tuning approaches. Existing Parameter-Efficient Fine-Tuning (PEFT) methods apply uniform adapter dimensions across all transformer layers, leading to suboptimal parameter allocation and reduced adaptation efficiency. We introduce Telescopic Adapters, a novel PEFT framework that employs depth-aware scaling to progressively increase adapter capacity from shallow to deep transformer layers. Our method integrates lightweight bottleneck modules within CLIPSeg's vision and text encoders, with adapter dimensions dynamically scaled based on layer depth and semantic relevance. Using only 613k trainable parameters--244x fewer than end-to-end fine-tuning, Telescopic Adapters achieve superior performance across five diverse medical datasets spanning polyp segmentation, skin lesion detection, and breast ultrasound imaging. Comprehensive ablation studies demonstrate that deeper layers require substantially more adaptation capacity than shallow layers, validating our telescopic scaling hypothesis. Our approach establishes a new paradigm for efficient medical VLSM fine-tuning, enabling deployment in resource-constrained clinical environments while maintaining competitive segmentation accuracy.
How I Met Your Bias: Investigating Bias Amplification in Diffusion Models
Authors: Nathan Roos; Ekaterina Iakovleva; Ani Gjergji; Vito Paolo Pastore; Enzo Tartaglione
Venue: WACV 2026 (main)

Abstract: Diffusion-based generative models demonstrate state-of-the-art performance across various image synthesis tasks, yet their tendency to replicate and amplify dataset biases remains poorly understood. Although previous research has viewed bias amplification as an inherent characteristic of diffusion models, this work provides the first analysis of how sampling algorithms and their hyperparameters influence bias amplification. We empirically demonstrate that samplers for diffusion models - commonly optimized for sample quality and speed - have a significant and measurable effect on bias amplification. Through controlled studies with models trained on Biased MNIST, Multi-Color MNIST and BFFHQ, and with Stable Diffusion, we show that sampling hyperparameters can induce both bias reduction and amplification, even when the trained model is fixed. Source code is available at https://github.com/How-I-met-your-bias/how_i_met_your_bias.
Color Bind: Exploring Color Perception in Text-to-Image Models
Authors: Shay Shomer-Chai; Wenxuan Peng; Bharath Hariharan; Hadar Averbuch-Elor
Venue: WACV 2026 (main)

Abstract: Text-to-image generation has recently seen remarkable success, granting users with the ability to create high-quality images through the use of text. However, contemporary methods face challenges in capturing the precise semantics conveyed by complex multi-object prompts. Consequently, many works have sought to mitigate such semantic misalignments, typically via inference-time schemes that modify the attention layers of the denoising networks. However, prior work has mostly utilized coarse metrics, such as the cosine similarity between text and image CLIP embeddings, or human evaluations, which are challenging to conduct on a larger-scale. In this work, we perform a case study on colors--a fundamental attribute commonly associated with objects in text prompts, which offer a rich test bed for rigorous evaluation. Our analysis reveals that pretrained models struggle to generate images that faithfully reflect multiple color attributes--far more so than with single-color prompts--and that neither inference-time techniques nor existing editing methods reliably resolve these semantic misalignments. Accordingly, we introduce a dedicated image editing technique, mitigating the issue of multi-object semantic alignment for prompts containing multiple colors. We demonstrate that our approach significantly boosts performance over a wide range of metrics, considering images generated by various text-to-image diffusion-based techniques. Our code, benchmark and evaluation protocol is publicly available on our project webpage.
Learning Subglacial Bed Topography from Sparse Radar with Physics-Guided Residuals
Authors: Bayu Adhi Tama; Jianwu Wang; Vandana Janeja; Mostafa Cham
Venue: WACV 2026 (main)

Abstract: Accurate subglacial bed topography is essential for ice-sheet modeling, yet radar observations are sparse and uneven. We propose a physics-guided residual learning framework that predicts bed thickness residuals over a BedMachine prior and reconstructs bed from the observed surface. A DeepLabV3+ decoder over a standard encoder (e.g., ResNet-50) is trained with lightweight physics and data terms: multi-scale mass conservation, flow-aligned total variation, Laplacian damping, non-negativity of thickness, a ramped prior-consistency term, and a masked Huber fit to radar picks modulated by a confidence map. To measure real-world generalization, we adopt leakage-safe block-wise hold-outs (vertical/horizontal) with safety buffers and report metrics only on held-out cores. Across two Greenland sub-regions, our approach achieves strong test-core accuracy (RMSE 3.05-10.54\,m; R^2=0.993-0.999) and high structural fidelity (SSIM \ge 0.998, PSNR up to 52.9\,dB), outperforming U-Net, Attention U-Net, FPN, and a plain CNN. The residual-over-prior design, combined with physics, yields spatially coherent, physically plausible beds suitable for operational mapping under domain shift.
From Lightweight CNNs to SpikeNets: Benchmarking Accuracy-Energy Tradeoffs with Pruned Spiking SqueezeNet
Authors: Radib Bin Kabir; Tawsif Tashwar Dipto; Mehedi Ahamed; Sabbir Ahmed; Md Hasanul Kabir
Venue: WACV 2026 (main)

Abstract: Spiking Neural Networks (SNNs) are increasingly studied as energy-efficient alternatives to Convolutional Neural Networks (CNNs), particularly for edge intelligence. However, prior work has largely emphasized large-scale models, leaving the design and evaluation of lightweight CNN-to-SNN pipelines underexplored. In this paper, we present the first systematic benchmark of lightweight SNNs obtained by converting compact CNN architectures into spiking networks, where activations are modeled with Leaky-Integrate-and-Fire (LIF) neurons and trained using surrogate gradient descent under a unified setup. We construct spiking variants of ShuffleNet, SqueezeNet, MnasNet, and MixNet, and evaluate them on CIFAR-10, CIFAR-100, and TinyImageNet, measuring accuracy, F1-score, parameter count, computational complexity, and energy consumption. Our results show that SNNs can achieve up to 15.7x higher energy efficiency than their CNN counterparts while retaining competitive accuracy. Among these, the SNN variant of SqueezeNet consistently outperforms other lightweight SNNs. To further optimize this model, we apply a structured pruning strategy that removes entire redundant fire modules, yielding a pruned architecture, SNN SqueezeNet-P. This pruned model improves CIFAR-10 accuracy by 6% and reduces parameters by 19% compared to the original SNN SqueezeNet. Crucially, it narrows the gap with CNN SqueezeNet, achieving nearly the same accuracy (only 1% lower) but with an 88.1% reduction in energy consumption due to sparse spike-driven computations. Together, these findings establish lightweight SNNs as practical, low-power alternatives for edge deployment, highlighting a viable path toward deploying high-performance, low-power intelligence on the edge. Code available at GitHub: Pruned-Spiking-SqueezeNet