GenAI4ELab/papercli-papers huggingface dataset:
Generalized Category Discovery for LiDAR Semantic Segmentation
Authors: Minseok Kim; Jiyong Boo; Kuk-Jin Yoon
Venue: WACV 2026 (main)

Abstract: Novel Category Discovery (NCD) methods for LiDAR Semantic Segmentation (LSS) assume that labeled and unlabeled points coexist in every scan and that all unlabeled points belong solely to novel categories. We formalize this into a more practical task, Generalized Category Discovery (GCD) for LSS, in which the labeled and unlabeled subsets are disjoint, and the unlabeled data contain a mixture of known and novel categories. Existing 2D GCD methods fail under this setting, struggling to distinguish the two groups in sparse, imbalanced LiDAR data. To address this limitation, we present a unified framework that (i) employs a learnable adaptive threshold to obtain point-wise anomaly scores to capture candidates, (ii) refines these candidates through a clustering-based filtering mechanism, and (iii) stabilizes training with a novel-feature queue that supplies reliable novel features even when a scene lacks novel categories. This explicit modeling of novel categories preserves segmentation quality for known classes while markedly improving discovery performance--a direction not explored in prior GCD or NCD methods. Extensive experiments on SemanticKITTI and nuScenes demonstrate that our approach consistently surpasses adapted baselines, establishing a strong benchmark for future work in open-world LSS.
GrowTAS: Progressive Expansion from Small to Large Subnets for Efficient ViT Architecture Search
Authors: Hyunju Lee; Youngmin Oh; Jeimin Jeon; Donghyeon Baek; Bumsub Ham
Venue: WACV 2026 (main)

Abstract: Transformer architecture search (TAS) aims to automatically discover efficient vision transformers (ViTs), reducing the need for manual design. Existing TAS methods typically train an over-parameterized network (i.e., a supernet) that encompasses all candidate architectures (i.e., subnets). However, all subnets share the same set of weights, which leads to interference that degrades the smaller subnets severely. We have found that well-trained small subnets can serve as a good foundation for training larger ones. Motivated by this, we propose a progressive training framework, dubbed GrowTAS, that begins with training small subnets and incorporate larger ones gradually. This enables reducing the interference and stabilizing a training process. We also introduce GrowTAS+ that fine-tunes a subset of weights only to further enhance the performance of large subnets. Extensive experiments on ImageNet and several transfer learning benchmarks, including CIFAR-10/100, Flowers, CARS, and INAT-19, demonstrate the effectiveness of our approach over current TAS methods.
NeuroBridge: Few-Shot Cross-Modal Neuron Re-identification via Dual-Channel Deep Metric Learning
Authors: Wenwei Li; Mingwei Liao; Lingyi Cai; Anan Li
Venue: WACV 2026 (main)

Abstract: Associating the in-vivo function of neurons with their ex-vivo anatomical structure is a central challenge in neuroscience. However, this field is constrained by a critical bottleneck: the extreme difficulty of acquiring paired cross-modal data, leading to a persistent scarcity of large-scale datasets. This inherent limitation frames the re-identification of the same neuron as a formidable few-shot, fine-grained visual recognition task. To address this challenge, we propose a novel deep metric learning framework designed to learn modality-invariant feature representations for single neurons under these data-scarce conditions. The core of this framework is a dual-channel network architecture that explicitly disentangles and fuses the local morphological information of the neuron's soma with the global topological context of the dendritic arbor, thereby capturing a more robust neural signature. To maximize data efficiency, we integrate a Circle Loss objective with a Multi-Similarity hard-sample mining strategy, which effectively optimizes the embedding space for better class separation. On a cross-modal neuron dataset that realistically reflects experimental data scarcity, our method demonstrates excellent performance, achieving a Recall of 77.4% and a Specificity of 90.1% on the test set. Extensive ablation studies and comparative analyses validate the effectiveness of our proposed method, establishing a new strong baseline for this critical yet data-limited biomedical application. To foster future research in this field, we will release our code, dataset, and pre-trained models.
IPTQ-ViT: Post-Training Quantization of Non-linear Functions for Integer-only Vision Transformers
Authors: Gihwan Kim; Jemin Lee; Hyungshin Kim
Venue: WACV 2026 (main)

Abstract: Quantization-Aware Training (QAT) for vision transformers relies on expensive retraining to recover accuracy loss in non-linear layer quantization, which limits their use in resource-constrained environments. In contrast, existing Post-Training Quantization (PTQ) methods either partially quantize non-linear functions or adjust activation distributions to maintain accuracy but fail to achieve fully integer-only inference. In this paper, we introduce IPTQ-ViT, a PTQ framework for fully integer-only vision transformers without retraining. We present approximation functions: a polynomial-based GELU optimized for vision data and a bit-shifting-based Softmax designed to improve approximation accuracy in PTQ. In addition, we propose a unified metric integrating quantization sensitivity, perturbation, and computational complexity to select the optimal approximation function per activation layer. IPTQ-ViT consistently outperforms previous PTQ methods, achieving up to 6.44%p (avg. 1.78%p) top-1 accuracy improvement for image classification and 1.0 mAP for object detection under W8A8 and W4A8. IPTQ-ViT achieves accuracy and latency comparable to integer-only QAT methods.
DynaGSLAM: Real-Time Gaussian-Splatting SLAM for Online Rendering, Tracking, Motion Predictions of Moving Objects in Dynamic Scenes
Authors: Runfa Blark Li; Mahdi Shaghaghi; Keito Suzuki; Xinshuang Liu; Varun Moparthi; Bang Du; Walker Curtis; Martin Renschler; Ki Myung Brian Lee; Nikolay Atanasov; Truong Nguyen
Venue: WACV 2026 (main)

Abstract: Simultaneous Localization and Mapping (SLAM) is one of the most important environment-perception and navigation algorithms for computer vision, robotics, and autonomous cars/drones. Hence, high quality and fast mapping becomes a fundamental problem. With the advent of 3D GaussianSplatting (3DGS) as an explicit representation with excellent rendering quality and speed, state-of-the-art (SOTA) works introduce GS to SLAM. Compared to classical pointcloud-SLAM, GS-SLAM generates photometric information by learning from input camera views and synthesize unseen views with high-quality textures. However, these GS-SLAM fail when moving objects occupy the scene that violate the static assumption of bundle adjustment. The failed updates of moving GS affect the static GS and contaminates the full map over long frames. Although some efforts have been made by concurrent works to consider moving objects for GS-SLAM, they simply detect and remove the moving regions from GS rendering ("anti" dynamic GS-SLAM), where only the static background could benefit from GS. To this end, we propose the first real-time GS-SLAM, "DynaGSLAM", that achieves high-quality online GS rendering, tracking, motion predictions of moving objects in dynamic scenes while jointly estimating accurate ego motion. Our DynaGSLAM outperforms SOTA static & "Anti" dynamic GS-SLAM on three dynamic real datasets, while keeping speed and memory efficiency in practice.
Denoise, Divide, Distill, and Predict (D3P): Towards Forecasting Long-horizon Real-world Anomaly from Normalcy
Authors: Quentin Mérilleau; Snehashis Majhi; Antitza Dantcheva; Quan Kong; Lorenzo Garattoni; Gianpiero Francesca; Francois Bremond
Venue: WACV 2026 (main)

Abstract: Forecasting abnormal human behavior (AHB) in unconstrained real-world environments is critical for enabling proactive safety interventions. Unlike short-term anomaly detection, long-horizon forecasting offers a vital reaction window but remains underexplored due to three core challenges: (i) noisy, complex human-agent interactions; (ii) weak temporal coupling between normal observations and distant anomalies; and (iii) data scarcity limiting the scalability of autoregressive models. To address these, we propose \mathcal D ^3\mathcal P  (Denoise, Divide, Distill, and Predict), a novel encoder-decoder framework that bridges denoised pasts with distilled autoregressive futures. Our Differential Past Encoder (DiPE) disentangles scene-level and object-level dynamics via differential attention, suppressing irrelevant interactions and enhancing discriminative cues. The Distilled Future Auto-Regressive Decoder (D-FAD) adopts a divide-and-conquer strategy, segmenting future queries into temporal chunks for sequential prediction, while leveraging distillation to balance robustness and latency. We validate our approach on the AHB-F benchmark, the only dataset dedicated to abnormal behavior forecasting, and further integrate D-FAD with several state-of-the-art methods. In all cases, our framework consistently outperforms prior work in both forecasting accuracy and computational efficiency.
Zero-Shot Domain Generalisation via Prompt-Driven Feature Refinement
Authors: Tingrui Qiao; Di Zhao; Caroline Walker; Chris Cunningham; Yun Sing Koh
Venue: WACV 2026 (main)

Abstract: Domain generalisation aims to develop models that generalise from source domains to unseen target domains. However, most existing methods assume access to source domain data and require additional training, which may not always be practical. We focus on a more flexible and broadly applicable setting, zero-shot domain generalisation, where models generalise without access to source data, target data, or any additional training. In this work, we propose Prefer (prompt-driven feature refinement), a simple and effective approach which enhances the zero-shot domain generalisation ability of vision-language foundation models. Prefer generates a diverse set of textual prompts for each class by imagining domain-specific variations (e.g., "a painting of a cat under a golden sunset with thick brush strokes"), and uses them to probe the model. We evaluate how reliably each feature channel represents a class across domains by measuring two quantities: (1) how strongly the channel aligns with the original class prompt (e.g., "a photo of a cat") across the generated domain-specific prompts, and (2) how stable the channel remains across those prompts, quantified by its variance. Channels that exhibit both high alignment and low variability are selected at inference time to improve class prediction under domain shift. Without any model updates or external data, Prefer achieves consistent improvements across domain generalisation benchmarks, outperforming existing state-of-the-art methods. The source code is available at https://anonymous.4open.science/r/WACV26-Prefer.
Generalizing Sports Feedback Generation by Watching Competitions and Reading Books: A Rock Climbing Case Study
Authors: Arushi Rai; Adriana Kovashka
Venue: WACV 2026 (main)

Abstract: While there is rapid progress in video-LLMs with advanced reasoning capabilities, prior work shows that these models struggle on the challenging task of sports feedback generation and require expensive and difficult-to-collect finetuning feedback data for each sport. This limitation is evident from the poor generalization to sports unseen during finetuning. Furthermore, traditional text generation evaluation metrics (e.g., BLEU-4, METEOR, ROUGE-L, BERTScore), originally developed for machine translation and summarization, fail to capture the unique aspects of sports feedback quality. To address the first problem, using rock climbing as our case study, we propose using auxiliary freelyavailable web data from the target domain, such as competition videos and coaching manuals, in addition to existing sports feedback from a disjoint, source domain to improve sports feedback generation performance on the target domain. To improve evaluation, we propose two evaluation metrics: (1) specificity and (2) actionability. Together, our approach enables more meaningful and practical generation of sports feedback under limited annotations.
ChameleonTuner: Automatic ISP Color Tuning in Subjective Scenarios
Authors: Zijie Tan; Yuxin Yue; Bahador Rashidi
Venue: WACV 2026 (main)

Abstract: Image color enhancement is a key component of ISP pipelines, with 3D LUTs widely adopted for nonlinear color transformations and color refinement. Existing learning-based approaches attempt to learn a global mapping from paired pixel-aligned training samples, but this offers limited control and flexibility for practitioners and restricts adaptation to specific images or scenarios. Moreover, the assumption of pixel-aligned supervision requires costly manual retouching to generate ground-truth references. A practical alternative is to use DSLR images as high-quality references, but the inevitable FoV and PoV variations make pixel-level 3D LUT calibration unreliable. We propose ChameleonTuner, a novel framework that establishes region-level color correspondences to handle FoV/PoV discrepancies. Unlike prior learning-based 3D LUT methods, the proposed framework defines a dedicated search space for LUT tuning and employs multi-objective search to achieve controllable and interpretable optimization. Extensive experiments across multiple perceptual color metrics demonstrate that our method is effective in subjective scenarios (e.g., DPED) with geometric misalignments, while remaining competitive on standard pixel-aligned benchmarks (e.g., MIT-Adobe FiveK). Our code is available at https://github.com/ZjTan4/ ChameleonTuner.git.
ControlVP: Interactive Geometric Refinement of AI-Generated Images with Consistent Vanishing Points
Authors: Ryota Okumura; Kaede Shiohara; Toshihiko Yamasaki
Venue: WACV 2026 (main)

Abstract: Recent text-to-image models, such as Stable Diffusion, have achieved impressive visual quality, yet they often suffer from geometric inconsistencies that undermine the structural realism of generated scenes. One prominent issue is vanishing point inconsistency, where projections of parallel lines fail to converge correctly in 2D space. This leads to structurally implausible geometry that degrades spatial realism, especially in architectural scenes. We propose ControlVP, a user-guided framework for correcting vanishing point inconsistencies in generated images. Our approach extends a pre-trained diffusion model by incorporating structural guidance derived from building contours. We also introduce geometric constraints that explicitly encourage alignment between image edges and perspective cues. Our method enhances global geometric consistency while maintaining visual fidelity comparable to the baselines. This capability is particularly valuable for applications that require accurate spatial structure, such as image-to-3D reconstruction. The dataset and source code are available at https://github.com/RyotaOkumura/ControlVP.
UnderWater SLAM with Laser-light sectioning method using ST-GAT
Authors: Heyang Gao; Kazuto Ichimaru; Takafumi Iwaguchi; Hiroshi Kawasaki
Venue: WACV 2026 (main)

Abstract: Multi-line laser ID assignment is crucial for underwater 3D reconstruction but fails when lines fragment. We reformulate this as a graph-based sequence labeling task and propose a novel two-stage hierarchical framework using Spatio-Temporal Graph Attention Networks (ST-GAT). Our method first reasons over a spatio-temporal graph of laser endpoints and intersections to handle local fragmentation, then elevates this to a global segment-level optimization with trajectory-constrained Viterbi decoding to ensure temporal consistency. This GNN-based approach eliminates the reliance on complete epipolar geometry. Experiments on real underwater datasets demonstrate superior reconstruction completeness and temporal stability, especially in challenging environments where traditional methods fail.
Non-Contact Blood Pressure Estimation from Face Videos via Physiology-Aware Contrastive Learning
Authors: JaeHyuk Son; Young-Seok Choi
Venue: WACV 2026 (main)

Abstract: Remote photoplethysmography (rPPG) has emerged as a promising foundation for camera-based blood pressure (BP) monitoring, but practical deployment remains limited by strong domain gaps across datasets, scarce and imbalanced labels, and the difficulty of preserving waveform morphology. We present a dual-branch framework that combines raw rPPG segments with handcrafted waveform features and introduces an augmentation-free contrastive pre-training strategy. The approach learns subject-invariant, domain-agnostic embeddings from unlabeled facial videos and aligns them with physiology-inspired descriptors in a shared latent space, while a distribution-aware loss reduces label imbalance. This design integrates the strengths of data-driven representations and handcrafted physiological cues, producing morphology-sensitive features that generalize across acquisition domains. Experiments on multiple datasets demonstrate that the proposed method achieves competitive accuracy under both controlled and in-the-wild conditions, improves cross-dataset transfer, and maintains robust performance when labeled data are limited. Beyond accuracy, the framework emphasizes interpretability by grounding learned embeddings in physiological features, and its reliance on unlabeled videos makes it highly scalable to larger populations without requiring extensive manual annotation. Taken together, these results suggest that bridging representation learning with physiological modeling offers a practical and scalable path toward reliable, non-contact BP monitoring in diverse real-world environments.
Shift-Equivariant Complex-Valued Convolutional Neural Networks
Authors: Quentin Gabot; Teck-Yian Lim; Jeremy Fix; Joana Frontera-Pons; Chengfang Ren; Jean-Philippe Ovarlez
Venue: WACV 2026 (main)

Abstract: Convolutional neural networks have shown remarkable performance in recent years on various computer vision problems. However, the traditional convolutional neural network architecture lacks a critical property: shift equivariance and invariance, broken by downsampling and upsampling operations. Although data augmentation techniques can help the model learn the latter property empirically, a consistent and systemic way to achieve this goal is by designing downsampling and upsampling layers that theoretically guarantee these properties by construction. Adaptive Polyphase Sampling (APS)introduced the cornerstone for shift invariance, later extended to shift equivariance with Learnable Polyphase up/downsampling (LPS) applied to real-valued neural networks. In this paper, we extend the work on LPSto complex-valued neural networks both from a theoretical perspective and with a novel building block of a projection layer from \mathbb C  to \mathbb R  before the Gumbel Softmax. We finally evaluate this extension on several computer vision problems for either the invariance property with classification tasks or the equivariance with both reconstruction and semantic segmentation problems using polarimetric synthetic aperture images.
HiGlassRM: Learning to Remove High-prescription Glasses via Synthetic Dataset Generation
Authors: Sebin Lee; Heewon Kim
Venue: WACV 2026 (main)

Abstract: Existing eyeglass removal methods can handle frames and shadows but fail to correct lens-induced geometric distortions, as public datasets lack the necessary supervision. To address this, we introduce the HiGlass Dataset, the first large-scale synthetic dataset providing explicit flow-based supervision for refractive warping. We also propose HiGlassRM, a novel pipeline whose core is a network that explicitly estimates a displacement flowmap to de-warp distorted facial geometry.Experiments on both synthetic and real images show that this flowmap-centric approach, trained on our data, significantly improves identity preservation and perceptual quality over existing methods. Our work demonstrates that explicitly modeling and correcting geometric distortion via flowmap estimation, enabled by targeted supervision, is key to faithful eyeglass removal.
Neural Geometry Image-Based Representations with Optimal Transport (OT)
Authors: Xiang Gao; Yuanpeng Liu; Jiazhi Li; Xinmu Wang; Minghao Guo; Yu Guo; Xiyun Song; Heather Yu; Zhiqiang Lao; Xianfeng David Gu
Venue: WACV 2026 (main)

Abstract: Neural representations for 3D meshes are emerging as an effective solution for compact storage and efficient processing. Existing methods often rely on neural overfitting, where a coarse mesh is stored and progressively refined through multiple decoder networks. While this can restore high-quality surfaces, it is computationally expensive due to successive decoding passes and the irregular structure of mesh data. In contrast, images have a regular structure that enables powerful super-resolution and restoration frameworks, but applying these advantages to meshes is difficult because their irregular connectivity demands complex encoder-decoder architectures. Our key insight is that a geometry image-based representation transforms irregular meshes into a regular image grid, making efficient image-based neural processing directly applicable. Building on this idea, we introduce our neural geometry image-based representation, which is decoder-free, storage-efficient, and naturally suited for neural processing. It stores a low-resolution geometry-image mipmap of the surface, from which high-quality meshes are restored in a single forward pass. To construct geometry images, we leverage Optimal Transport (OT), which resolves oversampling in flat regions and undersampling in feature-rich regions, and enables continuous levels of detail (LoD) through geometry-image mipmapping. Experimental results demonstrate state-of-the-art storage efficiency and restoration accuracy, measured by compression ratio (CR), Chamfer distance (CD), and Hausdorff distance (HD).
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships
Authors: Futa Waseda; Antonio Tejero-de-Pablos; Isao Echizen
Venue: WACV 2026 (main)

Abstract: Pre-trained vision-language (VL) models are highly vulnerable to adversarial attacks. However, existing defense methods primarily focus on image classification, overlooking two key aspects of VL tasks: multimodal attacks, where both image and text can be perturbed, and the one-to-many relationship of images and texts, where a single image can correspond to multiple textual descriptions and vice versa (1:N and N:1). This work is the first to explore defense strategies against multimodal attacks in VL tasks, whereas prior VL defense methods focus on vision robustness. We propose multimodal adversarial training (MAT), which incorporates adversarial perturbations in both image and text modalities during training, significantly outperforming existing unimodal defenses. Furthermore, we discover that MAT is limited by deterministic one-to-one (1:1) image-text pairs in VL training data. To address this, we conduct a comprehensive study on leveraging one-to-many relationships to enhance robustness, investigating diverse augmentation techniques. Our analysis shows that, for a more effective defense, augmented image-text pairs should be well-aligned, diverse, yet avoid distribution shift---conditions overlooked by prior research. This work pioneers defense strategies against multimodal attacks, providing insights for building robust VLMs from both optimization and data perspectives. Our code is publicly available at https://github.com/CyberAgentAI/multimodal-adversarial-training.