GenAI4ELab/papercli-papers huggingface dataset:
Understanding the Visual Projection Space of Multimodal LLMs
Authors: Sungheon Jeong; Yoojeong Song; Hyungjoon Kim
Venue: WACV 2026 (main)

Abstract: What role does a single vision token play inside a multimodal large language model (MLLM)? Despite recent successes, most MLLMs adopt a simple design: a projected visual feature z = P(f_x) prepended to the text sequence. Yet it remains unclear whether this vector merely provides context or actively steers generation. We propose a geometric probing framework that analyzes latent-token alignment, intrinsic dimensionality, and perturbation sensitivity. Across four datasets and three representative MLLMs (LLaVA, BLIP-2, Kosmos-2), we find clear operating regimes: BLIP-2 enforces rigid low-rank compression with strong alignment but near-zero sensitivity, LLaVA exhibits flexible high-dimensional mappings with high responsiveness, and Kosmos-2 balances between them. These signatures correlate with downstream behavior--SQA correctness and VQAv2 hallucination severity--showing that reduced or excessive sensitivity predicts unreliable grounding. Our results highlight geometry as a diagnostic lens for vision-language coupling and offer actionable guidance for projection design, alignment objectives, and user-steerable multimodal generation.
WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields
Authors: Sadra Safadoust; Fabio Tosi; Fatma Güney; Matteo Poggi
Venue: WACV 2026 (main)

Abstract: We introduce WarpRF, a training-free general-purpose framework for quantifying the uncertainty of radiance fields. Built upon the assumption that photometric and geometric consistency should hold among images rendered by an accurate model, WarpRF quantifies its underlying uncertainty from an unseen point of view by leveraging backward warping across viewpoints, projecting reliable renderings to the unseen viewpoint and measuring the consistency with images rendered there. WarpRF is simple and inexpensive, does not require any training, and can be applied to any radiance field implementation for free. WarpRF excels at both uncertainty quantification and downstream tasks, e.g., active view selection and active mapping, outperforming any existing method tailored to specific frameworks.
FAST-EQA: Efficient Embodied Question Answering with Global and Local Region Relevancy
Authors: Haochen Zhang; Nirav Savaliya; Faizan Siddiqui; Enna Sachdeva
Venue: WACV 2026 (main)

Abstract: Embodied Question Answering (EQA) combines visual scene understanding, goal-directed exploration, spatial and temporal reasoning under partial observability. A central challenge is to confine physical search to question-relevant subspaces while maintaining a compact, actionable memory of observations. Furthermore, for real-world deployment, fast inference time during exploration is crucial. We introduce FAST-EQA, a question-conditioned framework that (i) identifies likely visual targets, (ii) scores global regions of interest to guide navigation, and (iii) employs Chain-of-Thought (CoT) reasoning over visual memory to answer confidently. FAST-EQA maintains a bounded scene memory that stores a fixed-capacity set of region-target hypotheses and updates them online, enabling robust handling of both single- and multi-target questions without unbounded growth. To expand coverage efficiently, a global exploration policy treats narrow openings and doors as high-value frontiers, complementing local target seeking with minimal computation. Together, these components focus the agent's attention, improves scene coverage, and improve answer reliability while running substantially faster than prior approaches. On HMEQA and EXPRESS-Bench, FAST-EQA achieves state-of-the-art performance, while performing competitively on OpenEQA and MT-HM3D.
Flood-LDM: Generalizable Latent Diffusion Models for rapid and accurate zero-shot High-Resolution Flood Mapping
Authors: Sun Han Neo; Sachith Seneviratne; Herath Mudiyanselage Viraj Vidura Herath; Abhishek Saha; Sanka Rasnayaka; Lucy Amanda Marshall
Venue: WACV 2026 (main)

Abstract: Flood prediction is critical for emergency planning and response to mitigate human and economic losses. Traditional physics-based hydrodynamic models generate high-resolution flood maps using numerical methods requiring fine-grid discretization; which are computationally intensive and impractical for real-time large-scale applications. While recent studies have applied convolutional neural networks for flood map super-resolution with good accuracy and speed, they suffer from limited generalizability to unseen areas. In this paper, we propose a novel approach that leverages latent diffusion models to perform super-resolution on coarse-grid flood maps, with the objective of achieving the accuracy of fine-grid flood maps while significantly reducing inference time. Experimental results demonstrate that latent diffusion models substantially decrease the computational time required to produce high-fidelity flood maps without compromising on accuracy, enabling their use in real-time flood risk management. Moreover, diffusion models exhibit superior generalizability across different physical locations, with transfer learning further accelerating adaptation to new geographic regions. Our approach also incorporates physics-informed inputs, addressing the common limitation of black-box behavior in machine learning, thereby enhancing interpretability. Code is available at https://github.com/neosunhan/flood-diff.
Fused Similarity Measure Based Alignment with Dual-Scale Adaptive Selection for Weakly Supervised Video Anomaly Detection
Authors: Yue-Gao Lu; Hong-Jie Xing; Chun-Guo Li
Venue: WACV 2026 (main)

Abstract: In most multi-modal weakly supervised video anomaly detection (WSVAD) methods, cross-modal alignment relies on cosine similarity measure, which captures only directional consistency but neglects feature magnitude information. Moreover, the existing multi-instance learning frameworks usually adopt the Top-k selection strategy, which is difficult to adapt to the anomalous proportions of different videos, resulting in missed detection of anomalous segments and introduction of label noise. To address these problems, an alignment and selection enhanced WSVAD (ASE-WSVAD) method is proposed. ASE-WSVAD combines cross-modal alignment method based on the fused similarity measure (CA-FSM) with dual-scale adaptive selection (DSAS) to improve semantic consistency and detection performance. Specifically, visual-textual alignment is implemented by a fusion of complementary similarity measures (i.e., cosine and Euclidean distance similarity measures) so that the alignment objective jointly leverages both direction and magnitude. DSAS combines local instance-level selection (LILS) with global batch-aware selection (GBAS) to efficiently detect anomalous segments and handle videos with varying proportions of anomalies. Experimental results demonstrate that ASE-WSVAD achieves the state-of-the-art performance with AUC value of 89.00% and AP value of 85.44% on UCF-Crime and XD-Violence, respectively.
FARF-Net: Frequency-guided Adaptive Receptive Field Network for Edge-enhanced Polyp Segmentation
Authors: Xue Li; Aiwen Jiang; Hongqian Yu; Yang Xiao
Venue: WACV 2026 (main)

Abstract: Accurate segmentation of colorectal polyps plays a vital role in the early diagnosis and prevention of colorectal cancer. Despite notable progress, existing methods struggle with limited region adaptability due to fixed receptive fields, lack explicit boundary modeling, and are prone to interference from background noise, leading to suboptimal segmentation results. To address these issues, we propose FARF-Net, a novel edge-aware segmentation framework that leverages frequency-domain adaptive receptive fields. Built upon the Pyramid Vision Transformer v2 backbone, FARF-Net introduces three tailored components. Specifically, EdgeKAN module applies Kolmogorov-Arnold Networks for channel-wise nonlinear modeling, enhancing local edge semantics and boundary detail representation. Adaptive Receptive Field module adjusts spatial receptive fields based on localized frequency energy, boosting sensitivity to high-frequency boundaries. Frequency-Guided Dual-Supervision Decoder integrates high-frequency structural features and boundary priors to refine edge predictions and suppress irrelevant high-frequency background noise. Extensive experiments on five public polyp segmentation benchmarks demonstrate that FARF-Net consistently surpasses state-of-the-art methods. Notably, it achieves superior boundary reconstruction and robustness in challenging cases such as blurred contours and small polyps.
PerVL-Bench: Benchmarking Multimodal Personalization for Large Vision-Language Models
Authors: Minsung Kim
Venue: WACV 2026 (main)

Abstract: In recent years, personalization, which utilizes user-specific data to generate tailored responses, has been increasingly adopted in user-centric domains. However, while Large Language Models (LLMs) are actively researched, the exploration of the personalization capabilities of Large Vision-Language Models (LVLMs) remains limited. To systematically evaluate the personalization ability of LVLMs, we introduce PerVL-Bench, a synthetic benchmark specifically designed for this purpose. PerVL-Bench incorporates user-specific data, including multiple images and long text information, and provides two types of QA pairs. Furthermore, we use PerVL-Bench to comprehensively evaluate the essential capabilities for personalization in current state-of-the-art LVLMs. Through this evaluation, we reveal the limitations of current models in multimodal personalization and provide insights for the development of personalized LVLMs. We publicly release PerVL-Bench and the accompanying experimental code to advance future research: https://github.com/MSungK/PerVL-Bench
OSEG: Improving Diffusion sampling through Orthogonal Smoothed Energy Guidance
Authors: Masud An Nur Islam Fahim; Nazmus Saqib; Joon-Min Gil
Venue: WACV 2026 (main)

Abstract: Pre-trained text-to-image (T2I) diffusion models produce high-quality images from random noise guided by textual prompts, but classifier-free guidance (CFG) often yields suboptimal samples. Retraining diffusion models can enhance performance, though the associated computational cost is often prohibitive. To address this, researchers have proposed modifying the CFG step through perturbation techniques for cost-effective sampling improvements. For instance, Smoothed Energy Guidance (SEG) enhances image quality by reducing the curvature of the energy landscape in self-attention layers. However, SEG's indiscriminate curvature reduction can suppress fine details and cause prompt misalignment. We introduce Orthogonal Smoothed Energy Guidance (OSEG), which selectively smooths the orthogonal components of token embeddings to preserve critical details while maintaining prompt alignment. Theoretical analysis justifies OSEG's effectiveness, supported by empirical comparisons with recent methods. By plugging into text-to-video (T2V) models, OSEG consistently improves visual and temporal coherence. Finally, we present extensive metric comparisons to demonstrate the efficiency of OSEG for both image and video domains. Project homepage: https://osegguidance. github.io/
Detecting Social Engagement of Elderly From Lifelog Image-streams to Identify Effective Cues for Autobiographic Recall
Authors: Vengateswaran Subramaniam; Vigneshwaran Subbaraju; Debaditya Roy; Pramath Krishna; Thivya Kandappu; Qianli Xu
Venue: WACV 2026 (main)

Abstract: Lifelog images captured automatically by wearable cameras serve as effective cues that induce Autobiographic Memory Recall (AMR) of social interactions. This is very useful for personalized memory interventions. However, manual selection of images for such therapy imposes significant load on the caregivers who need to browse through a voluminous collection of images. To reduce this load, automated tools that identify moments involving significant engagement of the camera wearer in social interactions are needed. To achieve this, we re-annotate images extracted from public lifelog datasets for the presence of non-verbal social signals and the perceived engagement of the life-logger during interactions. We use this data to develop models and explore how social signals and the detected intensity of social engagement are helpful for predicting AMR. We show that understanding visual social engagement can enhance AMR prediction, demonstrating the potential of the models in reducing caregivers' effort.
Text Slider: Efficient and Plug-and-Play Continuous Concept Control for Image/Video Synthesis via LoRA Adapters
Authors: Pin-Yen Chiu; I-Sheng Fang; Jun-Cheng Chen
Venue: WACV 2026 (main)

Abstract: Recent advances in diffusion models have significantly improved image and video synthesis. In addition, several concept control methods have been proposed to enable fine-grained, continuous, and flexible control over free-form text prompts. However, these methods not only require intensive training time and GPU memory usage to learn the sliders or embeddings but also need to be retrained for different diffusion backbones, limiting their scalability and adaptability. To address these limitations, we introduce Text Slider, a lightweight, efficient and plug-and-play framework that identifies low-rank directions within a pre-trained text encoder, enabling continuous control of visual concepts while significantly reducing training time, GPU memory consumption, and the number of trainable parameters. Furthermore, Text Slider supports multi-concept composition and continuous control, enabling fine-grained and flexible manipulation in both image and video synthesis. We show that Text Slider enables smooth and continuous modulation of specific attributes while preserving the original spatial layout and structure of the input. Text Slider achieves significantly better efficiency: 5xfaster training than Concept Slider and 47xfaster than Attribute Control, while reducing GPU memory usage by nearly 2xand 4x, respectively.
Motion-Aware Graph Fusion Network for 3D Human Pose Estimation
Authors: Yen Pham; Xiaohui Yuan; Chengyuan Zhuang
Venue: WACV 2026 (main)

Abstract: Recent state-of-the-art (SOTA) methods in 3D human pose estimation (HPE) typically prioritize lifting 2D pose coordinates to 3D but tend to underemphasize the importance of generalizing under real-world conditions with noisy 2D inputs from off-the-shelf 2D detector. In this paper, we introduce Graph Attention Fusion Network (GAtFuN), a novel motion-aware framework that integrates our spatial and temporal graph attention mechanisms to explicitly model joint velocities and motion transformations, resulting in more stable and coherent 3D pose predictions despite being trained with the same dataset pipeline as other SOTA methods. GAtFuN achieves a 7.8% improvement in MPJPE over the current SOTA on the Human3.6M dataset and a 1.9% improvement on the MPI-INF-3DHP dataset, while demonstrating more robust performance on the 3DPW dataset in the wild.
Learning Spatio-temporal Feature Representations for Video-based Gaze Estimation
Authors: Alexandre Personnic; Mihai Bace
Venue: WACV 2026 (main)

Abstract: Video-based gaze estimation methods aim to capture the inherently temporal dynamics of human eye gaze from multiple image frames. However, since models must capture both spatial and temporal relationships, performance is limited by the feature representations within a frame but also between multiple frames.We propose Spatio-Temporal Gaze Network (ST-Gaze), a model that combines an existing CNN backbone with dedicated channel attention and self-attention modules to fuse eye and face features optimally. The fused features are then treated as a spatial sequence, allowing for the capture of an intra-frame context, which is then propagated through time to model inter-frame dynamics. We evaluated our method on the EVE dataset and show that ST-Gaze achieves state-of-the-art performance both with and without person-specific adaptation. Additionally, our ablation study provides further insights into the model performance, showing that preserving and modelling intra-frame spatial context with our spatio-temporal recurrence is fundamentally superior to premature spatial pooling. As such, our results pave the way towards robust video-based gaze estimation.
ProtoGMVAE: A Variational Auto-Encoder with True Gaussian Mixture Prior for Prototypical-based Self-Explainability
Authors: Martin Blanchard; Christophe Ducottet; Damien Muselet; Olivier Delézay
Venue: WACV 2026 (main)

Abstract: Recently, significant efforts were made towards Variational Autoencoder (VAE) -based prototypical Self Explainable Models (SEM) for image classification. The princi-ple is to learn class-specific prototypes that can be projected back into the image spacethanks to the decoding branch of a VAE. However, existing VAE-based SEM fail to rep-resent properly the distribution of training samples in the embedding space, requiringto define specific additional constraints as diversity or orthogonality. In this work, wepropose to define the prototypes as the components of a Gaussian Mixture VAE (GM-VAE) that is an approximation of the distribution of training samples. We show that thisdefinition allows to produce relevant and diverse prototypes providing a probabilistic ex-planation of the model without assigning prototypes to a specific class. We support ourdefinition with extensive experimentation and comparison with previous self-explainableapproaches.
Graph Query Networks for Object Detection with Automotive Radar
Authors: Loveneet Saini; Hasan Tercan; Tobias Meisen
Venue: WACV 2026 (main)

Abstract: Object detection with 3D radar is essential for 360^ \circ  automotive perception, but radar's long wavelengths produce sparse and irregular reflections that challenge traditional grid and sequence-based convolutional and transformer detectors. This paper introduces Graph Query Networks (GQN), an attention-based framework that models objects sensed by radar as graphs, to extract individualized relational and contextual features. GQN employs a novel concept of graph queries to dynamically attend over the bird's-eye view (BEV) space, constructing object-specific graphs processed by two novel modules: EdgeFocus for relational reasoning and DeepContext Pooling for contextual aggregation. On the NuScenes dataset, GQN improves relative mAP by up to +53%, including a +8.2% gain over the strongest prior radar method, while reducing peak graph construction overhead by 80% with moderate FLOPs cost.
GroupPortrait: Multi-ID Portrait Generation with High Identity Preservation and Fine-Grained Control
Authors: Meijia Huang; Ruida Li; Bing Ma; Liangwei Jiang; Shuo Fang; Chenguang Ma
Venue: WACV 2026 (main)

Abstract: Identity-preserving portrait generation has achieved tremendous advancements with the development of diffusion models. However, multi-ID generation remains challenging due to degraded identity fidelity and insufficient control over layout, pose, and expression. To address these challenges, we propose GroupPortrait, a novel approach for multi-ID portrait generation with three key innovations:(1) LatentID for high-fidelity identity preservation, (2) Facial Controller enabling layout guidance and fine-grained facial control, and (3) Mask-Attention Controller allocating identity embeddings to specific facial regions. First, the LatentID module improves identity preservation by adding LatentID loss during training. It maps latent representations to identity features and uses ID consistency loss for feedback training to improve identity retention. Since LatentID loss is calculated in latent space, it is more efficient in terms of time and GPU usage compared to the method that calculates ID loss in pixel space. Second, to enhance layout and facial controllability, the Facial Controller utilizes 3D Morphable Models (3DMM) to acquire facial shapes, poses, and expressions for each individual, imposing strong spatial conditions during the diffusion process. Finally, we propose a novel Mask-Attention Controller for multi-ID generation, which distributes ID embeddings into target facial regions by aligning the cross-attention map of LatentID with the given facial region masks. Extensive experiments demonstrate that GroupPortrait can generate group portraits with high fidelity, local harmony, and controllability.
4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer's Diagnosis
Authors: Yuxiang Wei; Yanteng Zhang; Xi Xiao; Tianyang Wang; Xiao Wang; Vince D. Calhoun
Venue: WACV 2026 (main)

Abstract: Multimodal neuroimaging provides complementary structural and functional insights into both human brain organization and disease-related dynamics. Recent studies demonstrate enhanced diagnostic sensitivity for Alzheimer's disease (AD) through synergistic integration of neuroimaging data (e.g., sMRI, fMRI) with tabular data (e.g., behavioral and cognitive tests). However, the intrinsic heterogeneity across modalities (e.g., 4D spatiotemporal fMRI dynamics vs. 3D anatomical sMRI structure) presents critical challenges for discriminative feature fusion, often leading to information loss or biased fusion. To bridge this gap, we propose M2M-AlignNet: a multimodal co-attention network with latent alignment for early AD diagnosis using sMRI and fMRI. At the core of our approach is a multi-patch-to-multi-patch (M2M) contrastive loss function that quantifies and reduces representational discrepancies via weighted patch correspondence, explicitly aligning fMRI components across brain regions with their sMRI structural substrates without one-to-one constraints. Additionally, we propose a latent-as-query co-attention module to autonomously discover fusion patterns, circumventing modality prioritization biases while minimizing feature redundancy. We conduct extensive experiments to confirm the effectiveness of our method and highlight the correspondence between fMRI and sMRI as AD biomarkers.
Beyond Realism: Learning the Art of Expressive Composition with StickerNet
Authors: Haoming Lu; David Kocharian; Humphrey Shi
Venue: WACV 2026 (main)

Abstract: As a widely used operation in image editing workflows, image composition has traditionally been studied with a focus on achieving visual realism and semantic plausibility. However, in practical editing scenarios of the modern content creation landscape, many compositions are not intended to preserve realism. Instead, users of online platforms motivated by gaining community recognition often aim to create content that is more artistic, playful, or socially engaging. Taking inspiration from this observation, we define the expressive composition task, a new formulation of image composition that embraces stylistic diversity and looser placement logic, reflecting how users edit images on real-world creative platforms. To address this underexplored problem, we present StickerNet, a two-stage framework that first determines the composition type, then predicts placement parameters such as opacity, mask, location, and scale accordingly. Unlike prior work that constructs datasets by simulating object placements on real images, we directly build our dataset from 1.8 million editing actions collected on an anonymous online visual creation and editing platform, each reflecting user-community validated placement decisions. This grounding in authentic editing behavior ensures strong alignment between task definition and training supervision. User studies and quantitative evaluations show that StickerNet outperforms common baselines and closely matches human placement behavior, demonstrating the effectiveness of learning from real-world editing patterns despite the inherent ambiguity of the task. This work introduces a new direction in visual understanding that emphasizes expressiveness and user intent over realism.