GenAI4ELab/papercli-papers huggingface dataset:
FujiView: Multimodal Late-Fusion for Predicting Scenic Visibility
Authors: Bryceton Bible; Nehal Hasnaeen; Hairong Qi
Venue: WACV 2026 (main)

Abstract: Visibility of natural landmarks such as Mount Fuji is a defining factor in both tourism planning and visitor experience, yet it remains difficult to predict due to rapidly changing atmospheric conditions. We present FujiView, a multimodal learning framework and dataset for predicting scenic visibility by fusing webcam imagery with structured meteorological data. Our late-fusion approach combines image-derived class probabilities with numerical weather features to classify visibility into five categories. The dataset currently comprises over 100,000 webcam images paired with concurrent and forecasted weather conditions from more than 40 cameras around Mount Fuji, and continues to expand; it will be released to support further research in environmental forecasting. Experiments show that YOLO-based vision features dominate short-term horizons such as "nowcasting" and "samedaycasting", while weather-driven forecasts increasingly take over as the primary predictive signal beyond +1d. Late fusion consistently yields the highest overall accuracy, achieving ACC0.89 for same-day prediction and up to 84% for next-day forecasts. These results position Scenic Visibility Forecasting (SVF) as a new benchmark task for multimodal learning.
Sketch3R: Rapid and Realistic 3D VR Sketch Creation to Shape Retrieval
Authors: Mritunjoy Halder; Shivam Ashok Shukla; Lokender Tiwari; Raghav Mittal; Brojeshwar Bhowmick
Venue: WACV 2026 (main)

Abstract: Large 3D shape repositories are rapidly expanding, driven by advances in generative modeling, making efficient shape retrieval increasingly important for authoring tools. While text queries capture high-level semantics, they often fail to convey precise geometric details. 3D sketches provide a more expressive means of representing shape geometry, and recent AR/VR developments have made sketch-based retrieval practical. However, existing 3D sketch datasets face three major limitations:(1) reliance on quad meshes or voxel hulls, which often fail on complex or non-manifold shapes; (2) use of fixed-size point clouds that discard stroke connectivity and limit geometric fidelity; and (3) dependence on expensive curve-based or multi-view rendering pipelines, which hinder large-scale data generation. Limited point cloud representations also fail to capture sketch connectivity and topology when used to train retrieval models. To address these challenges, we propose Sketch3R, a scalable framework that converts arbitrary 3D meshes into human-like VR sketches using a graph-based representation that preserves stroke connectivity and adapts to sketch complexity. Leveraging this representation, Sketch3R employs a lightweight graph-attention Siamese network for efficient and accurate sketch-to-shape retrieval. Experiments demonstrate that our method outperforms prior approaches in both accuracy and speed, while robustly handling 3D shapes across diverse topologies.
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping
Authors: Vineet Bhat; Naman Patel; Prashanth Krishnamurthy; Ramesh Karri; Farshad Khorrami
Venue: WACV 2026 (main)

Abstract: Robotic manipulation of unseen objects via natural language commands remains challenging. Language driven robotic grasping (LDRG) predicts stable grasp poses from natural language queries and RGB-D images. We propose MapleGrasp, a novel framework that leverages mask-guided feature pooling for efficient vision-language driven grasping. Our two-stage training first predicts segmentation masks from CLIP-based vision-language features. The second stage pools features within these masks to generate pixel-level grasp predictions, improving efficiency, and reducing computation. Incorporating mask pooling results in a 7% improvement over prior approaches on the OCID-VLG benchmark. Furthermore, we introduce RefGraspNet, an open-source dataset eight times larger than existing alternatives, significantly enhancing model generalization for open-vocabulary grasping. MapleGrasp scores a strong grasping accuracy of 89% when compared with competing methods in the RefGraspNet benchmark. Our method achieves comparable performance to larger Vision-Language-Action models on the LIBERO benchmark, and shows significantly better generalization to unseen tasks. Real-world experiments on a Franka arm demonstrate 73% success rate with unseen objects, surpassing competitive baselines by 11%. Code and dataset is available here: https://github.com/vineet2104/MapleGrasp.
Explaining the Unseen: Multimodal Vision-Language Reasoning for Situational Awareness in Underground Mining Disasters
Authors: Mizanur Rahman Jewel; Mohamed Elmahallawy; Sanjay Madria; Samuel Frimpong
Venue: WACV 2026 (main)

Abstract: Underground mining disasters produce pervasive darkness, dust, and collapses that obscure vision and make situational awareness difficult for humans and conventional systems. To address this, we propose MDSE, Multimodal Disaster Situation Explainer, a novel vision-language framework that automatically generates detailed textual explanations of post-disaster underground scenes. MDSE has three-fold innovations: (i) Context-Aware Cross-Attention for robust alignment of visual and textual features even under severe degradation; (ii) Segmentation-aware dual pathway visual encoding that fuses global and region-specific embeddings; and (iii) Resource-Efficient Transformer-Based Language Model for expressive caption generation with minimal compute cost. To support this task, we present the Underground Mine Disaster (UMD) dataset--the first image-caption corpus of real underground disaster scenes--enabling rigorous training and evaluation. Extensive experiments on UMD and related benchmarks show that MDSE substantially outperforms state-of-the-art captioning models, producing more accurate and contextually relevant descriptions that capture crucial details in obscured environments, improving situational awareness for underground emergency response. The code is at https://github.com/mizanJewel/Multimodal-Disaster-Situation-Explainer.
ReBrain: Brain MRI Reconstruction from Sparse CT Slice via Retrieval-Augmented Diffusion
Authors: Junming Liu; Yifei Sun; Weihua Cheng; Yujin Kang; Yirong Chen; Ding Wang; Guosun Zeng
Venue: WACV 2026 (main)

Abstract: Magnetic Resonance Imaging (MRI) plays a crucial role in brain disease diagnosis, but it is not always feasible for certain patients due to physical or clinical constraints. Recent studies attempt to synthesize MRI from Computed Tomography (CT) scans; however, low-dose protocols often result in highly sparse CT volumes with poor through-plane resolution, making accurate reconstruction of the full brain MRI volume particularly challenging. To address this, we propose ReBrain, a retrieval-augmented diffusion framework for brain MRI reconstruction. Given any 3D CT scan with limited slices, we first employ a Brownian Bridge Diffusion Model (BBDM) to synthesize MRI slices along the 2D dimension. Simultaneously, we retrieve structurally and pathologically similar CT slices from a comprehensive prior database via a fine-tuned retrieval model. These retrieved slices are used as references, incorporated through a ControlNet branch to guide the generation of intermediate MRI slices and ensure structural continuity. We further account for rare retrieval failures when the database lacks suitable references and apply spherical linear interpolation to provide supplementary guidance. Extensive experiments on SynthRAD2023 and BraTS demonstrate that ReBrain achieves state-of-the-art performance in cross-modal reconstruction under sparse conditions.
QUOTA: Quantifying Objects with Text-to-Image Models for Any Domain
Authors: Wenfang Sun; Yingjun Du; Gaowen Liu; Yefeng Zheng; Cees G. M. Snoek
Venue: WACV 2026 (main)

Abstract: We tackle the problem of quantifying the number of objects by a generative text-to-image model. Rather than retraining such a model for each new image domain of interest, which leads to high computational costs and limited scalability, we are the first to consider this problem from a domain-agnostic perspective. We propose QUOTA, an optimization framework for text-to-image models that enables effective object quantification across unseen domains without retraining. It leverages a dual-loop meta-learning strategy to optimize a domain-invariant prompt. Further, by integrating prompt learning with learnable counting and domain tokens, our method captures stylistic variations and maintains accuracy, even for object classes not encountered during training.For evaluation, we adopt a new benchmark QUANT-Bench specifically designed for object quantification in domain generalization, enabling rigorous assessment of object quantification accuracy and adaptability across unseen domains in text-to-image generation.Extensive experiments demonstrate that QUOTA outperforms conventional models in both object quantification accuracy and semantic consistency, setting a new benchmark for efficient and scalable text-to-image generation for any domain.
Chain-of-Look Spatial Reasoning for Dense Surgical Instrument Counting
Authors: Rishikesh Bhyri; Brian R Quaranto; Junsong Yuan; Peter C W Kim; Nan Xi
Venue: WACV 2026 (main)

Abstract: Accurate counting of surgical instruments in Operating Rooms (OR) is a critical prerequisite for ensuring patient safety during surgery. Despite recent progress of large visual-language models and agentic AI, accurately counting such instruments remains highly challenging, particularly in dense scenarios where instruments are tightly clustered. To address this problem, we introduce Chain-of-Look, a novel visual reasoning framework that mimics the sequential human counting process by enforcing a structured visual chain, rather than relying on classic object detection which is unordered. This visual chain guides the model to count along a coherent spatial trajectory, improving accuracy in complex scenes. To further enforce the physical plausibility of the visual chain, we introduce the neighboring loss function, which explicitly models the spatial constraints inherent to densely packed surgical instruments. We also present SurgCount-HD, a new dataset comprising 1,464 high-density surgical instrument images. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches for counting (e.g., CountGD, REC) as well as Multimodality Large Language Models (e.g., Qwen, ChatGPT) in the challenging task of dense surgical instrument counting.
Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score
Authors: Eman Ali; Sathira Silva; Chetan Arora; Muhammad Haris Khan
Venue: WACV 2026 (main)

Abstract: Vision-language models (VLMs) like CLIP excel in zero-shot learning by aligning image and text representations through contrastive pretraining. Existing approaches to unsupervised adaptation (UA) for fine-grained classification with VLMs either rely on fixed alignment scores that may not capture evolving, subtle class distinctions or rely on computationally expensive pseudo-labeling strategies that limit scalability.In contrast, we show that modeling fine-grained cross-modal interactions during adaptation produces more accurate, class-discriminative pseudo-labels and substantially improves performance over state-of-the-art (SOTA) methods.We introduce Fine-grained Alignment and Interaction Refinement (FAIR), an innovative approach that dynamically aligns localized image features with descriptive language embeddings through a set of Class Description Anchors (CDA). This enables the definition of a Learned Alignment Score (LAS), which incorporates CDA as an adaptive classifier, facilitating cross-modal interactions to improve self-training in unsupervised adaptation.Furthermore, we propose a self-training weighting mechanism designed to refine pseudo-labels in the presence of inter-class ambiguities.Our approach, FAIR, delivers a substantial performance boost in fine-grained unsupervised adaptation, achieving a notable overall gain of 2.78% across 13 fine-grained datasets compared to SOTA methods.
Zero-Shot Coreset Selection via Iterative Subspace Sampling
Authors: Brent A. Griffin; Jacob Marks; Jason J. Corso
Venue: WACV 2026 (main)

Abstract: Deep learning increasingly relies on massive data with substantial storage, annotation, and training costs. To reduce costs, coreset selection finds a representative subset of data to train models while ideally performing on par with the full data training. To maximize performance, current state-of-the-art coreset methods select data using dataset-specific ground truth labels and training. However, these methodological requirements prevent selection at scale on real-world, unlabeled data. To that end, this paper addresses the selection of coresets that achieve state-of-the-art performance but without using any labels or training on candidate data. Instead, our solution, Zero-Shot Coreset Selection via Iterative Subspace Sampling (ZCore), uses previously-trained foundation models to generate zero-shot, high-dimensional embedding spaces to interpret unlabeled data. ZCore then iteratively quantifies the relative value of all candidate data based on coverage and redundancy in numerous subspace distributions. Finally, ZCore selects a coreset sized for any data budget to train downstream models. We evaluate ZCore on four datasets and outperform several state-of-the-art label-based methods, especially at low data rates that provide the most substantial cost reduction. On ImageNet, ZCore selections for 10% training data achieve a downstream validation accuracy of 53.99%, which outperforms prior label-based methods and removes annotation and training costs for 1.15 million images. Our paper's code is publicly available at https://github.com/voxel51/zcore.
Mobile-Oriented Video Diffusion: Enabling Text-to-Video Generation on Mobile Devices Without Retraining, Compression, or Pruning
Authors: Bosung Kim; Kyuhwan Lee; Isu Jeong; Jungmin Cheon; Yeojin Lee; Seulki Lee
Venue: WACV 2026 (main)

Abstract: We present Mobile-Oriented Video Diffusion (MOVD) framework, the first diffusion-based text-to-video generation framework designed for efficient on-device execution on smartphone-grade hardware without requiring retraining, compression or pruning of the target denoising model. To address the challenges of diffusion-based text-to-video generation on computation- and memory-limited mobile devices, MOVD applies two novel techniques to pretrained video generative models. First, Linear Proportional Leap (LPL) reduces the excessive denoising steps required in video diffusion through an efficient leap-based approach. Second, Temporal Dimension Token Merging (TDTM) reduces the heavy computational load of attention layers by merging consecutive tokens along the temporal dimension. By integrating these techniques with Concurrent Inference with Dynamic Loading (CI-DL), which splits large models into smaller, executable segments for limited memory environments, MOVD allows a text-to-video diffusion generative model to run on an iPhone 15 Pro. We envision the proposed MOVD as a significant first step toward democratizing state-of-the-art generative technologies, enabling video generation on mobile and embedded devices without resource-intensive optimization procedures.
Efficient Vision Transformers via Token Merging with Head-wise Attention Correction
Authors: Yuki Ichikawa; Masato Motomura; Thiem Van Chu; Daichi Fujiki
Venue: WACV 2026 (main)

Abstract: Vision Transformers (ViTs) offer strong performance by modeling global relationships across image patches, but their scalability is limited by the quadratic cost of self-attention. To mitigate this, Token Merging (ToMe) reduces computation by merging similar tokens. This approach relies on proportional attention to preserve the original balance of attention weights after merging. Yet, proportional attention does not fully resolve the attention distortion. It only compensates for a merged token's influence on other tokens, while ignoring the fundamental distortion of the token's own self-attention score. This change creates unaddressed distortions that vary across attention heads. In this work, we conduct a detailed analysis of these attention distortions and reveal their dependence on the query-key projection weights of each head. Based on this finding, we propose Head-wise Attention Correction (HAC), a method that adjusts attention scores after token merging by accounting for head-specific characteristics. HAC effectively mitigates the distortions overlooked by proportional attention, maintaining model accuracy while significantly reducing computation. Experiments on ImageNet demonstrate that our method effectively improves the trade-off between efficiency and performance, advancing the development of efficient Vision Transformers via token merging. Code is available at https://github.com/ychikawa/HAC-ToMe
GFT-GCN: Privacy-Preserving 3D Face Mesh Recognition with Spectral Diffusion
Authors: Hichem Felouat; Hanrui Wang; Isao Echizen
Venue: WACV 2026 (main)

Abstract: 3D face recognition offers a robust biometric solution by capturing facial geometry, providing resilience to variations in illumination, pose changes, and presentation attacks. Its strong spoof resistance makes it suitable for high-security applications, but protecting stored biometric templates remains critical. We present GFT-GCN, a privacy-preserving 3D face recognition framework that combines spectral graph learning with diffusion-based template protection. Our approach integrates the Graph Fourier Transform (GFT) and Graph Convolutional Networks (GCN) to extract compact, discriminative spectral features from 3D face meshes. To secure these features, we introduce a spectral diffusion mechanism that produces irreversible, renewable, and unlinkable templates. A lightweight client-server architecture ensures that raw biometric data never leaves the client device. Experiments on the BU-3DFE and FaceScape datasets demonstrate high recognition accuracy and strong resistance to reconstruction attacks. Results show that GFT-GCN effectively balances privacy and performance, offering a practical solution for secure 3D face authentication.
Disentangle and Regularize: Sign Language Production with Articulator-Based Disentanglement and Channel-Aware Regularization
Authors: Sümeyye Meryem Taşyürek; Tuğçe Kızıltepe; Hacer Yalim Keles
Venue: WACV 2026 (main)

Abstract: In this work, we propose DARSLP, a simple gloss-free, transformer-based sign language production (SLP) framework that directly maps spoken-language text to sign pose sequences. We first train a pose autoencoder that encodes sign poses into a compact latent space using an articulator-based disentanglement strategy, where features corresponding to the face, right hand, left hand, and body are modeled separately to promote structured and interpretable representation learning. Next, a non-autoregressive transformer decoder is trained to predict these latent representations from word-level text embeddings of the input sentence. To guide this process, we apply channel-aware regularization by aligning predicted latent distributions with priors extracted from the ground-truth encodings using a KL divergence loss. The contribution of each channel to the loss is weighted according to its associated articulator region, enabling the model to account for the relative importance of different articulators during training. Our approach does not rely on gloss supervision or pretrained models, and achieves state-of-the-art results on the PHOENIX14T and CSL-Daily datasets.
Test-Time Adaptation through Semantically-guided Feature Decomposition for Few-shot Chest X-ray Diagnosis
Authors: Jayant Mahawar; Angshuman Paul
Venue: WACV 2026 (main)

Abstract: Training a deep neural network with a small amount of labeled data is challenging. The challenge is even more severe for medical images because of the many possible variations in the images. We propose a novel framework for few-shot chest x-ray (CXR) diagnosis. For classification problems, training with limited data may be facilitated if class-specific features can be extracted and utilized. Semantic information about the abnormalities may also be helpful in this context. To that end, we design an autoencoder-based approach that extracts visual features and decomposes them into class-agnostic and class-specific features utilizing the semantic information of the abnormalities. The decomposition helps in efficient classification using the class-specific features. Additionally, we perform test-time adaptation to deal with possible variations in the test data compared to the training data. From this perspective, our method is one of the first of its kind. Extensive evaluations on publicly available chest x-ray datasets under few-shot settings show the effectiveness of our method. Results on the publicly available chest x-ray datasets show a 3-5% improvement in AUROC scores.
Self-Supervised Compression and Artifact Correction for Streaming Underwater Imaging Sonar
Authors: Rongsheng Qian; Chi Xu; Xiaoqiang Ma; Hao Fang; Yili Jin; William I. Atlas; Jiangchuan Liu
Venue: WACV 2026 (main)

Abstract: Real-time imaging sonar is crucial for underwater monitoring where optical sensing fails, but its use is limited by low uplink bandwidth and severe sonar-specific artifacts (speckle, motion blur, reverberation, acoustic shadows) affecting up to 98% of frames. We present SCOPE, a self-supervised framework that jointly performs compression and artifact correction without clean-noise pairs or synthetic assumptions. SCOPE combines (i) Adaptive Codebook Compression (ACC), which learns frequency-encoded latent representations tailored to imaging sonar, with (ii) Frequency-Aware Multiscale Segmentation (FAMS), which decomposes frames into low-frequency structure and sparse high-frequency dynamics while suppressing rapidly fluctuating artifacts. A hedging training strategy further guides frequency-aware learning using low-pass proxy pairs generated without labels. Evaluated on months of in-situ ARIS sonar data, SCOPE achieves a structural similarity index (SSIM) of 0.77, representing a 40% improvement over prior self-supervised denoising baselines, at bitrates down to \leq 0.0118 bpp. It reduces uplink bandwidth by more than 80% while improving downstream detection. The system runs in real time, with 3.1 ms encoding on an embedded GPU and 97 ms full multi-layer decoding on the server end. SCOPE has been deployed for months in three Pacific Northwest rivers to support real-time salmon enumeration and environmental monitoring in the wild. Results demonstrate that learning frequency-structured latents enables practical, low-bitrate sonar streaming with preserved signal details under real-world deployment conditions.