GenAI4ELab/papercli-papers huggingface dataset:
mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrieval
Authors: Kyeong Seon Kim; Baek Seong-Eun; Lee Jung-Mok; Tae-Hyun Oh
Venue: WACV 2026 (main)

Abstract: Scalable Vector Graphics (SVGs) function both as visual images and as structured code that encode rich geometric and layout information, yet most methods rasterize them and discard this symbolic organization. At the same time, recent sentence embedding methods produce strong text representations but do not naturally extend to visual or structured modalities. We propose a training-free, instruction-guided multimodal embedding framework that uses a Multimodal Large Language Model (MLLM) to map text, raster images, and SVG code into an aligned embedding space. We control the direction of embeddings through modality-specific instructions and structural SVG cues, eliminating the need for learned projection heads or contrastive training. Our method has two key components: (1) Multimodal Explicit One-word Limitation (mEOL), which instructs the MLLM to summarize any multimodal input into a single token whose hidden state serves as a compact semantic embedding. (2) A semantic SVG rewriting module that assigns meaningful identifiers and simplifies nested SVG elements through visual reasoning over the rendered image, exposing geometric and relational cues hidden in raw code. Using a repurposed VGBench, we build the first text-to-SVG retrieval benchmark and show that our training-free embeddings outperform encoder-based and training-based multimodal baselines. These results highlight prompt-level control as an effective alternative to parameter-level training for structure-aware multimodal retrieval. Project page: https://scene-the-ella.github.io/meol/
GateFusion: Hierarchical Gated Cross-Modal Fusion for Active Speaker Detection
Authors: Yu Wang; Juhyung Ha; Frangil M. Ramirez; Yuchen Wang; David J. Crandall
Venue: WACV 2026 (main)

Abstract: Active Speaker Detection (ASD) aims to identify who is currently speaking in each frame of a video. Most state-of-the-art approaches rely on late fusion to combine visual and audio features, but late fusion often fails to capture fine-grained cross-modal interactions, which can be critical for robust performance in unconstrained scenarios. In this paper, we introduce GateFusion, a novel architecture that combines strong pretrained unimodal encoders with a Hierarchical Gated Fusion Decoder (HiGate). HiGate enables progressive, multi-depth fusion by adaptively injecting contextual features from one modality into the other at multiple layers of the Transformer backbone, guided by learnable, bimodally-conditioned gates. To further strengthen multimodal learning, we propose two auxiliary objectives: Masked Alignment Loss (MAL) to align unimodal outputs with multimodal predictions, and Over-Positive Penalty (OPP) to suppress spurious video-only activations. GateFusion establishes new state-of-the-art results on several challenging ASD benchmarks, achieving 77.8% mAP (+9.4%), 86.1% mAP (+2.9%), and 96.1% mAP (+0.5%) on Ego4D-ASD, UniTalk, and WASD benchmarks, respectively, and delivering competitive performance on AVA-ActiveSpeaker. Out-of-domain experiments demonstrate the generalization of our model, while comprehensive ablations show the complementary benefits of each component.
From Cognitive Priors to Instance Semantics: A Unified Framework for Multi-task Affective Computing
Authors: Guanyu Hu; Dimitrios Kollias; Xinyu Yang
Venue: WACV 2026 (main)

Abstract: Understanding human affect via Valence-Arousal, Expressions, and Action Unit is essential for human-machine interaction. While recent multi-task learning (MTL) methods seek to unify these tasks, they overlook three key challenges: (i) the absence of unified modeling all three affective task types: regression, detection, and classification; (ii) reliance on complete annotations for all tasks, leaving disjoint single-task datasets underutilized; and (iii) task conflicts caused by Noisy Gradients, Negative Transfer (NT), and Task-specific Performance Misalignment (TPM). We introduce COIN, a novel two-stage MTL framework that bridges Cognitive Priors and Instance Semantics for robust training. First, we design a cognitively guided cross-task label induction strategy to propagate supervision under sparse annotations and mitigate NT, yielding strong task-specific CogXperts. Second, we introduce two complementary branches to address TPM: (i) Task-Specific Branch: transferring cognitive knowledge from task-optimal CogXperts to jointly optimize objectives under partial supervision, and (ii) Semantic Alignment Branch: enhancing instance-level semantic representations via Class-Conditioned and Instance-Adaptive Prompts. Experiments across six diverse datasets demonstrate COIN's robustness and generalization.
Detection-Driven Object Count Optimization for Text-to-Image Diffusion Models
Authors: Oz Zafar; Yuval Cohen; Lior Wolf; Idan Schwartz
Venue: WACV 2026 (main)

Abstract: Accurately controlling object count in text-to-image generation remains a key challenge. Supervised methods often fail, as training data rarely covers all count variations. Methods that manipulate the denoising process to add or remove objects can help; however, they still require labeled data, limit robustness and image quality, and rely on a slow, iterative process. Pre-trained differentiable counting models that rely on soft object density summation exist and could steer generation, but employing them presents three main challenges: (i) they are pre-trained on clean images, making them less effective during denoising steps that operate on noisy inputs; (ii) they are not robust to viewpoint changes; and (iii) optimization is computationally expensive, requiring repeated model evaluations per image. We propose a new framework that uses pre-trained object counting techniques and object detectors to guide generation. First, we optimize a counting token using an outer-loop loss computed on fully generated images. Second, we introduce a detection-driven scaling term that corrects errors caused by viewpoint and proportion shifts, etc., without requiring backpropagation through the detection model. Third, we show that the optimized parameters can be reused for new prompts, removing the need for repeated optimization. Our method provides efficiency through token reuse, flexibility via compatibility with various detectors, and accuracy with improved counting across diverse object categories.
PoseAdapt: Sustainable Human Pose Estimation via Continual Learning Benchmarks and Toolkit
Authors: Muhammad Saif Ullah Khan; Didier Stricker
Venue: WACV 2026 (main)

Abstract: Human pose estimators are typically retrained from scratch or naively fine-tuned whenever keypoint sets, sensing modalities, or deployment domains change--an inefficient, compute-intensive practice that rarely matches field constraints. We present PoseAdapt, an open-source framework and benchmark suite for continual pose model adaptation. PoseAdapt defines domain-incremental and class-incremental tracks that simulate realistic changes in density, lighting, and sensing modality, as well as skeleton growth. The toolkit supports two workflows: (i) Strategy Benchmarking, which lets researchers implement continual learning (CL) methods as plugins and evaluate them under standardized protocols; and (ii) Model Adaptation, which allows practitioners to adapt strong pretrained models to new tasks with minimal supervision. We evaluate representative regularization-based methods in single-step and sequential settings. Benchmarks enforce a fixed lightweight backbone, no access to past data, and tight per-step budgets. This isolates adaptation strategy effects, highlighting the difficulty of maintaining accuracy under strict resource limits. PoseAdapt connects modern CL techniques with practical pose estimation needs, enabling adaptable models that improve over time without repeated full retraining.
Fully Unsupervised Self-debiasing of Text-to-Image Diffusion Models
Authors: Korada Sri Vardhana; Shrikrishna Lolla; Soma Biswas
Venue: WACV 2026 (main)

Abstract: Text-to-image (T2I) diffusion models have achieved widespread success due to their ability to generate high-resolution, photorealistic images. These models are trained on large-scale datasets, like LAION-5B, often scraped from the internet. However, since this data contains numerous biases, the models inherently learn and reproduce them, resulting in stereotypical outputs. We introduce SelfDebias, a fully unsupervised test-time debiasing method applicable to any diffusion model that uses a UNet as its noise predictor. SelfDebias identifies semantic clusters in an image encoder's embedding space and uses these clusters to guide the diffusion process during inference, minimizing the KL divergence between the output distribution and the uniform distribution. Unlike supervised approaches, SelfDebias does not require human-annotated datasets or external classifiers trained for each generated concept. Instead, it is designed to automatically identify semantic modes. Extensive experiments show that SelfDebias generalizes across prompts and diffusion model architectures, including both conditional and unconditional models. It not only effectively debiases images along key demographic dimensions while maintaining the visual fidelity of the generated images, but also more abstract concepts for which identifying biases is also challenging.
Cycle-Consistent Multi-Graph Matching for Self-Supervised Annotation of C. Elegans
Authors: Sebastian Stricker; Christoph Karg; Lisa Hutschenreiter; Bogdan Savchynskyy; Dagmar Kainmueller
Venue: WACV 2026 (main)

Abstract: In this work we present a novel approach for unsupervised multi-graph matching, which applies to problems for which a Gaussian distribution of keypoint features can be assumed. We leverage cycle consistency as loss for self-supervised learning, and determine Gaussian parameters through Bayesian Optimization, yielding a highly efficient approach that scales to large datasets. Our fully unsupervised approach enables us to reach the accuracy of state-of-the-art supervised methodology for the biomedical use case of semantic cell annotation in 3D microscopy images of the worm C. elegans. To this end, our approach yields the first unsupervised atlas of C. elegans, i.e. a model of the joint distribution of all of its cell nuclei, without the need for any ground truth cell annotation. This advancement enables highly efficient semantic annotation of cells in large microscopy datasets, overcoming a current key bottleneck. Beyond C. elegans, our approach offers fully unsupervised construction of cell-level atlases for any model organism with a stereotyped body plan down to the level of unique semantic cell labels, and thus bears the potential to catalyze respective biomedical studies in a range of further species.
Online Episodic Memory Visual Query Localization with Egocentric Streaming Object Memory
Authors: Zaira Manigrasso; Matteo Dunnhofer; Antonino Furnari; Moritz Nottebaum; Antonio Finocchiaro; Davide Marana; Rosario Forte; Giovanni Maria Farinella; Christian Micheloni
Venue: WACV 2026 (main)

Abstract: Episodic memory retrieval enables wearable cameras to recall objects or events previously observed in video.However, existing formulations assume an "offline" setting with full video access at query time, limiting their applicability in real-world scenarios with power and storage-constrained wearable devices.Towards more application-ready episodic memory systems, we introduce Online Visual Query 2D (OVQ2D), a task where models process video streams online, observing each frame only once, and retrieve object localizations using a compact memory instead of full video history.We address OVQ2D with ESOM (Egocentric Streaming Object Memory), a novel framework integrating an object discovery module, an object tracking module, and a memory module that find, track, and store spatio-temporal object information for efficient querying.Experiments on Ego4D demonstrate ESOM's superiority over other online approaches, though OVQ2D remains challenging, with top performance at only  4% success. ESOM's accuracy increases markedly with perfect object tracking (31.91%), discovery (40.55%), or both (81.92%), underscoring the need of applied research on these components.
Visual Detector Compression via Location-Aware Discriminant Analysis
Authors: Qizhen Lan; Jung Im Choi; Qing Tian
Venue: WACV 2026 (main)

Abstract: Deep neural networks are powerful, yet their high complexity greatly limits their potential to be deployed on billions of resource-constrained edge devices. Pruning is a crucial network compression technique, yet most existing methods focus on classification models, with limited attention to detection. Even among those addressing detection, there is a lack of utilization of essential localization information. Also, many pruning methods passively rely on pre-trained models, in which useful and useless components are intertwined, making it difficult to remove the latter without harming the former at the neuron/filter level. To address the above issues, in this paper, we propose a proactive detection-discriminants-based network compression approach for deep visual detectors, which alternates between two steps: (1) maximizing and compressing detection-related discriminants and aligning them with a subset of neurons/filters immediately before the detection head, and (2) tracing the detection-related discriminating power across the layers and discarding features of lower importance. Object location information is exploited in both steps. Extensive experiments, employing four advanced detection models and four state-of-the-art competing methods on the KITTI and COCO datasets, highlight the superiority of our approach. Remarkably, our compressed models can even beat the original base models with a substantial reduction in complexity.
Salience-SGG: Enhancing Unbiased Scene Graph Generation with Iterative Salience Estimation
Authors: Runfeng Qu; Ole Hall; Pia K Bideau; Julie Ouerfelli-Ethier; Martin Rolfs; Klaus Obermayer; Olaf Hellwich
Venue: WACV 2026 (main)

Abstract: Scene Graph Generation (SGG) suffers from a long-tailed distribution, where a few predicate classes dominate while many others are underrepresented, leading to biased models that underperform on rare relations. Unbiased-SGG methods address this by implementing debiasing strategies, but often at the cost of spatial understanding--resulting in over-reliance on semantic priors. We introduce Salience-SGG, a novel framework featuring an Iterative Salience Decoder (ISD) that emphasizes triplets with salient spatial structures. To support this, we propose semantic-agnostic salience labels guiding ISD. Evaluations on Visual Genome, Open Images V6, and GQA-200 show that Salience-SGG achieves state-of-the-art performance and improves existing Unbiased-SGG methods in their spatial understanding as demonstrated by the Pairwise Localization Average Precision
SPAR-Det: Segmentation-guided and Prior-Aided Routing for Small Object Detection
Authors: Seungchan Kwon; Gyuil Lim; Youngjoon Han
Venue: WACV 2026 (main)

Abstract: Small Object Detection (SOD) is essential for real-world applications, including satellite image analysis and drone-based surveillance, where target objects typically exhibit limited spatial extent, high density, and visual similarity to complex backgrounds. These factors substantially hinder conventional object detection methods, resulting in weak feature representations and poor detection accuracy. To overcome these challenges, we introduce Segmentation-guided and Prior-Aided Routing for Small Object Detection (SPAR-Det), a unified framework integrating segmentation-guided attention, geometric prior supervision, and adaptive feature routing. At its core, SPAR-Det employs a Cross-Attention Heterogeneous Feature Fusion (CAHF) module that leverages pretrained segmentation backbones to enhance foreground object features while effectively suppressing background. Additionally, we propose a Geometric Prior Supervision Loss that combines Gaussian bounding box maps with segmentation feature maps, providing crucial geometric context and semantic cues to address the limited self-representation capability of small objects. Furthermore, our framework includes a Mixture-of-Experts (MoE) detection head, dynamically allocating specialized classifiers according to varying scene characteristics, thereby significantly improving generalization across diverse environments. Extensive evaluations conducted on three benchmark datasets, AI-TOD, Visdrone and LEVIR-Ship, demonstrate that SPAR-Det achieves state-of-the-art performance, verifying its robustness and applicability for challenging small object detection scenarios. Code is available at https://github.com/kwonseungchan/SPAR-Det.git
Semi-supervised Key-Point Estimation for Echocardiography Video
Authors: Seok-Hwan Oh; Hyeon-Jik Lee; Guil Jung; Myeong-Gee Kim; Young-Min Kim; Hyuksool Kwon; Hyeon-Min Bae
Venue: WACV 2026 (main)

Abstract: Echocardiography, a widely used imaging modality, offers real-time assessments of cardiac morphology and function, with a particular emphasis on left ventricular dynamics. Despite its clinical importance, existing automated methods for echocardiographic analysis struggle to ensure temporal consistency in left ventricular key-point trajectories, largely due to their reliance on static frame annotations. To overcome these challenges, we propose a semi-supervised trajectory refinement framework that employs inter-frame correlations to enhance key-point estimation across echocardiography videos. A semi-supervised trajectory learning scheme is presented to improve the efficacy of key-point trajectory analysis using unannotated echocardiography videos. The experiments present considerable improvements in both spatial accuracy and temporal stability of the left ventricle key-point trajectories, outperforming state-of-the-art baselines and demonstrating the clinical applicability for robust echocardiography analysis.
Locally Explaining Prediction Behavior via Gradual Interventions and Measuring Property Gradients
Authors: Niklas Penzel; Joachim Denzler
Venue: WACV 2026 (main)

Abstract: Deep learning models achieve high predictive performance but lack intrinsic interpretability, hindering our understanding of the learned prediction behavior. Existing local explainability methods focus on associations, neglecting the causal drivers of model predictions. Other approaches adopt a causal perspective but primarily provide global, model-level explanations. However, for specific inputs, it's unclear whether globally identified factors apply locally. To address this limitation, we introduce a novel framework for local interventional explanations by leveraging recent advances in image-to-image editing models. Our approach performs gradual interventions on semantic properties to quantify the corresponding impact on a model's predictions using a novel score, the expected property gradient magnitude. We demonstrate the effectiveness of our approach through an extensive empirical evaluation on a wide range of architectures and tasks. First, we validate it in a synthetic scenario and demonstrate its ability to locally identify biases. Afterward, we apply our approach to investigate medical skin lesion classifiers, analyze network training dynamics, and study a pre-trained CLIP model with real-life interventional data. Our results highlight the potential of interventional explanations on the property level to reveal new insights into the behavior of deep models.
Scalable Video Action Anticipation with Cross Linear Attentive Memory
Authors: Zeyun Zhong; Manuel Martin; David Schneider; David J. Lerch; Chengzhi Wu; Frederik Diederichs; Juergen Gall; Jürgen Beyerer
Venue: WACV 2026 (main)

Abstract: Recent advances in action anticipation rely heavily on Transformer architectures to learn discriminative representations of the past observation, incurring high computational and memory overhead that limits their applicability to long videos. While temporal processors with linear complexity like RNNs and state-space models offer efficient alternatives, their sequential nature risks overlooking subtle cues in observed frames that could enhance future anticipation. We address this limitation with Cross Linear Attentive Memory (CLAM), a memory module that selectively retrieves complementary context cues from frame features. By reformulating linear attention to replace traditional cross-attention, CLAM achieves linear computation complexity and constant memory usage relative to input length. Finally, by fusing the outputs of the temporal processor and CLAM, a non-autoregressive Transformer decoder generates future actions in one shot with high accuracy. Experiments on egocentric (EpicKitchens100 and Ego4D) and third-person (Thumos14) benchmarks demonstrate our model's superior anticipation accuracy and scalability, processing longer sequences with significantly less latency growth than alternatives. Our approach also achieves promising results in online action detection.
Are All Marine Species Created Equal? Performance Disparities in Underwater Object Detection
Authors: Melanie Wille; Tobias Fischer; Scarlett Raine
Venue: WACV 2026 (main)

Abstract: Underwater object detection is critical for monitoring marine ecosystems but poses unique challenges, including degraded image quality, imbalanced class distribution, and distinct visual characteristics. Not every species is detected equally well, yet underlying causes remain unclear. We investigate two questions: 1) What factors beyond data quantity drive class-specific performance disparities? 2) How can we systematically improve detection of under-performing marine species? Using manipulated DUO and RUOD datasets, we decouple localization from classification and investigate the scallop class as a representative case. Localization analysis using YOLO11 and TIDE shows that foreground-background discrimination is the most problematic stage regardless of data quantity. Classification experiments reveal persistent precision gaps even under balanced sampling, indicating intrinsic feature-based challenges beyond data scarcity. We recommend imbalanced distributions when prioritizing precision, and balanced distributions when prioritizing recall. Improving under-performing classes should focus on algorithmic advances, especially within localization modules. We publicly release our code and datasets.
MoRe: Monocular Geometry Refinement via Graph Optimization for Cross-View Consistency
Authors: Dongki Jung; Jaehoon Choi; Yonghan Lee; Sungmin Eum; Heesung Kwon; Dinesh Manocha
Venue: WACV 2026 (main)

Abstract: Monocular 3D foundation models offer an extensible solution for perception tasks, making them attractive for broader 3D vision applications.In this paper, we propose MoRe, a training-free Monocular Geometry Refinement method designed to improve cross-view consistency and achieve scale alignment.To induce inter-frame relationships, our method employs feature matching between frames to establish correspondences.Rather than applying simple least squares optimization on these matched points, we formulate a graph-based optimization framework that performs local planar approximation using the estimated 3D points and surface normals estimated by monocular foundation models.This formulation addresses the scale ambiguity inherent in monocular geometric priors while preserving the underlying 3D structure.We further demonstrate that MoRe not only enhances 3D reconstruction but also improves novel view synthesis, particularly in sparse-view rendering scenarios.