GenAI4ELab/papercli-papers huggingface dataset:
Cluster-Guided Adversarial Perturbations for Robust Contrastive Learning
Authors: Seongyun Seo; Sungmin Han; Jeonghyun Lee; Sangkyun Lee
Venue: WACV 2026 (main)

Abstract: Adversarial contrastive learning aims to learn robust representations from unlabeled data by integrating adversarial training and contrastive learning. Accordingly, existing methods typically generate adversarial perturbations that maximize the contrastive loss during adversarial training. However, these approaches frequently produce ineffective perturbations, as their effectiveness heavily depends on the semantic similarity among samples within each mini-batch, which is not explicitly controlled. As a result, the improvement in robustness remains limited. To address this, we propose a novel approach that leverages the well-structured representation space learned via contrastive learning, where semantically similar samples cluster well while dissimilar ones are positioned farther apart. Exploiting this clustering structure, we construct adversarial perturbations that move samples away from a group of similar samples and toward a group of dissimilar ones, thereby inducing stronger adversarial effects. Compared to existing approaches, our method achieves significant improvements in robust accuracy by up to 4.75% against the PGD attack and 7.59% against Auto-Attack.
FSP-DETR: Few-Shot Prototypical Parasitic Ova Detection
Authors: Shubham Trehan; Udhav Ramachandran; Akash Rao; Ruth Scimeca; Sathyanarayanan N. Aakur
Venue: WACV 2026 (main)

Abstract: Object detection in biomedical settings is fundamentally constrained by the scarcity of labeled data and the frequent emergence of novel or rare categories. We present FSP-DETR, a unified detection framework that enables robust few-shot detection, open-set recognition, and generalization to unseen biomedical tasks within a single model. Built upon a class-agnostic DETR backbone, our approach constructs class prototypes from original support images and learns an embedding space using augmented views and a lightweight transformer decoder. Training jointly optimizes a prototype matching loss, an alignment-based separation loss, and a KL divergence regularization to improve discriminative feature learning and calibration under scarce supervision. Unlike prior work that tackles these tasks in isolation, FSP-DETR enables inference-time flexibility to support unseen class recognition, background rejection, and cross-task adaptation without retraining. We also introduce a new ova species detection benchmark with 20 parasite classes and establish standardized evaluation protocols. Extensive experiments across ova, blood cell, and malaria detection tasks demonstrate that FSP-DETR significantly outperforms prior few-shot and prototype-based detectors, especially in low-shot and open-set scenarios.
IDEAL-M3D: Instance Diversity-Enriched Active Learning for Monocular 3D Detection
Authors: Johannes Meier; Florian Günther; Riccardo Marin; Oussema Dhaouadi; Jacques Kaiser; Daniel Cremers
Venue: WACV 2026 (main)

Abstract: Monocular 3D detection relies on just a single camera and is therefore easy to deploy. Yet, achieving reliable 3D understanding from monocular images requires substantial annotation, and 3D labels are especially costly. To maximize performance under constrained labeling budgets, it is essential to prioritize annotating samples expected to deliver the largest performance gains. This prioritization is the focus of active learning. Curiously, we observed two significant limitations in active learning algorithms for 3D monocular object detection. First, previous approaches select entire images, which is inefficient, as non-informative instances contained in the same image also need to be labeled. Secondly, existing methods rely on uncertainty-based selection, which in monocular 3D object detection creates a bias toward depth ambiguity. Consequently, distant objects are selected, while nearby objects are overlooked.To address these limitations, we propose IDEAL-M3D, the first instance-level pipeline for monocular 3D detection.For the first time, we demonstrate that an explicitly diverse, fast-to-train ensemble improves diversity-driven active learning for monocular 3D. We induce diversity with heterogeneous backbones and task-agnostic features, loss weight perturbation, and time-dependent bagging. IDEAL-M3D shows superior performance and significant resource saving: with just 60% of the annotations, we achieve similar or better AP3D on KITTI validation and test set results compared to training the same detector on the whole dataset.
RemEdit: Efficient Diffusion Editing with Riemannian Geometry
Authors: Eashan Adhikarla; Brian D. Davison
Venue: WACV 2026 (main)

Abstract: Controllable image generation is fundamental to the success of modern generative AI, yet it faces a critical trade-off between semantic fidelity and inference speed. The RemEdit diffusion-based framework addresses this trade-off with two synergistic innovations. First, for editing fidelity, we navigate the latent space as a Riemannian manifold. A mamba-based module efficiently learns the manifold's structure, enabling direct and accurate geodesic path computation for smooth semantic edits. This control is further refined by a dual-SLERP blending technique and a goal-aware prompt enrichment pass from a Vision-Language Model. Second, for additional acceleration, we introduce a novel task-specific attention pruning mechanism. A lightweight pruning head learns to retain tokens essential to the edit, enabling effective optimization without the semantic degradation common in content-agnostic approaches. RemEdit surpasses prior state-of-the-art editing frameworks while maintaining real-time performance under 50% pruning. Consequently, RemEdit establishes a new benchmark for practical and powerful image editing. Source code: https://github.com/eashanadhikarla/RemEdit.
VFace: A Training-Free Approach for Diffusion-Based Video Face Swapping
Authors: Sanoojan Baliah; Yohan Abeysinghe; Rusiru Thushara; Khan Muhammad; Abhinav Dhall; Karthik Nandakumar; Muhammad Haris Khan
Venue: WACV 2026 (main)

Abstract: We present a training-free, plug-and-play method, namely VFace, for high-quality face swapping in videos. It can be seamlessly integrated with image-based face swapping approaches built on diffusion models. First, we introduce a Frequency Spectrum Attention Interpolation technique to facilitate generation and intact key identity characteristics. Second, we achieve Target Structure Guidance via plug-and-play attention injection to better align the structural features from the target frame to the generation. Third, we present a Flow-Guided Attention Temporal Smoothening mechanism that enforces spatiotemporal coherence without modifying the underlying diffusion model to reduce temporal inconsistencies typically encountered in frame-wise generation. Our method requires no additional training or video-specific fine-tuning. Extensive experiments show that our method significantly enhances temporal consistency and visual fidelity, offering a practical and modular solution for video-based face swapping. Our code is available at https://github.com/Sanoojan/VFace.
CaRS: A Causal Intervention Segmentation Framework and Benchmark Dataset for Autonomous Driving under Transitional Weather Conditions
Authors: Kondapally Madhavi; K Naveen Kumar; C Krishna Mohan; Sobhan Babu
Venue: WACV 2026 (main)

Abstract: Autonomous vehicles must excel in safety-critical perception tasks, especially in adverse weather conditions. In addition, transitional weather shifts in nature, such as sunny to rainy, rainy to cloudy, etc., pose abrupt illumination changes that can distort object boundaries and degrade segmentation performance. Existing research focuses mainly on segmentation in clear and discrete weather conditions, leaving a gap in addressing the issues of transitional weather scenarios. Hence, we propose a novel method called causal road and rest segmentation (CaRS) that utilizes causal intervention to mitigate the confounding bias due to transitional weather changes. We use dual complementary attention modules, one for causal and another for confounding feature extraction. These modules complement each other and are fine-tuned via an adversarial min-max approach to reduce confounding bias and enhance segmentation performance. Also, our CaRS method concurrently performs road semantic segmentation and instance segmentation of vehicles and pedestrians. Further, we introduce a transitional weather-driving dataset for segmentation (TWDS16) using a spurious correlation generator that leverages data interpolation to produce 16 weather transitions. We evaluate the performance of CaRS on TWDS16, along with three other benchmark datasets, namely, Foggy Cityscapes, RainCityscapes, and BDD100K. The experimental results validate the efficacy of the proposed method in mitigating confounding influences, leading to improved mIoU for semantic segmentation and mAP for instance segmentation across diverse datasets
FreeCond: Free Lunch in the Input Conditions of Text-Guided Inpainting
Authors: Teng-Fang Hsiao; Bo-Kai Ruan; Sung-Lin Tsai; Yi-Lun Wu; Hong-Han Shuai
Venue: WACV 2026 (main)

Abstract: Text-to-image inpainting models often exhibit an unpredictable balance among image coherence and prompt adherence. This rigidity limits their adaptability across diverse scenarios, including coarse masks, non-object, and interaction prompts. Recognizing this instability as an indicator of learned generation diversity, we aim to control model behavior for given objective. We propose Empirical Feature Intervention (EFI), a metric-agnostic framework that precomputes how feature interventions influence evaluation metrics--such as CLIP, Human Preference Score (HPS), and Image Reward (IR). Building on EFI, we introduce FreeCond, a free-of-cost framework that applies two simple input interventions (Image Frequency and Mask Value Modulation), these interventions can be further optimized via Surrogate Intervention Optimization (SIO) based on a surrogate model regressed with precomputed EFI data. FreeCond enables real-time, user-interactive control of pre-trained models without retraining or architectural modifications. Also, to benchmark performance on challenging settings, we present FCIBench. Experiments on EditBench, BrushBench, and FCIBench demonstrate that FreeCond substantially improves CLIP, HPS, and IR metrics by up to 22%, 8%, and 54%, respectively.
Advancing Player Identification and Tracking with Global ID Fusion (GIF)
Authors: Karol Wojtulewicz; Minxing Liu; Niklas Carlsson
Venue: WACV 2026 (main)

Abstract: Rapid player motion, occlusions, substitutions, and jersey changes pose major challenges for identity-consistent tracking in sports. Existing multi-object tracking (MOT) methods struggle in long-term and multi-perspective settings like broadcast footage, where views change frequently. To address this, we first introduce MuPNIT, the first MOT and ReID benchmark to capture these multi-perspective dynamics along with long-term appearance variations across seasons, teams, and jersey changes. Second, we propose Global ID Fusion (GIF), a novel context-aware tracking-with-identification framework that enables robust tracking of both seen and unseen players. Unlike prior approaches, GIF performs single pass global ID association and supports zero-shot identity recognition. Our approach achieves state-of-the-art results, improving HOTA by 25.3% and IDF1 by 79.5% over OC-SORT. Finally, to assess identity consistency, we introduce five Global ID metrics that reveal tradeoffs in tracking stability. By bridging MOT and ReID, our work advances identity-aware player tracking in sports and sets a new benchmark with applications in sports analytics, surveillance, and long-term person search.
VRAgent: Self-Refining Agent for Zero-Shot Multimodal Video Retrieval
Authors: Ketul Shah; Pankaj Nathani; Rama Chellappa; Fabian Caba Heilbron
Venue: WACV 2026 (main)

Abstract: Recent advances in Vision-Language Models (VLMs) and Large Language Models (LLMs) have demonstrated remarkable zero-shot capabilities while becoming increasingly accessible. They have enabled zero-shot text-to-video search, yet they still struggle on real-world queries that demand temporally aligned reasoning across vision and speech. We present VRAgent, an agentic retrieval framework that leverages a central LLM as a planner which (i) decomposes a free-form user query into a tool-instruction set spanning visual, dialogue and other modality-specific retrievers, and (ii) iteratively self-refines this plan by scoring the outputs and rewriting the next tool-instruction set. The resulting closed-loop optimization acts at test time and requires no additional training data or gradient updates. VRAgent is modular by design--adding a new modality is as simple as adding a corresponding foundation model into the toolbox. On our newly proposed MM-MSRVTT and TVR-1200 multimodal benchmarks, VRAgent improves average recall by +8.3% and +1.7% over the best zero-shot baselines, while on single-modality MSR-VTT and DiDeMo it obtains consistent gains of +3.7% and +4.1%. An interactive variant that asks the user up to two multiple-choice questions pushes average recall to 79.7% on MSR-VTT, underscoring the value of on-the-fly human feedback.
TaxonRL: Reinforcement Learning with Intermediate Rewards for Interpretable Fine-Grained Visual Reasoning
Authors: Maximilian von Klinski; Maximilian Schall
Venue: WACV 2026 (main)

Abstract: Traditional vision-language models struggle with fine-grained taxonomic reasoning, particularly distinguishing between visually similar species within the same genus or family. We propose a reinforcement learning approach using Group Relative Policy Optimization with intermediate rewards that decompose the reasoning process into hierarchical taxonomic predictions. Our method incentivizes models to explicitly reason about species-level, genus-level, and family-level features before making final classifications. This structured approach is designed not only to boost accuracy but also to yield a transparent, verifiable decision-making process. On the challenging Birds-to-Words dataset, our approach achieves 91.7% accuracy on same-species verification, matching human performance (77.3%) while generating interpretable reasoning traces. We demonstrate cross-domain generalization showing substantial gains on primate verification while generating explainable traces. The intermediate reward mechanism shows that structured biological reasoning provides a powerful framework for fine-grained visual discrimination.
Revisiting Retentive Networks for Fast Range-View 3D LiDAR Semantic Segmentation
Authors: Simone Mosco; Daniel Fusaro; Wanmeng Li; Alberto Pretto
Venue: WACV 2026 (main)

Abstract: LiDAR semantic segmentation is a crucial task in autonomous driving and robotics, where real-time performance is essential for online decision-making. Recent trends exploit range images and Vision Transformers, using the self-attention mechanism. However, these approaches often lack explicit spatial priors and involve a large number of parameters. To tackle these limitations, we propose a novel method, adapting the Retentive Network architecture from the Natural Language Processing (NLP) field, for its efficient sequence modeling capabilities, directly operating on the range-view representation. Our approach incorporates a circular retention (CiR) mechanism that explicitly captures spatial relationships and continual circular property of the range image while modeling long-range dependencies and preserving the receptive field. In addition, we introduce a new set of range-view augmentations, adapted from 3D techniques, to improve generalization and mitigate class imbalance. Extensive experiments on three large-scale datasets, as SemanticKITTI, PandaSet and SemanticPOSS demonstrate that our method achieve state-of-the-art performance among range-view approaches on two out of three datasets, while satisfying real-time constraints. The code is available at https://github.com/SiMoM0/RangeRet.
Alignment and Distillation: A Robust Framework for Multimodal Domain Generalizable Human Action Recognition
Authors: Hyeonbin Ji; Juyeob Lee; Eunil Park
Venue: WACV 2026 (main)

Abstract: Human Action Recognition (HAR) in real-world scenarios is significantly challenged by unseen domain shifts, such as variations in the camera viewpoint, illumination, lighting, or background. Although recent advancements in video domain generalization are promising for HAR by introducing models that are robust to these shifts, existing methods often fall short. They typically depend on a single modality or employ static frame-level fusion approaches, which inherently limit the capture of multi-scale temporal dependencies and the alignment of the asynchronous modalities frequently present in video data. To address these limitations, we propose Multimodal Alignment and Distillation for Domain Generalization (MAD-DG), a novel framework that synchronizes asynchronous modalities through a segment-label aligned temporal binding window with a contrastive alignment mechanism. We further incorporate an online self-distillation temporal module that captures multi-scale temporal relationships and learns robust, domain-invariant representations. Extensive experiments demonstrate that MAD-DG achieves state-of-the-art performance and exhibits better generalization capabilities across both single- and multi-source domain generalization settings. Our source code is available at https://github.com/dxlabskku/MAD-DG.
GraspDiffusion: Synthesizing Realistic Whole-body Hand-Object Interaction
Authors: Patrick Kwon; Chen Chen; Hanbyul Joo
Venue: WACV 2026 (main)

Abstract: Recent generative models can synthesize high-quality images but often fail to generate humans interacting with objects using their hands. This arises mostly from the model's misunderstanding of such interactions, and the hardships of synthesizing intricate regions of the body. In this paper, we propose GraspDiffusion, a novel generative method that creates realistic scenes of human-object interaction. Given a 3D object mesh, GraspDiffusion first constructs life-like whole-body poses with control over the object's location relative to the human body. This is achieved by separately leveraging the generative priors for 3D body and hand poses, optimizing them into a joint grasping pose. The resulting pose guides the image synthesis to correctly reflect the intended interaction, allowing the creation of realistic and diverse human-object interaction scenes. We demonstrate that GraspDiffusion can successfully tackle the relatively uninvestigated problem of generating full-bodied human-object interactions while outperforming previous methods.
Understanding Human-Like Biases in VLMs via Subjective Face Analytics
Authors: Chaitanya Roygaga; Aparna Bharati
Venue: WACV 2026 (main)

Abstract: Vision-Language Models (VLMs) effectively integrate visual and textual information, often relying on shared embedding spaces to align modalities. However, the extent to which these spaces capture complex, subjective human judgments, such as perceived facial trustworthiness and attractiveness, and whether they replicate associated human social biases, remains underexplored. This paper investigates the representation of subjective face attributes within Multimodal LLM and VLM embedding spaces, examining whether these representations encode human-like biases and assessing their interpretability. Using probing techniques on face datasets annotated with human judgments, we analyze the structure of VLM embeddings (e.g., from CLIP-like models). Our findings demonstrate that similarity scores between face image and textual description in the VLM embedding space align with human ratings of subjective attributes like trustworthiness and attractiveness, and crucially, these representations exhibit correlations and demographic disparities mirroring known biases in human social perception. Furthermore, we show that the use of variable context via face and attribute-specific captions can significantly improve the alignment of the VLM embedding space with human impressions. Interpreting the embedded social biases highlights the need for critical evaluation and bias-aware development of VLMs to mitigate the risk of perpetuating harmful stereotypes in downstream applications that involve Human-AI interaction.
Correcting and Quantifying Systematic Errors in 3D Box Annotations for Autonomous Driving
Authors: Alexandre Justo Miro; Ludvig af Klinteberg; Bogdan Timus; Aron Asefaw; Ajinkya Khoche; Thomas Gustafsson; Sina Sharif Mansouri; Masoud Daneshtalab
Venue: WACV 2026 (main)

Abstract: Accurate annotations are critical to providing ground truth to supervised learning and to evaluating the performance of autonomous vehicle systems. These vehicles are typically equipped with active sensors, such as LiDAR, which scan the environment in predefined patterns. 3D box annotation based on data from such sensors is challenging in dynamic scenarios, where objects are observed at different timestamps, hence different positions. Without proper handling of this phenomenon, systematic errors are prone to being introduced in the annotations.Our work is the first one to describe why such annotation errors occur and illustrates them using examples from widely used, publicly available datasets. Through our novel estimation method, we correct the annotations so that they follow physically feasible trajectories and achieve spatial and temporal consistency with the sensor data. For the first time, we define metrics for this problem; and we evaluate our method on the Argoverse 2 and MAN TruckScenes datasets, as well as in our proprietary dataset. Our approach demonstrates robust performance in increasing the quality of ground truth by more than 17% in these datasets. Finally, we quantify the annotation errors in them and find that the original annotations are misplaced by up to 2.5 m, with highly dynamic objects being the most affected. Our code is available at https://github.com/alexandre-justo-miro/annotation-correction-3D-boxes.
CaFlow: Enhancing Long-Term Action Quality Assessment with Causal Counterfactual Flow
Authors: Ruisheng Han; Kanglei Zhou; Shuang Chen; Amir Atapour-Abarghouei; Hubert P. H. Shum
Venue: WACV 2026 (main)

Abstract: Action Quality Assessment (AQA) predicts fine-grained execution scores from action videos and is widely applied in sports, rehabilitation, and skill evaluation. Long-term AQA, as in figure skating or rhythmic gymnastics, is especially challenging since it requires modeling extended temporal dynamics while remaining robust to contextual confounders. Existing approaches either depend on costly annotations or rely on unidirectional temporal modeling, making them vulnerable to spurious correlations and unstable long-term representations. To this end, we propose CaFlow, a unified framework that integrates counterfactual de-confounding with bidirectional time-conditioned flow. To address the issue of spurious correlations, the Causal Counterfactual Regularization (CCR) module disentangles causal and confounding features in a self-supervised manner and enforces causal robustness through counterfactual interventions. To tackle unstable temporal refinement in long sequences, the BiT-Flow module explicitly models forward and backward dynamics with a cycle-consistency constraint, producing smoother and more coherent representations. Extensive experiments on multiple long-term AQA benchmarks demonstrate that CaFlow achieves state-of-the-art performance, validating its effectiveness.