GenAI4ELab/papercli-papers huggingface dataset:
M-ErasureBench: A Comprehensive Multimodal Evaluation Benchmark for Concept Erasure in Diffusion Models
Authors: Ju-Hsuan Weng; Jia-Wei Liao; Cheng-Fu Chou; Jun-Cheng Chen
Venue: WACV 2026 (main)

Abstract: Text-to-image diffusion models may generate harmful or copyrighted content, motivating research on concept erasure. However, existing approaches primarily focus on erasing concepts from text prompts, overlooking other input modalities that are increasingly critical in real-world applications such as image editing and personalized generation. These modalities can become attack surfaces, where erased concepts re-emerge despite defenses. To bridge this gap, we introduce M-ErasureBench, a novel multimodal evaluation framework that systematically benchmarks concept erasure methods across three input modalities: text prompts, learned embeddings, and inverted latents. For the latter two, we evaluate both white-box and black-box access, yielding five evaluation scenarios. Our analysis shows that existing methods achieve strong erasure performance against text prompts but largely fail under learned embeddings and inverted latents, with Concept Reproduction Rate (CRR) exceeding 90% in the white-box setting. To address these vulnerabilities, we propose IRECE (Inference-time Robustness Enhancement for Concept Erasure), a plug-and-play module that localizes target concepts via cross-attention and perturbs the associated latents during denoising. Experiments demonstrate that IRECE consistently restores robustness, reducing CRR by up to 40% under the most challenging white-box latent inversion scenario, while preserving visual quality. To the best of our knowledge, M-ErasureBench provides the first comprehensive benchmark of concept erasure beyond text prompts. Together with IRECE, our benchmark offers practical safeguards for building more reliable protective generative models.
Bridging the Domain Gap in Small Multimodal Models: A Dual-level Alignment Perspective
Authors: Aveen Dayal; Peketi Divya; Nidhi Tiwari; Linga Reddy Cenkeramaddi; C Krishna Mohan; Abhinav Kumar
Venue: WACV 2026 (main)

Abstract: Small Multimodal Models (SMMs) suffer under distribution shift after fine-tuning. Unsupervised Domain Adaptation (UDA) is a common remedy for this issue, but existing theory and methods are designed primarily for single- or dual-encoder architectures, overlooking the encoder-decoder structure of SMMs, whose fusion mechanism introduces additional shift. This work bridges this gap in two steps. First, we derive a dual-divergence risk bound that separates encoder divergence from fusion divergence and illustrate its tightness compared to the classical encoder-only bound with a negation-flip example. Second, motivated by this theory, we propose Dual-level Adversarial Alignment (DuAA), a two-stage alignment algorithm. DuAA inserts domain-discriminative adapters after the encoder and within the decoder to minimize both divergences. Furthermore, DuAA employs selective pseudo-labeling to refine target semantics. Our contribution targets domain shift in encoder-decoder SMMs and is agnostic to the fine-tuning mechanism, i.e., DuAA acts on internal representations, making it orthogonal to LoRA, or other fine-tuning variants. We adopt LoRA in experiments solely as a popular, parameter-efficient instantiation to keep the protocol fixed across settings. We compile twelve new cross-domain VQA tasks with distinct visual and textual shifts from existing datasets and observe that DuAA consistently outperforms standard fine-tuning across all tasks.
Leveraging Pretrained Representations for Cross-Modal Point Cloud Completion
Authors: Kshitij Kale; Hrishikesh U; V sreenidhe; Shylaja S S
Venue: WACV 2026 (main)

Abstract: The utility of 3D point clouds in critical applications like robotics is often hindered by their inherent incompleteness, a result of real-world occlusions and limited sensor viewpoints. To overcome this, image-guided 3D point cloud completion aims to reconstruct complete shapes by leveraging a corresponding 2D image. However, current methods typically train a cross-modal network from scratch, often failing to capture the high-level semantic context and complex structural information required for robust reconstruction. This paper challenges that paradigm by demonstrating that preexisting knowledge from large-scale, pretrained vision models can be effectively leveraged to guide the completion process. We introduce a novel Dual Branch Image Encoder, a dedicated module designed to extract and fuse rich semantic priors from a pretrained Vision Transformer with geometric depth cues. This fused representation provides a powerful, multifaceted guide that is integrated into EGIInet, a state-of-the-art point cloud completion network. Our experiments show that by conditioning the completion on these strong, pretrained priors, our method outperforms existing state-of-the-art techniques by 7% without changing the rest of the architecture, producing more semantically coherent and structurally accurate 3D shapes.
Gated Temporal Fusion Transformers for Robust Multi-Object Tracking
Authors: Jinho Kim; Kuk-Jin Yoon
Venue: WACV 2026 (main)

Abstract: Multiple Object Tracking (MOT) in dynamic and densely populated scenes presents significant challenges due to frequent occlusions, erratic object motion, and identity switches. While recent Transformer-based approaches have successfully leveraged global attention for object detection, most rely on temporal reasoning at the decoder level, leaving encoder-stage modeling underexplored. In this work, we propose an encoder-level temporal reasoning Transformer framework that embeds historical object trajectory information into the encoder stage via a tracklet memory. The encoder module, enhanced by Attention-by-Tracking, enriches visual features with temporal priors, while the decoder leverages Tracking-by-Attention to guide identity association using learned tracklet representations. To further improve temporal consistency and object localization, we introduce a gating-based temporal feature fusion mechanism that adaptively integrates multi-frame features based on cosine similarity. Our method is model-agnostic and can be applied to various Transformer-based MOT frameworks. When integrated into existing models such as TransTrack, MeMOTR, and MOTIP, it yields consistent performance improvements. Extensive experiments on DanceTrack and SportsMOT benchmarks demonstrate that our approach achieves superior tracking performance, including a HOTA score of 76.4 on SportsMOT. These results validate the effectiveness of encoder-level temporal integration and adaptive feature fusion for robust multi-object tracking in real-world scenarios.
Moire Zero: An Efficient and High-Performance Neural Architecture for Moire Removal
Authors: Seungryong Lee; Woojeong Baek; Younghyun Kim; Eunwoo Kim; Haru Moon; Donggon Yoo; Eunbyung Park
Venue: WACV 2026 (main)

Abstract: Moire patterns, caused by frequency aliasing between fine repetitive structures and a camera sensor's sampling process, have been a significant obstacle in various real-world applications, such as consumer photography and industrial defect inspection. With the advancements in deep learning algorithms, numerous studies-predominantly based on convolutional neural networks-have suggested various solutions to address this issue. Despite these efforts, existing approaches still struggle to effectively eliminate artifacts due to the diverse scales, orientations, and color shifts of moire patterns, primarily because the constrained receptive field of CNN-based architectures limits their ability to capture the complex characteristics of moire patterns. In this paper, we propose MZNet, a U-shaped network designed to bring images closer to a 'Moire-Zero' state by effectively removing moire patterns. It integrates three specialized components: Multi-Scale Dual Attention Block (MSDAB) for extracting and refining multi-scale features, Multi-Shape Large Kernel Convolution Block (MSLKB) for capturing diverse moire structures, and Feature Fusion-Based Skip Connection for enhancing information flow. Together, these components enhance local texture restoration and large-scale artifact suppression. Experiments on benchmark datasets demonstrate that MZNet achieves state-of-the-art performance on high-resolution datasets and delivers competitive results on lower-resolution dataset, while maintaining a low computational cost, suggesting that it is an efficient and practical solution for real-world applications.
DermEVAL: A Dermatologist-Reviewed Benchmark for Multimodal Large Language Models
Authors: Hongjin Zhao; Weihao Li; Zhenyue Qin; Ge-Peng Ji; Yang Liu; Tom Gedeon; Nick Barnes
Venue: WACV 2026 (main)

Abstract: Clinical photographs play a vital role in conversational computer-aided diagnosis, particularly in dermatology. However, existing skin disease benchmarks contain limitations like insufficient dataset size, the sole presence of categorical labels, the lack of expert inspections, and limited diversity in annotations. To address these shortcomings, we introduce DermEVAL, a large-scale benchmark specifically designed to evaluate the performance of Multimodal Large Language Models (MLLMs) in dermatology. Our benchmark includes image-text pairs depicting 16 distinct skin diseases, featuring a total of 11,347 representative images drawn from various dermatological datasets, carefully selected and annotated with the guidance of dermatologists. DermEVAL enables two primary tasks: visual question answering (VQA) and medical report generation (MRG), designed to simulate real-world medical diagnostics. We evaluate the performance of MLLMs in dermatology using multiple metrics, including traditional metrics and GPT-4V-based assessments. Our results indicate that accurately diagnosing skin diseases remains challenging for state-of-the-art MLLMs. We also demonstrate that fine-tuning MLLMs using DermEVAL significantly improves their performance on dermatology-related image-text tasks.
GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model -- Bringing Motion Generation to the Clinical Domain
Authors: Vida Adeli; Soroush Mehraban; Majid Mirmehdi; Alan Whone; Benjamin Filtjens; Amirhossein Dadashzadeh; Alfonso Fasano; Andrea Iaboni; Babak Taati
Venue: WACV 2026 (main)

Abstract: Gait analysis is crucial for the diagnosis and monitoring of movement disorders like Parkinson's Disease. While computer vision models have shown potential for objectively evaluating parkinsonian gait, their effectiveness is limited by scarce clinical datasets and the challenge of collecting large and well-labelled data, impacting model accuracy and risk of bias.To address these gaps, we propose GAITGen, a novel framework that generates realistic gait sequences conditioned on specified pathology severity levels. GAITGen employs a Conditional Residual Vector Quantized Variational Autoencoder to learn disentangled representations of motion dynamics and pathology-specific factors, coupled with Mask and Residual Transformers for conditioned sequence generation. GAITGen generates realistic, diverse gait sequences across severity levels, enriching datasets and enabling large-scale model training in parkinsonian gait analysis. Experiments on our new PD-GaM (real) dataset demonstrate that GAITGen outperforms adapted state-of-the-art models in both reconstruction fidelity and generation quality, accurately capturing critical pathology-specific gait features. A clinical user study confirms the realism and clinical relevance of our generated sequences. Moreover, incorporating GAITGen-generated data into downstream tasks improves parkinsonian gait severity estimation, highlighting its potential for advancing clinical gait analysis.
Perceptually Guided 3DGS Streaming and Rendering for Mixed Reality
Authors: Yunxiang Zhang; Sai Harsha Mupparaju; Kenneth Chen; Jenna Kang; Xinyu Zhang; Maito Omori; Kazuyuki Arimatsu; Qi Sun
Venue: WACV 2026 (main)

Abstract: Recent breakthroughs in radiance fields, particularly 3D Gaussian Splatting (3DGS), have unlocked real-time, high-quality rendering of complex environments, enabling a wide range of applications. However, the stringent requirements of mixed reality (MR) rendering, such as rapid refresh rates, high-resolution stereo viewing, and constrained computing budgets, remain out of reach for current 3DGS techniques. Nevertheless, the wide field-of-view design of MR displays, which mimics human vision, presents a unique opportunity to exploit human visual system's own perceptual limitations to reduce computational overhead while not compromising user-perceived rendering quality. To this end, we propose a perception-guided, continuous level-of-detail (LOD) framework for 3DGS that maximizes perceived quality under given compute resources. We distill a visual quality metric, which encodes the spatial, temporal, and peripheral characteristics of human visual perception, into a lightweight, gaze-contingent model that predicts and adaptively modulates rendering LOD across the visual field based on each region's contributions to perceptual quality. This budget-driven LOD modulation, guided by both scene content and gaze behavior, enables significant computation reduction with minimal loss in perceived quality. To support low-power, untethered MR setups, we design an edge-cloud collaborative rendering framework to partially offload computation to the cloud, further reducing overhead on the edge MR devices. Objective metrics and MR user study evidence that, compared to vanilla and foveated LOD baselines, our method achieves superior trade-offs between computational efficiency and user-perceived visual quality.
RAVU: Retrieval Augmented Video Understanding with Compositional Reasoning over Graph
Authors: Sameer Malik; Ayush Singh; Moyuru Yamada; Dishank Aggarwal
Venue: WACV 2026 (main)

Abstract: Comprehending long videos remains a significant challenge for Large Multi-modal Models (LMMs). Current LMMs struggle to process even minutes to hours videos due to their lack of explicit memory and retrieval mechanisms. To address this limitation, we propose RAVU (Retrieval Augmented Video Understanding), a novel framework for video understanding enhanced by retrieval with compositional reasoning over a spatio-temporal graph. We construct a graph representation of the video, capturing both spatial and temporal relationships between entities. This graph serves as a long-term memory, allowing us to track objects and their actions across time. To answer complex queries, we decompose the queries into a sequence of reasoning steps and execute these steps on the graph, retrieving relevant key information. Our approach enables more accurate understanding of long videos, particularly for queries that require multi-hop reasoning and tracking objects across frames. Our approach demonstrate superior performances with limited retrieved frames (5-10) compared with other SOTA methods and baselines on two major video QA datasets, NExT-QA and EgoSchema.
Style-Friendly SNR Sampler for Style-Driven Generation
Authors: Jooyoung Choi; Chaehun Shin; Yeongtak Oh; Heeseung Kim; Jungbeom Lee; Sungroh Yoon
Venue: WACV 2026 (main)

Abstract: Recent text-to-image diffusion models generate high-quality images but struggle to learn new styles, which limits the personalized content creation. In response, style-driven generation has become a popular task, wherein users supply reference images capturing the target style, complemented by text prompts that specify stylistic cues. Fine-tuning is a common approach, yet it often blindly utilizes pre-training configurations without modification, especially for noise schedules defined in terms of signal-to-noise ratio (SNR), which determines the amount of image information available at each denoising step. We discover that stylistic features predominantly emerge at low SNR range, leading current fine-tuning methods using regular noise schedules to exhibit suboptimal style alignment. We propose the Style-friendly SNR sampler, which focuses the fine-tuning on low SNR range where stylistic features emerge. We demonstrate improved generation of novel styles that cannot be described solely with a text prompt, enabling high-fidelity personalized content creation.
Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification
Authors: Taha Mustapha Nehdi; Nairouz Mrabah; Atif Belal; Marco Pedersoli; Eric Granger
Venue: WACV 2026 (main)

Abstract: Adapting person re-identification (reID) models to new target environments remains a challenging problem that is typically addressed using unsupervised domain adaptation (UDA) methods. Recent works show that when labeled data originates from several distinct sources (for example, datasets and cameras), considering each source separately and applying multi-source domain adaptation (MSDA) typically yields higher accuracy and robustness compared to blending the sources and performing conventional UDA. However, state-of-the-art MSDA methods learn domain-specific backbone models or require access to source domain data during adaptation, resulting in significant growth in training parameters and computational cost. In this paper, a Source-free Adaptive Gated Experts (SAGE-reID) method is introduced for person reID. SAGE-reID is a cost-effective, source-free MSDA method that first trains individual source-specific low-rank adapters (LoRA) through source-free UDA. Next, a lightweight gating network is introduced and trained to dynamically assign optimal merging weights for the fusion of LoRA experts, enabling effective cross-domain knowledge transfer. While the number of backbone parameters remains constant across source domains, LoRA experts scale linearly but remain negligible in size (less than or equal to 2 percent per source), reducing both memory consumption and the risk of overfitting. Extensive experiments conducted on three challenging benchmarks -- Market-1501, DukeMTMC-reID, and MSMT17 -- indicate that SAGE-reID outperforms state-of-the-art methods while being computationally efficient.
ChartQA-X: Generating Explanations for Visual Chart Reasoning
Authors: Shamanthak Hegde; Pooyan Fazli; Hasti Seifi
Venue: WACV 2026 (main)

Abstract: The ability to explain complex information from chart images is vital for effective data-driven decision-making. In this work, we address the challenge of generating detailed explanations alongside answering questions about charts. We present ChartQA-X, a comprehensive dataset comprising 30,799 chart samples across four chart types, each paired with contextually relevant questions, answers, and explanations. Explanations are generated and selected based on metrics such as faithfulness, informativeness, coherence, and perplexity. Our human evaluation with 245 participants shows that model-generated explanations in ChartQA-X surpass human-written explanations in accuracy and logic and are comparable in terms of clarity and overall quality. Moreover, models fine-tuned on ChartQA-X show substantial improvements across various metrics, including absolute gains of up to 24.57 points in explanation quality, 18.96 percentage points in question-answering accuracy, and 14.75 percentage points on unseen benchmarks for the same task. By integrating explanatory narratives with answers, our approach enables agents to convey complex visual information more effectively, improving comprehension and greater trust in the generated responses.
BOP-Distrib: Revisiting 6D Pose Estimation Benchmarks for Better Evaluation under Visual Ambiguities
Authors: Boris Meden; Asma Brazi; Fabrice Mayran de Chamisso; Steve Bourgeois; Vincent Lepetit
Venue: WACV 2026 (main)

Abstract: 6D pose estimation aims at determining the object pose that best explains the camera observation. The unique solution for non-ambiguous objects can turn into a multi-modal pose distribution for symmetrical objects or when occlusions of symmetry-breaking elements happen, depending on the viewpoint.Currently, 6D pose estimation methods are benchmarked on datasets that consider, for their ground truth annotations, visual ambiguities as only related to global object symmetries, whereas they should be defined per-image to account for the camera viewpoint. We thus first propose an automatic method to re-annotate those datasets with a 6D pose distribution specific to each image, taking into account the object surface visibility in the image to correctly determine the visual ambiguities. Second, given this improved ground truth, we re-evaluate the state-of-the-art single pose methods and show that this greatly modifies the ranking of these methods. Third, as some recent works focus on estimating the complete set of solutions, we derive a precision/recall formulation to evaluate them against our image-wise distribution ground truth, making it the first benchmark for pose distribution methods on real images.
SPOC: Spatially-Progressing Object State Change Segmentation in Video
Authors: Priyanka Mandikal; Tushar Nagarajan; Alex Stoken; Zihui Xue; Kristen Grauman
Venue: WACV 2026 (main)

Abstract: Object state changes in video reveal critical cues about human and agent activity. However, existing methods are limited to temporal localization of when the object is in its initial state (e.g., cheese block) versus when it has completed a state change (e.g., grated cheese), offering no insight into where the change is unfolding. We propose to deepen the problem by introducing the spatially-progressing object state change segmentation task. The goal is to segment at the pixel-level those regions of an object that are actionable and those that are transformed. We show that state-of-the-art VLMs and video segmentation methods struggle at this task, underscoring its difficulty and novelty. As an initial baseline, we design a VLM-based pseudo-labeling approach, state-change dynamics constraints, and a novel WhereToChange benchmark built on in-the-wild Internet videos. Experiments on two datasets validate both the challenge of the new task as well as the promise of our model for localizing exactly where and how fast objects are changing in video. We further demonstrate useful implications for tracking activity progress to benefit robotic agents. Overall, our work positions spatial OSC segmentation as a new frontier task for video understanding: one that challenges current SOTA methods and invites the community to build more robust, state-change-sensitive representations.
RealDroneVision: Dataset and Architecture Advancements for Small-Object Drone Detection
Authors: Arun Kumar Sivapuram; Pranav R T Peddinti; Harish Puppala; Komuravelli Prashanth; Jaladi Sri Harsha; Rama Krishna Sai Gorthi
Venue: WACV 2026 (main)

Abstract: Drones are increasingly used in civilian and defense domains, but reliable detection remains challenging due to their small size, fast motion, and diverse environments. Existing datasets, such as synthetic benchmarks, fail to capture real-world variability. We introduce RealDroneVision, a unified contribution that advances both dataset and methodology. First, we curate a large-scale real-world drone detection dataset comprising 173,023 images, constructed via a semi-automatic pipeline inspired by self-annotated labeling from videos, enhanced with a human-in-the-loop to iteratively reduce false positives and false negatives. This approach yields high-quality annotations with reduced manual effort. Second, we propose the Nano Object Vision Attention (NOVA) module, a drop-in replacement for YOLOv8's C2f block. By combining depthwise separable convolutions, scale-aware dilated branches, lightweight mixing, and coordinate-aware attention, our design improves small-object detection while remaining computationally efficient. Extensive benchmarks against YOLOv8m/l and YOLOv9c/e demonstrate that YOLOv8-NOVA dominates across precision (0.912), recall (0.870), and mAP@50 (0.920) while being significantly more lightweight (2.3M params, 5 MB weights). These results establish RealDroneVision as a strong foundation for advancing real-world drone detection research.