GenAI4ELab/papercli-papers huggingface dataset:
AusSmoke meets MultiNatSmoke: a fully-labelled diverse smoke segmentation dataset
Authors: Weihao Li; Hongjin Zhao; Gao Zhu; Ge-Peng Ji; Nicholas Wilson; Marta Yebra; Nick Barnes
Venue: WACV 2026 (main)

Abstract: Wildfires are an escalating global concern due to the devastating impacts on the environment, economy, and human health, with notable incidents such as the 2019-2020 Australian bushfires and the 2025 California wildfires underscoring the severity of these events. AI-enabled camera-based smoke detection has emerged as a promising approach for the rapid detection of wildfires. However, existing wildfire smoke segmentation datasets that are used for training detection and segmentation models are limited in scale, geographically constrained, and often rely on synthetic imagery, which hinders effective training and generalization. To overcome these limitations, we present AusSmoke, a new smoke segmentation dataset collected from Australia to address the data scarcity in this region. Furthermore, we introduce a MultiNational geographically diverse and substantially larger fully-labelled benchmark, called MultiNatSmoke, that consolidates publicly available international datasets with the newly collected Australian imagery, expanding the scale by an order of magnitude over previous collections. Finally, we benchmark smoke segmentation models, demonstrating improved performance and enhanced generalization across diverse geographical contexts.
MVAT: Multi-View Aware Teacher for Weakly Supervised 3D Object Detection
Authors: Saad Lahlali; Alexandre Fournier-Mongieux; Nicolas Granger; Hervé Le Borgne; Quoc-Cuong Pham
Venue: WACV 2026 (main)

Abstract: Annotating 3D data remains a costly bottleneck for 3D object detection, motivating the development of weakly supervised annotation methods that rely on more accessible 2D box annotations. However, relying solely on 2D boxes introduces projection ambiguities since a single 2D box can correspond to multiple valid 3D poses. Furthermore, partial object visibility under a single viewpoint setting makes accurate 3D box estimation difficult. We propose MVAT, a novel framework that leverages temporal multi-view present in sequential data to address these challenges. Our approach aggregates object-centric point clouds across time to build 3D object representations as dense and complete as possible. A Teacher-Student distillation paradigm is employed: The Teacher network learns from single viewpoints but targets are derived from temporally aggregated static objects. Then the Teacher generates high quality pseudo-labels that the Student learns to predict from a single viewpoint for both static and moving objects. The whole framework incorporates a multi-view 2D projection loss to enforce consistency between predicted 3D boxes and all available 2D annotations. Experiments on the nuScenes and Waymo Open datasets demonstrate that MVAT achieves state-of-the-art performance for weakly supervised 3D object detection, significantly narrowing the gap with fully supervised methods without requiring any 3D box annotations.
DreamCatcher: Efficient Multi-Concept Customization via Representation Finetuning
Authors: Jungwon Lee; Changhun Lee; Eunhyeok Park
Venue: WACV 2026 (main)

Abstract: Recent advances in customizing Text-to-Image models allow users to generate personalized images with just a few samples. As demand for multi-concept generation grows, methods using weight fusion and test-time optimization have emerged, integrating multiple concepts within a single image. However, these approaches inject concept knowledge into the parametric space, leading to high overhead in multi-concept generation. We introduce DreamCatcher, an efficient framework based on representation finetuning. Our key innovation embeds conceptual information into the feature space, achieving up to 5x faster multi-concept generation while reducing learnable storage per concept by 88%, all without quality loss. Besides, our method is highly versatile, enabling personalized inpainting without additional training.
GorillaWatch: An Automated System for In-the-Wild Gorilla Re-Identification and Population Monitoring
Authors: Maximilian Schall; Felix Leonard Knöfel; Noah Elias König; Jan Jonas Kubeler; Maximilian von Klinski; Joan Wilhelm Linnemann; Xiaoshi Liu; Iven Jelle Schlegelmilch; Ole Woyciniuk; Alexandra Schild; Dante Wasmuht; Magdalena Bermejo Espinet; German Illera Basas; Gerard de Melo
Venue: WACV 2026 (main)

Abstract: Monitoring critically endangered Western Lowland Gorillas is currently hampered by the immense manual effort required to re-identify individuals from vast archives of camera trap footage. The primary obstacle to automating this process has been the lack of large-scale, "in-the-wild" video datasets suitable for training robust deep learning models. To address this gap, we introduce a comprehensive benchmark with three novel datasets: Gorilla-SPAC-Wild, the largest video dataset for wild primate re-identification to date; Gorilla-Berlin-Zoo, for assessing cross-domain re-identification generalization; and Gorilla-SPAC-MoT, for evaluating multi-object tracking in camera trap footage. Building on these datasets, we present GorillaWatch, an end-to-end pipeline integrating detection, tracking, and re-identification. To exploit temporal information, we introduce a multi-frame self-supervised pretraining strategy that leverages consistency in tracklets to learn domain-specific features without manual labels. To ensure scientific validity, a differentiable adaptation of AttnLRP verifies that our model relies on discriminative biometric traits rather than background correlations. Extensive benchmarking subsequently demonstrates that aggregating features from large-scale image backbones outperforms specialized video architectures. Finally, we address unsupervised population counting by integrating spatiotemporal constraints into standard clustering to mitigate over-segmentation. We publicly release all code and datasets to facilitate scalable, non-invasive monitoring of endangered species.
QCFace: Image Quality Control for boosting Face Representation & Recognition
Authors: Duc-Phuong Doan-Ngo; Thanh-Dang Diep; Thanh Nguyen-Duc; Thanh-Sach LE; Nam Thoai
Venue: WACV 2026 (main)

Abstract: Recognizability, a key perceptual factor in human face processing, strongly affects the performance of face recognition (FR) systems in both verification and identification tasks. Effectively using recognizability to enhance feature representation remains challenging. In deep FR, the loss function plays a crucial role in shaping how features are embedded. However, current methods have two main drawbacks: (i) recognizability is only partially captured through soft margin constraints, resulting in weaker quality representation and lower discrimination, especially for low-quality or ambiguous faces; (ii) mutual overlapping gradients between feature direction and magnitude introduce undesirable interactions during optimization, causing instability and confusion in hypersphere planning, which may lead to poor generalization, and entangled representations where recognizability and identity are not cleanly separated. To address these issues, we introduce a hard margin strategy - Quality Control Face (QCFace), which overcomes the mutual overlapping gradient problem and enables the clear decoupling of recognizability from identity representation. Based on this strategy, a novel hard-margin-based loss function employs a guidance factor for hypersphere planning, simultaneously optimizing for recognition ability and explicit recognizability representation. Extensive experiments confirm that QCFace not only provides robust and quantifiable recognizability encoding but also achieves state-of-the-art performance in both verification and identification benchmarks compared to existing recognizability-based losses.
CLoCKDistill: Consistent Location and Context aware Knowledge Distillation for DETRs
Authors: Qizhen Lan; Qing Tian
Venue: WACV 2026 (main)

Abstract: Object detection has advanced significantly with Detection Transformers (DETRs). However, these models are computationally demanding, posing challenges for deployment in resource-constrained environments (e.g., self-driving cars). Knowledge distillation (KD) is an effective compression method widely applied to CNN detectors, but its application to DETR models has been limited. Most KD methods for DETRs fail to distill transformer-specific global context. Also, they blindly believe in the teacher model, which can sometimes be misleading. To bridge the gaps, this paper proposes Consistent Location-and-Context-aware Knowledge Distillation (CLoCKDistill) for DETR detectors, which includes both feature distillation and logit distillation components. For feature distillation, instead of distilling backbone features like many existing KD methods, we distill the transformer encoder output (i.e., memory) that contains valuable global context and long-range dependencies. Also, we enrich this memory with object location details during feature distillation so that the student model can prioritize relevant regions. To facilitate logit distillation, we create target-aware queries based on the ground truth, allowing both the student and teacher decoders to attend to consistent and accurate parts of encoder memory. Experiments on KITTI and COCO show our CLoCKDistill method's efficacy across various DETRs. Our method boosts student detector performance by 2.2% to 6.4%. Our code is available at: https://github.com/lanqz7766/CLoCKDistill.
False Alarm Rectification for Early Smoke Segmentation
Authors: Hongjin Zhao; Weihao Li; Ge-Peng Ji; Nick Barnes
Venue: WACV 2026 (main)

Abstract: Early smoke segmentation plays a critical role in forest protection and industrial safety. With the increasing deployment of fixed cameras and drones, vision-based smoke detection has become widely adopted. However, in open environments, smoke is easily confused with visually similar phenomena such as clouds, fog, and water vapour, leading to frequent false positives. To address this challenge, we propose SmokeCPC, a framework composed of a Confidence-Aware Prior Correction Module (CPCM) and a Cross-scale Fusion Module (CFM) that explicitly targets false alarm suppression in pixel-level smoke segmentation while preserving overall detection performance. The core idea of our method is to leverage the confidence of an image-level smoke classifier as a prior to guide both training and inference of the segmentation model. Building on this idea, CPCM strengthens supervision for high-confidence samples to enhance discriminative capability, while down-weighting low-confidence samples to mitigate noise propagation. In parallel, CFM integrates texture and semantic cues across feature hierarchies, improving robustness to thin smoke plumes and complex backgrounds. We further incorporate a supervised contrastive loss to encourage intra-class compactness and inter-class separability in the feature space. Overall, our method reduces the false positive rate without sacrificing segmentation quality. Experiments on the SmokeSeg dataset demonstrate the effectiveness of our approach, achieving an IoU of 61.83% and an FPR of only 0.28%.
NAPP: Noise-Adaptive Prototype Perturbation for Few-Shot Learning
Authors: Ilhwan Kim; Sangwoo Yun; Dongheon Lee; Seongsu Kim; Joonki Paik
Venue: WACV 2026 (main)

Abstract: Few-shot learning aims to generalize deep models to novel categories with only a handful of labeled examples, but existing methods remain vulnerable to task-irrelevant noise, unstable prototype estimation, and limited adaptability under domain shift. To address these issues, we propose the Noise-Adaptive Prototype Perturbation Network (NAPP), a framework that enhances robustness and generalization for few-shot learning. NAPP introduces three key innovations: (1) a Noise Cancellation Mechanism embedded in Vision Transformer self-attention layers that dynamically suppresses spurious, task-irrelevant features. (2) a MixPerturbation Module that perturbs class prototypes through augmented feature combinations, producing more stable and transferable prototype representations. (3) an Adaptive Noise-Conditioned Meta-Learning scheme that finetunes less than 0.02% of noise-related parameters at metatest time, enabling efficient and rapid adaptation to unseen classes without eroding pretrained knowledge. Extensive experiments demonstrate that NAPP achieves competitive and superior performance compared to state-of-the-art fewshot classification methods across both in-domain and challenging cross-domain benchmarks.
MemeTAG: Keyword-Driven Meme Classification through Tag Embedding Reconstruction
Authors: Akshit Sharma; Prashant W Patil
Venue: WACV 2026 (main)

Abstract: The proliferation of harmful internet memes poses a significant societal threat, yet their automated classification remains a formidable algorithmic challenge due to the nuanced, multimodal nature of their content. To address this, we introduce MemeTAG, a novel dual objective framework that pioneers a keyword-aware approach to meme classification. Our core innovation is a two-part semantic guid-ance mechanism: first, we leverage a pretrained Vision-Language Model to generate a set of descriptive keywords, that capture the high-level semantics. Second, we introduce the Aggregated Tag Inference Network (ATIN), an attention-based module that distills these keywords into a single, rich semantic embedding. This embedding servesas a target for a novel auxiliary reconstruction loss, which compels the model to learn deeply aligned visual and textual features. This approach, combined with an efficient three-stage training strategy, establishes a new state-of-the-art on the HarMeme, Hateful Memes Challenge (HMC) and PrideMM datasets, decisively outperforming existing state-of-the-art methods.
DMAT: An End-to-End Framework for Joint Atmospheric Turbulence Mitigation and Object Detection
Authors: Paul Hill; Zhiming Liu; Alin Achim; David Bull; Nantheera Anantrasirichai
Venue: WACV 2026 (main)

Abstract: Atmospheric Turbulence (AT) degrades the clarity and accuracy of surveillance imagery, posing challenges not only for visualization quality but also for object classification and scene tracking. Deep learning-based methods have been proposed to improve visual quality, but spatio-temporal distortions remain a significant issue. Although deep learning-based object detection performs well under normal conditions, it struggles to operate effectively on sequences distorted by atmospheric turbulence. In this paper, we propose a novel framework that learns to compensate for distorted features while simultaneously improving visualization and object detection. This end-to-end training strategy leverages and exchanges knowledge of low-level distorted features in the AT mitigator with semantic features extracted in the object detector. Specifically, in the AT mitigator a 3D Mamba-based structure is used to handle the spatio-temporal displacements and blurring caused by turbulence. Optimization is achieved through back-propagation in both the AT mitigator and object detector. Our proposed DMAT outperforms state-of-the-art AT mitigation and object detection systems up to a 15% improvement on datasets corrupted by generated turbulence.
ViGG: Robust RGB-D Point Cloud Registration using Visual-Geometric Mutual Guidance
Authors: Congjia Chen; Shen Yan; Yufu Qu
Venue: WACV 2026 (main)

Abstract: Point cloud registration is a fundamental task in 3D vision. Most existing methods only use geometric information for registration. Recently proposed RGB-D registration methods primarily focus on feature fusion or improving feature learning, which limits their ability to exploit image information and hinders their practical applicability. In this paper, we propose ViGG, a robust RGB-D registration method using mutual guidance. First, we solve clique alignment in a visual-geometric combination form, employing a geometric guidance design to suppress ambiguous cliques. Second, to mitigate accuracy degradation caused by noise in visual matches, we propose a visual-guided geometric matching method that utilizes visual priors to determine the search space, enabling the extraction of high-quality, noise-insensitive correspondences. This mutual guidance strategy brings our method superior robustness, making it applicable for various RGB-D registration tasks. The experiments on 3DMatch, ScanNet and KITTI datasets show that our method outperforms recent state-of-the-art methods in both learning-free and learning-based settings. Code is available at https://github.com/ccjccjccj/ViGG.
Learning Unified Spatio-temporal Representations for Efficient Compressed Video Understanding
Authors: Shristi Das Biswas; Efstathia Soufleri; Arani Roy; Kaushik Roy
Venue: WACV 2026 (main)

Abstract: Training robust deep video representations has proven to be computationally challenging due to substantial decoding overheads, the enormous size of raw video streams, and their inherent high temporal redundancy. Different from existing schemes, operating exclusively in the compressed video domain and exploiting all freely available modalities, i.e., I-frames, and P-frames (motion vectors and residuals) offers a compute-efficient alternative. Existing methods approach this task as a naive multi-modality problem, ignoring the temporal correlation and implicit sparsity across P-frames for modeling stronger shared representations for videos of the same action, making training and generalization easier. By revisiting the high-level design of dominant video understanding backbones, we increase inference speed by a factor of 56 while retaining similar performance. For this, we propose a hybrid end-to-end framework that factorizes learning across three key concepts to reduce inference cost by 330xversus prior art: First, a specially designed dual-encoder scheme with efficient Spiking Temporal Modulators to minimize latency while retaining cross-domain feature aggregation. Second, a unified transformer model to capture inter-modal dependencies using global self-attention to enhance I-frame -- P-frame contextual interactions. Third, a Multi-Modal Mixer Block to model rich representations from the joint spatiotemporal token embeddings. Experiments show that our method results in a lightweight architecture achieving state-of-the-art video recognition performance on UCF-101, HMDB-51, K-400, K-600 and SS-v2 datasets with favorable costs (0.73J/V) and fast inference (16V/s). Further, our observations bring new insights into practical design choices for efficient next-generation spatiotemporal learners.
Hymavi : A Hybrid Mamba-Attention Network in Multi-View Framework for Volumetric Medical Image Segmentation
Authors: Sy Dat Tran; Jin Kyu Gahm
Venue: WACV 2026 (main)

Abstract: Volumetric medical image segmentation remains a challenging and critical task in both clinical and research settings due to the inherent complexity of anatomical structures, modality-specific variability, and the need to capture both fine-grained local details and long-range spatial dependencies across 3D volumes. To address these challenges, we propose Hymavi, a novel hybrid architecture that combines Mamba-based sequence modeling with attention mechanisms in a parallel design. This dual-branch structure enables Hymavi to simultaneously leverage the high-resolution spatial reasoning capabilities of attention and the efficient global context modeling afforded by Mamba's recurrent-style architecture. In addition to its architectural innovations, Hymavi incorporates a multi-view learning strategy that leverages sagittal and coronal perspectives alongside the conventional axial view. This multi-view fusion enriches volumetric representation by integrating complementary anatomical information from different orientations, allowing the network to better capture inter-slice continuity and organ-specific variations. Extensive experiments on three widely used benchmark datasets including ACDC, BraTS2023, and AMOS22 demonstrate the effectiveness and strong generalization ability of our method across diverse segmentation tasks and imaging modalities. These results underscore the potential of Hymavi as a powerful tool for advancing automated medical image analysis. The code is publicly available at https://github.com/sydat2701/Hymavi_segmentation.
Revisiting Layer Normalization for Point Cloud Test Time Adaptation
Authors: Moslem Yazdanpanah; Ali Bahri; Mehrdad Noori; Sahar Dastani; Samuel Barbeau; David Osowiechi; Gustavo Adolfo Vargas Hakim; Ismail Ben Ayed; Christian Desrosiers
Venue: WACV 2026 (main)

Abstract: We analyze Layer Normalization (LN) from a domain (batch) perspective and explain why BatchNorm-style test-time fixes often fail on Transformer backbones. As feature dimension and batch size grow, the per-feature batch marginals after LN's pre-affine step concentrate at mean ~ 0 and variance ~ 1, making cross-batch re-standardization unnecessary and often harmful. This yields a simple rule: keep the pre-affine LN intact and adjust only the post-affine mean and gain. We instantiate this with LN-TTA, a backpropagation-free and source-free, test-time adaptation that performs a single forward pass and uniformly reparameterizes each LN layer. On three corrupted 3D point-cloud suites (ScanObjectNN-C, ModelNet40-C, ShapeNet-C), LN-TTA improves over Source-Only by +12.35, +15.58, and +3.03 points, surpasses backpropagation baselines (e.g., TENT), and sustains up to 93 samples/s, on average 39xfaster and 5xmore memory-efficient than the next-best backprop-free method. Code is available at: github.com/MosyMosy/LN_TTA
LooC: Effective Low-Dimensional Codebook for Compositional Vector Quantization
Authors: Jie Li; Kwan-Yee K. Wong; Kai Han
Venue: WACV 2026 (main)

Abstract: Vector quantization (VQ) is a prevalent and fundamental technique that discretizes continuous feature vectors by approximating them using a codebook.As the diversity and complexity of data and models continue to increase, there is an urgent need for high-capacity, yet more compact VQ methods.This paper aims to reconcile this conflict by presenting a new approach called LooC, which utilizes an effective Low-dimensional codebook for Compositional vector quantization.Firstly, LooC introduces a parameter-efficient codebook by reframing the relationship between codevectors and feature vectors, significantly expanding its solution space.Instead of individually matching codevectors with feature vectors, LooC treats them as lower-dimensional compositional units within feature vectors and combines them, resulting in a more compact codebook with improved performance.Secondly, LooC incorporates a parameter-free extrapolation-by-interpolation mechanism to enhance and smooth features during the VQ process, which allows for better preservation of details and fidelity in feature approximation. The design of LooC leads to full codebook usage, effectively utilizing the compact codebook while avoiding the problem of collapse. Thirdly, LooC can serve as a plug-and-play module for existing methods for different downstream tasks based on VQ. Finally, extensive evaluations on different tasks, datasets, and architectures demonstrate that LooC outperforms existing VQ methods, achieving state-of-the-art performance with a significantly smaller codebook.