GenAI4ELab/papercli-papers huggingface dataset:
R-MMA: Enhancing Vision-Language Models with Recurrent Adapters for Few-Shot and Cross-Domain Generalization
Authors: Md Fahim; Md Farhan Ishmam; Mir Sazzat Hossain; M Ashraful Amin; Amin Ahsan Ali; AKM Mahbubur Rahman
Venue: WACV 2026 (main)

Abstract: Despite the strong generalization capabilities of pre-trained vision-language models (VLMs) like CLIP, adapting these models for few-shot generalization tasks still presents a fundamental challenge. This challenge, often termed the discrimination-generalization dilemma, highlights the need to fine-tune task-specific knowledge while simultaneously preserving the model's general, pre-trained knowledge. Prompt learning offers a partial solution but often struggles to capture rich visual-textual interactions. Adapter-based methods like MMA improve alignment by adding learnable modules, but their use of multiple independent adapters increases parameter overhead and can limit transferability due to naive fusion of adapted and frozen features.To address these limitations, we introduce Recurrent Multi-Modal Adapter (R-MMA), a lightweight and efficient extension of MMA that enhances both performance and generalization. R-MMA employs a recurrent adapter module with shared weights across multiple layers of the image and text encoders. This design substantially reduces the parameter count while maintaining high expressive capacity. Additionally, R-MMA integrates an attention-based alignment mechanism to harmonize the adapter outputs with the frozen encoder features before fusion. This ensures better preservation of the pre-trained representations and enhances cross-modal consistency. Extensive experiments across 15 datasets on diverse tasks, including few-shot learning, generalization to novel classes and domains, and dataset transfer scenarios, demonstrate that R-MMA consistently surpasses state-of-the-art baselines, achieving strong performance with improved efficiency and a better balance between adaptation and generalization. Our work achieves one of the highest forms of parameter efficiency with only three trainable weight matrices for the whole network, regardless of the network depth. Our code is available at: https://github.com/farhanishmam/R-MMA.
TRACE: Confounder-free Adversarial Fine-tuning for Robust Object Detection
Authors: Wonho Lee; Jisu Lee; Hyunsik Na; Sohee Park; Daeseon Choi
Venue: WACV 2026 (main)

Abstract: Adversarial patch attacks critically endanger object detection systems by causing severe mispredictions with small, easily realizable perturbations in both digital and physical environments. Existing defenses such as certified methods or patch detection suffer from high latency, while conventional adversarial training often overfits to specific patches and lacks generalization, particularly in multi-object scenarios. To overcome high latency and poor generalization, we introduce TRACE (Tuning Robustness by Adversarial-patch Confounder Elimination), an adversarial fine-tuning framework that leverages Instrumental Variable Regression in the feature space. TRACE treats patch-related variations--including location, rotation, and brightness--as confounders, thereby eliminating spurious correlations and guiding the model toward causal features that sustain robust detection. Evaluations on YOLOv5 and YOLOv8 show that TRACE consistently outperforms conventional defense methods in both efficiency and robustness under adaptive and unseen patch attacks. Moreover, physical testbed experiments confirm its effectiveness beyond digital settings, highlighting TRACE as a practical solution for achieving generalized robustness in object detection.
PrevMatch: Revisiting and Maximizing Temporal Knowledge in Semi-Supervised Semantic Segmentation
Authors: Wooseok Shin; Hyun Joon Park; Jin Sob Kim; Juan Yun; Se Hong Park; Sung Won Han
Venue: WACV 2026 (main)

Abstract: In semi-supervised semantic segmentation, the Mean Teacher- and co-training-based approaches are employed to mitigate confirmation bias and coupling problems. However, despite their high performance, these approaches frequently involve complex training pipelines and a substantial computational burden, limiting the scalability and compatibility of these methods. In this paper, we propose a PrevMatch framework that effectively mitigates the aforementioned limitations by maximizing the utilization of the temporal knowledge obtained during the training process. The PrevMatch framework relies on two core strategies: (1) we reconsider the use of temporal knowledge and thus directly utilize previous models obtained during training to generate additional pseudo-label guidance, referred to as previous guidance. (2) we design a highly randomized ensemble strategy to maximize the effectiveness of the previous guidance. PrevMatch, a simple yet effective plug-in method, can be seamlessly integrated into existing semi-supervised learning frameworks with minimal computational overhead. Experimental results on three benchmark semantic segmentation datasets show that incorporating PrevMatch into existing methods significantly improves their performance. Furthermore, our analysis indicates that PrevMatch facilitates stable optimization during training, resulting in improved generalization performance.
SaccadeX: Directed Acyclic Graph-based Semi-Supervised Learning of Continuous Ocular Dynamics from Sparse Neuromorphic Streams
Authors: Nuwan Bandara; Thivya Kandappu; Archan Misra
Venue: WACV 2026 (main)

Abstract: Continuous eye tracking is critical for applications in human-computer interaction, including biometric authentication, gaze-based systems, and affective-cognitive modeling. Recent interest in neuromorphic event cameras has grown due to their sub-microsecond latency in capturing eye movement dynamics. However, existing event-based eye-tracking methods face challenges such as limited labels, sub-optimal event accumulation, and a lack of frameworks that fail to capture fine-grained temporal relationships within event volumes. To address these, we propose a directed acyclic graph-based semi-supervised approach with a framework that is adaptable across multiple closely related tasks, including gaze tracking, pupil tracking, and eye-based emotion recognition. Our approach enables efficient spatiotemporal learning with 95.5% parameter reduction compared to existing methods, achieving significant performance improvements: 38.75% improvement in pupil tracking accuracy, 68% and 63% reductions in gaze angle error on EV-Eye and EBV-Eye datasets respectively, and 3.3% improvement in emotion recognition across all evaluated datasets. Project page is at https://eye-tracking-for-physiological- sensing.github.io/SaccadeX/.
CAAC: Confidence-Aware Attention Calibration to Reduce Hallucinations in Large Vision-Language Models
Authors: Mehrdad Fazli; Bowen Wei; Ahmet Sari; Ziwei Zhu
Venue: WACV 2026 (main)

Abstract: Large vision-language models (LVLMs) achieve impressive performance on multimodal tasks but often suffer from hallucination and confidently describe objects or attributes not present in the image. Current training-free interventions struggle to maintain accuracy in open-ended and long-form generation scenarios. We introduce the Confidence-Aware Attention Calibration (CAAC) framework to address this challenge by targeting two key biases: spatial perception bias, which distributes attention disproportionately across image tokens, and modality bias, which shifts focus from visual to textual inputs over time. CAAC employs a two-step approach: Visual-Token Calibration (VTC) to balance attention across visual tokens, and Adaptive Attention Re-Scaling (AAR) to reinforce visual grounding guided by the model's confidence. This confidence-driven adjustment ensures consistent visual alignment during generation. Experiments on CHAIR, AMBER, and POPE benchmarks demonstrate that CAAC outperforms baselines, particularly in long-form generations, effectively reducing hallucination.
UCDSC: Open Set UnCertainty aware Deep Simplex Classifier for Medical Image Datasets
Authors: Arnav Aditya; Nitin Kumar; Saurabh Shigwan
Venue: WACV 2026 (main)

Abstract: Driven by advancements in deep learning, computer-aided diagnoses have made remarkable progress. However, outside controlled laboratory settings, algorithms may encounter several challenges. In the medical domain, these difficulties often stem from limited data availability due to ethical and legal restrictions, as well as the high cost and time required for expert annotations--especially in the face of emerging or rare diseases. In this context, open-set recognition plays a vital role by identifying whether a sample belongs to one of the known classes seen during training or should be rejected as an unknown. Recent studies have shown that features learned in the later stages of deep neural networks are observed to cluster around their class means, which themselves are arranged as individual vertices of a regular simplex [32]. The proposed method introduces a loss function designed to reject samples of unknown classes effectively by penalizing open space regions using auxiliary datasets. This approach achieves significant performance gain across four MedMNIST datasets--BloodMNIST, OCTMNIST, DermaMNIST, TissueMNIST and a publicly available skin dataset[29] outperforming state-of-the-art techniques.
SOLAR: Switchable Output Layer for Accuracy and Robustness in Once-for-All Training
Authors: Shaharyar Ahmed Khan Tareen; Lei Fan; Xiaojing Yuan; Qin Lin; Bin Hu
Venue: WACV 2026 (main)

Abstract: Once-for-All (OFA) training enables a single super-net to generate multiple sub-nets tailored to diverse deployment scenarios, supporting flexible trade-offs among accuracy, robustness, and model-size without retraining. However, as the number of supported sub-nets increases, excessive parameter sharing in the backbone limits representational capacity, leading to degraded calibration and reduced overall performance. To address this, we propose SOLAR (Switchable Output Layer for Accuracy and Robustness in Once-for-All Training), a simple yet effective technique that assigns each sub-net a separate classification head. By decoupling the logit learning process across sub-nets, the Switchable Output Layer (SOL) reduces representational interference and improves optimization, without altering the shared backbone. We evaluate SOLAR on five datasets (SVHN, CIFAR-10, STL-10, CIFAR-100, and TinyImageNet) using four super-net backbones (ResNet-34, WideResNet-16-8, WideResNet-40-2, and MobileNetV2) for two OFA training frameworks (OATS and SNNs). Experiments show that SOLAR outperforms the baseline methods: compared to OATS, it improves accuracy of sub-nets up to 1.26 %, 4.71 %, 1.67 %, and 1.76 %, and robustness up to 9.01 %, 7.71 %, 2.72 %, and 1.26 % on SVHN, CIFAR-10, STL-10, and CIFAR-100, respectively. Compared to SNNs, it improves TinyImageNet accuracy by up to 2.93 %, 2.34 %, and 1.35 % using ResNet-34, WideResNet-16-8, and MobileNetV2 backbones (with 8 sub-nets), respectively. The code of SOLAR is publicly available at: https://github.com/NAIL-UH/SOLAR and its website can be accessed at https://saktx.github.io/solar.github.io/.
Towards Streaming LiDAR Object Detection with Point Clouds as Egocentric Sequences
Authors: Mellon M. Zhang; Glen Chou; Saibal Mukhopadhyay
Venue: WACV 2026 (main)

Abstract: Accurate and low-latency 3D object detection is essential for autonomous driving, where safety hinges on both rapid response and reliable perception. While rotating LiDAR sensors are widely adopted for their robustness and fidelity, current detectors face a trade-off: streaming methods process partial polar sectors on the fly for fast updates but suffer from limited visibility, cross-sector dependencies, and distortions from retrofitted Cartesian designs, whereas full-scan methods achieve higher accuracy but are bottlenecked by the inherent latency of a LiDAR revolution. We propose Polar-Fast-Cartesian-Full (PFCF), a hybrid detector that combines fast polar processing for intra-sector feature extraction with accurate Cartesian reasoning for full-scene understanding. Central to PFCF is a custom Mamba SSM-based streaming backbone with dimensionally-decomposed convolutions that avoids distortion-heavy planes, enabling parameter-efficient, translation-invariant, and distortion-robust polar representation learning. Local sector features are extracted via this backbone, then accumulated into a sector feature buffer to enable efficient inter-sector communication through a full-scan backbone. PFCF establishes a new Pareto frontier on the Waymo Open dataset, surpassing prior streaming baselines by 10% mAP and matching full-scan accuracy at twice the update rate.
Subspace-Guided Knowledge Distillation for Efficient Model Transfer
Authors: Zeeshan Hayder; Ali Cheraghian; Lars Petersson; Mehrtash Harandi
Venue: WACV 2026 (main)

Abstract: Compact models can be effectively trained via Knowledge Distillation (KD), where a lightweight student model learns to replicate the behavior of a larger, high-performing teacher. A persistent challenge in KD lies in the misalignment between the representational spaces of teacher and student networks, especially when they differ in architecture or capacity. To address this, we propose Subspace-Driven Knowledge Distillation (SDMD), a novel framework that mitigates representational disparity by projecting features into an indefinite inner product space. This relaxation from traditional Hilbert spaces enables more flexible geometric alignment, capturing transformations such as rotations and reflections that are often necessary for accurate knowledge transfer. By learning a subspace that bridges the semantic gap between teacher and student, SDMD facilitates more effective distillation without increasing model complexity. We validate SDMD through extensive experiments on large-scale image classification (ImageNet-1K) and object detection (COCO), where it consistently outperforms existing distillation methods. Notably, SDMD-trained models not only achieve state-of-the-art results in distilled settings but also surpass the performance of equivalent models trained from scratch, highlighting the strength of our subspace-based alignment strategy.
DexAvatar: 3D Sign Language Reconstruction with Hand and Body Pose Priors
Authors: Kaustubh Kundu; Hrishav Bakul Barua; Lucy Robertson-Bell; Zhixi Cai; Kalin Stefanov
Venue: WACV 2026 (main)

Abstract: The trend in sign language generation is centered around data-driven generative methods that require vast amounts of precise 2D and 3D human pose data to achieve an acceptable generation quality. However, currently, most sign language datasets are video-based and limited to automatically reconstructed 2D human poses (i.e., keypoints) and lack accurate 3D information. Furthermore, existing state-of-the-art for automatic 3D human pose estimation from sign language videos is prone to self-occlusion, noise, and motion blur effects, resulting in poor reconstruction quality. In response to this, we introduce DexAvatar, a novel framework to reconstruct bio-mechanically accurate fine-grained hand articulations and body movements from in-the-wild monocular sign language videos, guided by learned 3D hand and body priors. DexAvatar achieves strong performance in the SGNify motion capture dataset, the only benchmark available for this task, reaching an improvement of 35.11% in the estimation of body and hand poses compared to the state-of-the-art. The official website of this work is: https://github.com/kaustesseract/DexAvatar.
EmojiDiff: Advanced Facial Expression Control with High Identity Preservation in Portrait Generation
Authors: Liangwei Jiang; Ruida Li; Zhifeng Zhang; Shuo Fang; Chenguang Ma
Venue: WACV 2026 (main)

Abstract: This paper aims to bring fine-grained expression control while maintaining high-fidelity identity in portrait generation. This is challenging due to the mutual interference between expression and identity. On one hand, fine expression control signals inevitably introduce appearance-related semantics (e.g., facial contours, and ratio), which impact the identity of the generated portrait. On the other hand, even coarse-grained expression control can cause facial changes that compromise identity, since they all act on the face. Here, we introduce EmojiDiff, the first end-to-end solution that enables simultaneous control of extremely detailed expression (RGB-level) and high-fidelity identity in portrait generation. To address the above challenges, EmojiDiff adopts a two-stage scheme involving decoupled training and fine-tuning. For decoupled training, we innovate ID-irrelevant Data Iteration (IDI) to synthesize high-quality cross-identity expression pairs by separating and optimizing the processes of maintaining expression and altering identity. Training the model with this data, we effectively disentangle fine expression features in the expression template from other extraneous information (e.g., identity, skin). Subsequently, we present ID-enhanced Contrast Aignment (ICA) for further fine-tuning. ICA achieves rapid reconstruction and joint supervision of identity and expression information, thus aligning identity representations of images with and without expression control. Experimental results demonstrate that our method significantly outperforms its counterparts, achieving precise expression control with highly maintained identity, and generalizing well to various diffusion models. Project page: https://emojidiff.github.io.
FairScene: Learning Class-Disentangled 2D/3D Representations for Semantic Scene Completion
Authors: Dian Jia; Pei Yu; Wei Tang
Venue: WACV 2026 (main)

Abstract: Semantic Scene Completion (SSC) aims to predict the semantic occupancy of each voxel within a 3D scene using sensor data, a critical task for autonomous driving and robotics. Despite recent progress, camera-based SSC remains challenging due to various difficulties, including voxel class imbalance, occlusion, and depth ambiguity. This paper introduces FairScene, a novel approach that learns class-disentangled 2D/3D representations to improve SSC. By ensuring balanced representations across classes, FairScene mitigates the dominance of majority classes and promotes fairer voxel categorization. Additionally, FairScene explicitly models spatial dependencies between different classes through a novel inter-class occupancy reasoning mechanism. Such explicit modeling helps alleviate occlusion and depth ambiguities in SSC. To address the scarcity of SSC training data, we propose OccMix, a novel augmentation strategy that generalizes MixUp from 2D to 2.5D and 3D metric spaces while maintaining geometric consistency. Extensive quantitative and qualitative experiments demonstrate that FairScene outperforms prior methods on both the SemanticKITTI and SSCBench-KITTI-360 benchmarks. The code is available at https://github.com/DianJJ/FairScene.
A Universal Self-Attention Enhancement for Bridging Low-bit Quantization and Vision Transformers
Authors: Jiahe Qian; Peisong Wang; Zhengyang Zhuge; Qinghao Hu; Jian Cheng
Venue: WACV 2026 (main)

Abstract: Low-bit quantization of Vision Transformers presents significant challenges due to the intrinsic properties of their Multi-head Self-attention modules. In this work, we investigate the quantization issues specific to this mechanism and identify two critical challenges. First, quantization amplifies discrepancies among attention heads, thereby impairing the model's capability to focus on the most informative regions. Second, high-magnitude values in the attention maps, which encode essential relational information, exhibit an extremely sharp distribution that renders them especially prone to substantial information loss during quantization. To address these challenges, we propose Quantized-aware Multi-head Self-attention (Q-MHSA), a universal self-attention enhancement module that integrates two lightweight components within Multi-head Self-attention (MHSA). The Cross-head Concordance Module (CCM) enforces adaptive consistency across attention heads, while the Learnable Smoothness Controller (LSC) replaces the fixed normalization factor with an adaptive mechanism that selectively smooths the distribution of high-magnitude attention values while disregarding less informative low values. Designed for seamless integration with any Vision Transformer architecture and quantization-aware training method, Q-MHSA incurs minimal overhead while consistently improving model accuracy. For instance, under the VVTQ quantization framework applied to a 4-bit Swin-S model, the incorporation of Q-MHSA yields a top-1 accuracy of 83.5%, representing a 0.9% improvement over the baseline, while incurring a marginal overhead of 0.01% in both parameters and FLOPs.
SegMo: Segment-aligned Text to 3D Human Motion Generation
Authors: Bowen Dang; Lin Wu; Xiaohang Yang; Zheng Yuan; Zhixiang Chen
Venue: WACV 2026 (main)

Abstract: Generating 3D human motions from textual descriptions is an important research problem with broad applications in video games, virtual reality, and augmented reality. Recent methods align the textual description with human motion at the sequence level, neglecting the internal semantic structure of modalities. However, both motion descriptions and motion sequences can be naturally decomposed into smaller and semantically coherent segments, which can serve as atomic alignment units to achieve finer-grained correspondence. Motivated by this, we propose SegMo, a novel Segment-aligned text-conditioned human Motion generation framework to achieve fine-grained text-motion alignment. Our framework consists of three modules: (1) Text Segment Extraction, which decomposes complex textual descriptions into temporally ordered phrases, each representing a simple atomic action; (2) Motion Segment Extraction, which partitions complete motion sequences into corresponding motion segments; and (3) Fine-grained Text-Motion Alignment, which aligns text and motion segments with contrastive learning. Extensive experiments demonstrate that SegMo improves the strong baseline on two widely used datasets, achieving an improved TOP 1 score of 0.553 on the HumanML3D test set. Moreover, thanks to the learned shared embedding space for text and motion segments, SegMo can also be applied to retrieval-style tasks such as motion grounding and motion-to-text retrieval.
SceneProp: Combining Neural Network and Markov Random Field for Scene-Graph Grounding
Authors: Keita Otani; Tatsuya Harada
Venue: WACV 2026 (main)

Abstract: Grounding complex, compositional visual queries with multiple objects and relationships is a fundamental challenge for vision-language models. While standard phrase grounding methods excel at localizing single objects, they lack the structural inductive bias to parse intricate relational descriptions, often failing as queries become more descriptive. To address this structural deficit, we focus on scene-graph grounding, a powerful but less-explored formulation where the query is an explicit graph of objects and their relationships. However, existing methods for this task also struggle, paradoxically showing decreased performance as the query graph grows---failing to leverage the very information that should make grounding easier. We introduce SceneProp, a novel method that resolves this issue by reformulating scene-graph grounding as a Maximum a Posteriori (MAP) inference problem in a Markov Random Field (MRF). By performing global inference over the entire query graph, SceneProp finds the optimal assignment of image regions to nodes that jointly satisfies all constraints. This is achieved within an end-to-end framework via a differentiable implementation of the Belief Propagation algorithm. Experiments on four benchmarks show that our dedicated focus on the scene-graph grounding formulation allows SceneProp to significantly outperform prior work. Critically, its accuracy consistently improves with the size and complexity of the query graph, demonstrating for the first time that more relational context can, and should, lead to better grounding. Codes are available at https://github.com/keitaotani/SceneProp.