GenAI4ELab/papercli-papers huggingface dataset:
Contrastive Integrated Gradients: A Feature Attribution-Based Method for Explaining Whole Slide Image Classification
Authors: Anh Mai Vu; Tuan L. Vo; Ngoc Lam Quang Bui; Nam N. B. Le; Akash Awasthi; Huy Q. Vo; Thanh-Huy Nguyen; Zhu Han; Chandra Mohan; Hien Van Nguyen
Venue: WACV 2026 (main)

Abstract: Interpretability is essential in Whole Slide Image (WSI) analysis for computational pathology, where understanding model predictions helps build trust in AI-assisted diagnostics. While Integrated Gradients (IG) and related attribution methods have shown promise, applying them directly to WSIs introduces challenges due to their high-resolution nature. These methods capture model decision patterns but may overlook class-discriminative signals that are crucial for distinguishing between tumor subtypes. In this work, we introduce Contrastive Integrated Gradients (CIG), a novel attribution method that enhances interpretability by computing contrastive gradients in logit space. First, CIG highlights class-discriminative regions by comparing feature importance relative to a reference class, offering sharper differentiation between tumor and non-tumor areas. Second, CIG satisfies the axioms of integrated attribution, ensuring consistency and theoretical soundness. Third, we propose two attribution quality metrics, MIL-AIC and MIL-SIC, which measure how predictive information and model confidence evolve with access to salient regions, particularly under weak supervision. We validate CIG across three datasets spanning distinct cancer types: CAMELYON16 (breast cancer metastasis in lymph nodes), TCGA-RCC (renal cell carcinoma), and TCGA-Lung (lung cancer). Experimental results demonstrate that CIG yields more informative attributions both quantitatively, using MIL-AIC and MIL-SIC, and qualitatively, through visualizations that align closely with ground truth tumor regions, underscoring its potential for interpretable and trustworthy WSI-based diagnostics.
Forget Less by Learning Together through Concept Consolidation
Authors: Arjun Ramesh Kaushik; Naresh Kumar Devulapally; Vishnu Suresh Lokhande; Nalini Ratha; Venu Govindaraju
Venue: WACV 2026 (main)

Abstract: Custom Diffusion Models (CDMs) have gained significant attention due to their remarkable ability to personalize generative processes. However, existing CDMs suffer from catastrophic forgetting when continuously learning new concepts. Most prior works attempt to mitigate this issue under the sequential learning setting with a fixed order of concept inflow and neglect inter-concept interactions. In this work, we propose a novel framework - Forget Less by Learning Together (FL2T) - that enables concurrent and order-agnostic concept learning while addressing catastrophic forgetting. Specifically, we introduce a set-invariant inter-concept learning module where proxies guide feature selection across concepts, facilitating improved knowledge retention and transfer. By leveraging inter-concept guidance, our approach preserves old concepts while efficiently incorporating new ones. Extensive experiments, across three datasets, demonstrates that our method significantly improves concept retention and mitigates catastrophic forgetting, highlighting the effectiveness of inter-concept catalytic behavior in incremental concept learning of ten tasks with atleast 2% gain on average Image Alignment scores.
GFT: Graph Feature Tuning for Efficient Point Cloud Analysis
Authors: Manish Dhakal; Venkat R Dasari; Rajshekhar Sunderraman; Yi Ding
Venue: WACV 2026 (main)

Abstract: Parameter-efficient fine-tuning (PEFT) significantly reduces computational and memory costs by updating only a small subset of the model's parameters, enabling faster adaptation to new tasks with minimal loss in performance. Previous studies have introduced PEFTs tailored for point cloud data, as general approaches are suboptimal. To further reduce the number of trainable parameters, we propose a point-cloud-specific PEFT, termed Graph Features Tuning (GFT), which learns a dynamic graph from initial tokenized inputs of the transformer using a lightweight graph convolution network and passes these graph features to deeper layers via skip connections and efficient cross-attention modules. Extensive experiments on object classification and segmentation tasks show that GFT operates in the same domain, rivalling existing methods, while reducing the trainable parameters. Code is available at https://github.com/manishdhakal/GFT.
Scalpel: Fine-Grained Alignment of Attention Activation Manifolds via Mixture Gaussian Bridges to Mitigate Multimodal Hallucination
Authors: Ziqiang Shi; Rujie Liu; Shanshan Yu; Satoshi Munakata; Koichi Shirahata
Venue: WACV 2026 (main)

Abstract: Rapid progress in large vision-language models(LVLMs) has achieved unprecedented performance in vision-language tasks. However, due to the strong prior of large language models (LLMs) and misaligned attention across modalities, LVLMs often generate outputs inconsistent with visual content - termed hallucination. To address this, we propose Scalpel, a method that reduces hallucination by refining attention activation distributions toward more credible regions. Scalpel predicts trusted attention directions for each head in Transformer layers during inference and adjusts activations accordingly. It employs a Gaussian mixture model to capture multi-peak distributions of attention in trust and hallucination manifolds, and uses entropic optimal transport (equivalent to Schrodinger bridge problem) to map Gaussian components precisely. During mitigation, Scalpel dynamically adjusts intervention strength and direction based on component membership and mapping relationships between hallucination and trust activations. Extensive experiments across multiple datasets and benchmarks demonstrate that Scalpel effectively mitigates hallucinations, outperforming previous methods and achieving state-of-the-art performance. Moreover, Scalpel is model- and data-agnostic, requiring no additional computation, only a single decoding step.
MMCM: Multimodality-aware Metric using Clustering-based Modes for Probabilistic Human Motion Prediction
Authors: Kyotaro Tokoro; Hiromu Taketsugu; Norimichi Ukita
Venue: WACV 2026 (main)

Abstract: This paper proposes a novel metric for Human Motion Prediction (HMP). Since a single past sequence can lead to multiple possible futures, a probabilistic HMP method predicts such multiple motions. While a single motion predicted by a deterministic method is evaluated only with the difference from its ground truth motion, multiple predicted motions should also be evaluated based on their distribution. For this evaluation, this paper focuses on the following two criteria. (a) Coverage: motions should be distributed among multiple motion modes to cover diverse possibilities. (b) Validity: motions should be kinematically valid as future motions observable from a given past motion. However, existing metrics simply appreciate widely distributed motions even if these motions are observed in a single mode and kinematically invalid. To resolve these disadvantages, this paper proposes a Multimodality-aware Metric using Clustering-based Modes (MMCM). For (a) coverage, MMCM divides a motion space into several clusters, each of which is regarded as a mode. These modes are used to explicitly evaluate whether predicted motions are distributed among multiple modes. For (b) validity, MMCM identifies valid modes by collecting possible future motions from a motion dataset. Our experiments validate that our clustering yields sensible mode definitions and that MMCM accurately scores multimodal predictions. Code: https://github.com/placerkyo/MMCM
ScoliGaitX: A Deep Multi-Modal Fusion Network for Scoliosis Assessment via Gait Video Analysis
Authors: Kaushik Vishwakarma; Aditya Nigam
Venue: WACV 2026 (main)

Abstract: Scoliosis presents significant diagnostic challenges, especially in its early stages due to structural deformities of the spine. The most common type, Adolescent Idiopathic Scoliosis (AIS), typically appears during rapid growth periods between ages 10 and 15, accounting for about 80-85% of all scoliosis cases. Currently, diagnosis mainly involves repeated X-rays and clinical evaluations, which are costly and expose patients to frequent radiation. To address these challenges, we propose ScoliGaitX, a novel non-invasive system for assessing scoliosis through gait video analysis. Our approach uses a multi-stream deep learning model trained specifically for scoliosis classification using the Scoliosis1K dataset. We integrate three different gait modalities--(i) silhouettes to capture appearance, (ii) optical flow for motion analysis, and (iii) GEI-subtracted sequences to highlight deviations from normal gait--to effectively identify gait abnormalities associated with scoliosis. A key component of our proposal is the Align Gate Fuse (AGF) module, designed to efficiently learn relationships between different modalities. It achieves this by adaptively assigning importance to each modality through a lightweight global gating mechanism. Our experimental results demonstrate that ScoliGaitX significantly outperforms existing methods, achieving an accuracy of 89.05%, which is 7.05% higher than the previous best approach (ScolNet-MT), while maintaining excellent specificity. This highlights the promise of our method for providing early, radiation-free scoliosis assessment.
OpenCowID: Zero-Shot Visual Identification of Dairy Cows
Authors: Omkar Prabhune; Younghyun Kim
Venue: WACV 2026 (main)

Abstract: Accurate identification of individual cows is essential to precision dairy farming. While computer vision offers a non-invasive alternative to ear tags and RFID systems, its practical deployment remains limited by the need for zero-shot identification in dynamic herds where test identities are unseen during training. In this work, we propose OpenCowID, a unified framework that addresses this challenge. First, we introduce a stochastic cow coat synthesis pipeline that efficiently generates large-scale, diverse images. Second, using the generated large-scale high-quality data, we present a centroid-guided feature learning strategy that forms a well-structured embedding space using virtual class centroids, enabling generalization to unseen identities. OpenCowID achieves state-of-the-art zero-shot and open-set identification on real-world cow benchmarks, without requiring any real labeled training data. This work contributes to the advancement of automated livestock monitoring, enabling robust, non-invasive identification. Code is available at https://github.com/neis-lab/OpenCowID.
FG-TRACER: Tracing Information Flow in Multimodal Large Language Models in Free-Form Generation
Authors: Alessia Saporita; Vittorio Pipoli; Federico Bolelli; Lorenzo Baraldi; Andrea Acquaviva; Elisa Ficarra
Venue: WACV 2026 (main)

Abstract: Multimodal Large Language Models (MLLMs) have achieved impressive performance across a variety of vision-language tasks. However, their internal working mechanisms remain largely underexplored. In his work, we introduce FG-TRACER, a framework designed to analyze the information flow between visual and textual modalities in MLLMs in free-form generation. Notably, our numerically stabilized computational method enables the first systematic analysis of multimodal information flow in underexplored domains such as image captioning and chain-of-thought (CoT) reasoning. We apply FG-TRACER to three state-of-the-art MLLMs--LLaVA 1.5, LLaMA 3.2-Vision, and Qwen 2.5-VL--across three vision-language benchmarks--TextVQA, COCO 2014, and ChartQA--and we conduct a word-level analysis of multimodal integration. Our findings uncover distinct patterns of multimodal fusion across models and tasks, demonstrating that fusion dynamics are both model- and task-dependent. Overall, FG-TRACER offers a robust methodology for probing the internal mechanisms of MLLMs in free-form settings, providing new insights into their multimodal reasoning strategies. Our source code is publicly available at https://github.com/AImageLab-zip/FG-TRACER
SpikeRain: Towards Energy-Efficient Single Image Deraining with Spiking Neural Networks
Authors: Md Tanvir Islam; Inzamamul Alam; Sambit Bakshi; Khan Muhammad; Javier Del Ser; Sangtae Ahn
Venue: WACV 2026 (main)

Abstract: With the rapid deployment of vision systems on edge devices, energy-efficient and temporally aware image deraining models are increasingly needed. We propose SpikeRain, a spiking neural network (SNN) that achieves competitive deraining performance with substantially lower computational cost than conventional artificial neural networks (ANNs). Unlike ANN-based approaches with dense activations and high memory demands, SpikeRain leverages the event-driven sparse-firing nature of spiking neurons for efficient temporal integration and contextual learning. Built on an encoder-decoder framework, SpikeRain incorporates three spiking native modules: a Dense Spiking Residual Block (DSRB) for temporal integration and feature reuse, a Multi-Dimensional Spiking Attention (MDSA) module to model temporal channel spatial dependencies, and an Adaptive Residual Feature Enhancement (ARFE) block with gated attention to refine salient features. Experiments on synthetic and real-world benchmarks show that SpikeRain achieves state-of-the-art PSNR and SSIM while reducing parameters by approximately 40% and FLOPs by approximately 89%, with energy efficiency on par with existing SNN methods. These results highlight the potential of SNNs for real-time low-power image restoration on neuromorphic platforms. SpikeRain code is available on \href https://github.com/tanvirnwu/SpikeRain  GitHub .
CAST: Evaluating Multi-Object Trackers with Context-Aware Switch and Transfer Scores
Authors: Jin Bai; Gregory D. Hager
Venue: WACV 2026 (main)

Abstract: Multi-object tracking (MOT) has been a subject of intensive research for decades. Multiple standard datasets and benchmarks have been set up, and several evaluation metrics, such as MOTA, IDF1 and HOTA. These metrics have become the de facto standard for comparing and ranking trackers on standardized datasets to measure progress. In this paper, we focus on MOTA and HOTA, and present a study of cases where these metrics' behaviors may not be desirable. In addition, we demonstrate how they might not be ideal when used as a tool to inspect a tracker's failure cases. We point out that these issues are related to the sizes of the context windows in which they measure association quality, where MOTA is too nearsighted while HOTA can be too holistic depending on the task settings. In this paper, we rethink the familiar notion of identity switches (IDSw) proposed in MOTA, and propose a generalized version of it by introducing a context window when evaluating the ID assignment choice for each detection. We show that the proposed metric, CAST, mitigates the limitations of MOTA and HOTA, and demonstrate its usefulness when diagnosing model failures through examples. Our code and toolkit will be made available at https://github.com/bkkm78/cast.
NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction
Authors: Thomas Monninger; Zihan Zhang; Steffen Staab; Sihao Ding
Venue: WACV 2026 (main)

Abstract: Accurate environmental representations are essential for autonomous driving, providing the foundation for safe and efficient navigation. Traditionally, high-definition (HD) maps are providing this representation of the static road infrastructure to the autonomous system a priori. However, because the real world is constantly changing, such maps must be constructed online from on-board sensor data. Navigation-grade standard-definition (SD) maps are widely available, but their resolution is insufficient for direct deployment. Instead, they can be used as coarse prior to guide the online map construction process. We propose NavMapFusion, a diffusion-based framework that performs iterative denoising conditioned on high-fidelity sensor data and on low-fidelity navigation maps. This paper strives to answer: (1) How can coarse, potentially outdated navigation maps guide online map construction? (2) What advantages do diffusion models offer for map fusion? We demonstrate that diffusion-based map construction provides a robust framework for map fusion. Our key insight is that discrepancies between the prior map and online perception naturally correspond to noise within the diffusion process; consistent regions reinforce the map construction, whereas outdated segments are suppressed. On the nuScenes benchmark, NavMapFusion conditioned on coarse road lines from OpenStreetMap data reaches a 21.4% relative improvement on 100 m, and even stronger improvements on larger perception ranges, while maintaining real-time capabilities. By fusing low-fidelity priors with high-fidelity sensor data, the proposed method generates accurate and up-to-date environment representations, guiding towards safer and more reliable autonomous driving. The code is available at https://github.com/tmonnin/navmapfusion
DREAM: Dynamic Prompts and GuidedMix for Efficient Continual Adaptation of Visual-Language Models
Authors: Evelyn Chee; Mong Li Lee; Wynne Hsu
Venue: WACV 2026 (main)

Abstract: Vision-language models (VLMs) exhibit impressive zero-shot transfer, but remain static and cannot adapt when exposed to new tasks. Meanwhile, conventional continual learning often overlooks preserving this zero-shot capability during adaptation. In this work, we present DREAM, a parameter-efficient framework that enables continual adaptation of VLMs while minimizing forgetting and preserving zero-shot performance. DREAM employs a dynamic prompt system with lightweight, task-specific parameters managed by two modules: a prompt composition module that dynamically generates prompts to adapt the VLM, and a query-key module that uses learned token weights to reliably activate the appropriate parameters at inference. To enhance robustness, we propose GuidedMix, which creates semantically meaningful mixed images, and pair them with mixture-aware text embeddings to strengthen representation learning through image-text alignment. We further leverage the GuidedMix samples to estimate task-specific query-key similarity thresholds that identify samples of unseen tasks and and prevent spurious prompt usage on the VLM, thereby safeguarding its zero-shot behavior. Experiments show that our method adapts efficiently, mitigates forgetting, and maintains strong zero-shot transfer with substantially fewer trainable parameters, showing consistent gains even under partial supervision.
SynPlay: Large-Scale Synthetic Human Data with Real-World Diversity for Aerial-View Perception
Authors: Jinsub Yim; Hyungtae Lee; Sungmin Eum; Yi-Ting Shen; Yan Zhang; Heesung Kwon; Shuvra S. Bhattacharyya
Venue: WACV 2026 (main)

Abstract: We introduce SynPlay, a large-scale synthetic human dataset purpose-built for advancing multi-perspective human localization, with a predominant focus on aerial-view perception. SynPlay departs from traditional synthetic datasets by addressing a critical but underexplored challenge: localizing humans in aerial scenes where subjects often occupy only tens of pixels in the image. In such scenarios, fine-grained details like facial features or textures become irrelevant, shifting the burden of recognition to human motion, behavior, and interactions. To meet this need, SynPlay implements a novel rule-guided motion generation framework that combines real-world motion capture with motion evolution graphs. This design enables human actions to evolve dynamically through high-level game rules rather than predefined scripts, resulting in effectively uncountable motion variations. Unlike existing synthetic datasets--which either focus on static visual traits or reuse a limited set of mocap-driven actions--SynPlay captures a wide spectrum of spontaneous behaviors, including complex interactions that naturally emerge from unscripted gameplay scenarios. SynPlay also introduces an extensive multi-camera setup that spans UAVs at random altitudes, CCTVs, and a freely roaming UGV, achieving true near-to-far perspective coverage in a single dataset. The majority of instances are captured from aerial viewpoints at varying scales, directly supporting the development of models for long-range human analysis--a setting where existing datasets fall short. Our data contains over 73k images and 6.5M human instances, with detailed annotations for detection, segmentation, and keypoint tasks. Extensive experiments demonstrate that training with SynPlay significantly improves human localization performance, especially in few-shot and data-scarce scenarios.
Overcoming Small Data Limitations in Video-Based Infant Respiration Estimation
Authors: Liyang Song; Hardik Bishnoi; Sai Kumar Reddy Manne; Sarah Ostadabbas; Briana J. Taylor; Michael Wan
Venue: WACV 2026 (main)

Abstract: The development of contactless respiration monitoring for infants could enable advances in the early detection and treatment of breathing irregularities, which are associated with neurodevelopmental impairments and conditions like sudden infant death syndrome (SIDS). But while respiration estimation for adults is supported by a robust ecosystem of computer vision algorithms and video datasets, only one small public video dataset with annotated respiration data for infant subjects exists, and there are no reproducible algorithms which are effective for infants. We introduce the annotated infant respiration dataset of 400 videos (AIR-400), contributing 275 new, carefully annotated videos from 10 recruited subjects to the public corpus. We develop the first reproducible pipelines for infant respiration estimation, based on infant-specific region-of-interest detection and spatiotemporal neural processing enhanced by optical flow inputs. We establish, through comprehensive experiments, the first reproducible benchmarks for the state-of-the-art in vision-based infant respiration estimation. We make our dataset, code repository, and trained models available for public use.
SIAM: Synchronous Interaction Attention for Human Mesh Recovery
Authors: Niaz Ahmad; Saif Ullah; Youngmoon Lee; Guanghui Wang
Venue: WACV 2026 (main)

Abstract: Conventional 3D body-mesh reconstruction methods often use decoupling strategies that isolate individual features for separate representation and therefore lack relational cues among entities. In this paper, a novel Synchronous Interaction Attention Module (SIAM) for human-mesh recovery is proposed. The framework builds on a high-resolution multi-branch backbone (HRNet) and introduces two key components. First, Synchronous Interaction Attention (SIA), which explicitly models spatial relational cues among multiple human instances in live scenes. Second, Feature Decomposition (FD), which extracts enriched instance-specific features by leveraging the attributes captured by the SIA module. This integrated approach significantly enhances spatial reasoning, mitigates error accumulation, and yields more accurate 3D human-mesh reconstructions. SIAM achieves state-of-the-art performance on several benchmarks, including 3DPW, 3DPW-OCC, AGORA, and CMU-Panoptic. Notably, the model processes video streams at 25 frames per second, demonstrating its suitability for real-time applications. The source code and supplementary materials are publicly available.