GenAI4ELab/papercli-papers huggingface dataset:
Saliency-Guided DETR for Moment Retrieval and Highlight Detection
Authors: Aleksandr Gordeev; Vladimir Dokholyan; Irina Tolstykh; Maksim Kuprashevich
Venue: WACV 2026 (main)

Abstract: With the rapid growth of video content available, the ability to search for specific moments within videos using textual queries has become increasingly relevant. This is crucial in many scenarios, from surveillance cameras where it may be necessary to find specific events in extensive video streams to searching for exciting movie scenes. However, existing approaches for video Moment Retrieval and Highlight Detection often struggle to effectively align text and video features, limiting their performance. We argue that utilizing recent foundational video models designed for video-text alignment can overcome these limitations. We propose a novel architecture that utilizes such models to test this hypothesis. Combined with our novel Saliency-Guided Cross Attention mechanism and a hybrid DETR architecture, our approach provides significantly improved results. To further enhance our approach, we developed InterVid-MR - a large-scale, high-quality dataset specifically designed for a pretraining stage. Extensive experiments and comparisons with current state-of-the-art methods confirm the effectiveness of the approach, achieving 58.8 mAP on QVHighlights, 60.7 R@1 mIoU on Charades-STA, and 42.4 R@1 mIoU on TACoS. These results highlight the efficiency and scalability of the method for video-language tasks in both zero-shot and fine-tuning scenarios.
MooTrack360: A Novel Fisheye Camera Dataset for Robust Multi Diary Cow Detection and Tracking
Authors: Rasmus Gjerlund K. Christiansen; Toan Van Nguyen; Lasse Rose Malskær; Leon Bodenhagen; Dirk Kraft
Venue: WACV 2026 (main)

Abstract: MooTrack360 is a novel top-down fisheye dataset designed to support the development of robust camera surveillance and monitoring systems in large-scale, real-world environments. While centered around continuous livestock monitoring of Holstein dairy cows, the dataset addresses general challenges in computer vision such as fisheye distortion, overlapping fields of view, variable lighting, and occlusions. It includes 102,747 annotated cow instances across 1,500 images, each labelled as "standing" or "lying", along with a 1-hour annotated video sequence for tracking evaluation. Several unannotated sequences are included for development and analysis. The dataset enables detection and tracking under conditions ranging from daylight to infrared-assisted nighttime imaging, and facilitates both application-specific and generalized model evaluation. A calibration pipeline based on the Double Sphere Camera model is provided to support distortion correction and precise spatial localization. An accompanying end-to-end training framework further addresses challenges such as illumination changes and occlusions. Benchmarks using state-of-the-art detection and tracking methods demonstrate the dataset's potential to advance research in non-invasive, camera-based monitoring across domains. The complete dataset--including annotated images, tracking sequences, sample videos, calibration footage, and the full codebase, is available at: https://zenodo.org/records/15470268.
Histopath-C: Towards Realistic Domain Shifts for Histopathology Vision-Language Adaptation
Authors: Mehrdad Noori; Gustavo A. Vargas Hakim; David Osowiechi; Fereshteh Shakeri; Ali Bahri; Moslem Yazdanpanah; Sahar Dastani; Ismail Ben Ayed; Christian Desrosiers
Venue: WACV 2026 (main)

Abstract: Medical Vision-language models (VLMs) have shown remarkable performances in various medical imaging domains such as histo\-pathology by leveraging pre-trained, contrastive models that exploit visual and textual information. However, histopathology images may exhibit severe domain shifts, such as staining, contamination, blurring, and noise, which may severely degrade the VLM's downstream performance. In this work, we introduce Histopath-C, a new benchmark with realistic synthetic corruptions designed to mimic real-world distribution shifts observed in digital histopathology. Our framework dynamically applies corruptions to any available dataset and evaluates Test-Time Adaptation (TTA) mechanisms on the fly. We then propose LATTE, a transductive, low-rank adaptation strategy that exploits multiple text templates, mitigating the sensitivity of histopathology VLMs to diverse text inputs. Our approach outperforms state-of-the-art TTA methods originally designed for natural images across a breadth of histopathology datasets, demonstrating the effectiveness of our proposed design for robust adaptation in histopathology images. Code and data are available at https://github.com/Mehrdad-Noori/Histopath-C.
Tables Decoded: DELTA for Structure, TARQA for Understanding
Authors: Jahanvi Rajput; Dhruv Kudale; Saikiran Kasturi; Utkarsh Verma; Ganesh Ramakrishnan
Venue: WACV 2026 (main)

Abstract: Table understanding is a core task in document intelligence, encompassing two key subtasks: table reconstruction and table visual question answering (TabVQA). While recent approaches predominantly rely on vision-language models (VLMs) operating on table images, we propose a more scalable and effective alternative based on structured textual representations. These representations are easier to process, align more naturally with LLMs, and eliminate the need for language-specific visual encoders, making them particularly suitable for multilingual documents. We present DELTA, which separates physical structure recognition, logical structure recognition, and OCR to extract both layout and content accurately. DELTA outputs tables in Optimised Table Structure Language (OTSL), a compact and unified format that encodes cell arrangements and textual content. On table structure recognition (TSR), DELTA achieves TEDS-Structure scores comparable with state-of-the-art methods across FinTabNet, PubTabNet, and PubTables-1M. We further establish its robustness on non-English tables through our curated Hindi benchmark, TORQUE. Building on this, we introduce TARQA, an LLM fine-tuned on OTSL sequences. Our approach yields gains of 9.3 p.p. on WTQ (TabQA) and 9.2 p.p. on FinTabNetQA (TabVQA), respectively. On TORQUE, our method ranks second among all VLMs and DELTA + LLM variants. We release our code, models, and benchmark at: https://github.com/Tihiitborg/Tables-Decoded.
HumanGuideNet: Adapter-Based Alignment of Deep Neural Networks with Human Similarity Judgments
Authors: Xufu Liu; Yifan Yang; Zhengxin Zhang
Venue: WACV 2026 (main)

Abstract: Aligning deep neural network (DNN) representations with human perceptual structure is an important direction for cognitively grounded AI, and emerging evidence suggests that human-aligned models can exhibit improved robustness. We introduce HumanGuideNet, an adapter-based architecture with a human-aligned branch--HumReg--trained jointly on standard class labels (e.g., ImageNet-1k) and human similarity judgments (THINGs data) to align model representations with human similarity structure. Unlike traditional alignment methods based on linear transforms, HumanGuideNet preserves the pretrained backbone and fuses human-aligned features with backbone representations to retain general visual knowledge while injecting perceptual alignment. We show that the HumReg representations better capture human representational similarity matrices (RSMs) and lead to fused features that significantly improve generalization and robustness. Specifically, the fused features boost few-shot classification and anomaly detection accuracy across a range of datasets, while also exhibiting robustness to natural image corruptions. Our results show that modular human alignment can effectively enhance large pretrained models, providing a scalable and interpretable approach to building human-aligned visual intelligence.
Broadcast2Pitch: Game State Reconstruction from Unconstrained Soccer Videos
Authors: Yin May Oo; Yewon Hwang; Muhammad Amrulloh Robbani; Vanyi Chao; Ankhzaya Jamsrandorj; Hoang Quoc Nguyen; Kyung-Ryoul Mun; Jinwook Kim
Venue: WACV 2026 (main)

Abstract: Game State Reconstruction (GSR) aims to reconstruct the 2D positions and identities of all athletes from broadcast soccer videos, requiring robust tracking, localization, and identity association under dynamic and unconstrained camera motions. We propose a modular GSR framework that integrates a multi-task keypoint and line detection model with an optimization-based homography estimation module. This approach leverages dense geometric cues from lines, circles, and keypoints to achieve robust spatial localization on a frame-by-frame basis, providing reliable alignment in diverse broadcast scenarios. To address identity consistency, we use appearance-based re-identification and a vision-language-guided tracklet refinement strategy to reduce ID switches and enforce temporal coherence. Comprehensive ablation studies validate the contribution of each component, and our framework achieves state-of-the-art performance on the SoccerNet-GSR benchmark, outperforming existing baselines by a significant margin. The proposed framework demonstrates strong robustness, generalization across scenes, and practical utility for structured game understanding in real-world broadcast sports analytics.
Mixed Diffusion for 3D Indoor Scene Synthesis
Authors: Siyi Hu; Diego Martín Arroyo; Stephanie Debats; Fabian Manhardt; Luca Carlone; Federico Tombari
Venue: WACV 2026 (main)

Abstract: Generating realistic 3D scenes is an area of growing interest in computer vision and robotics. However, creating high-quality, diverse synthetic 3D content often requires expert intervention, making it costly and complex. Recently, efforts to automate this process with learning techniques, particularly diffusion models, have shown significant improvements in tasks like furniture rearrangement. However, applying diffusion models to floor-conditioned indoor scene synthesis remains under-explored. This task is especially challenging as it requires arranging objects in continuous space while selecting from discrete object categories, posing unique difficulties for conventional diffusion methods. To bridge this gap, we present MiDiffusion, a novel mixed discrete-continuous diffusion model designed to synthesize plausible 3D indoor scenes given a floor plan and pre-arranged objects. We represent a scene layout by a 2D floor plan and a set of objects, each defined by category, location, size, and orientation. Our approach uniquely applies structured corruption across mixed discrete semantic and continuous geometric domains, resulting in a better-conditioned problem for denoising. Evaluated on the 3D-FRONT dataset, MiDiffusion outperforms state-of-the-art autoregressive and diffusion models in floor-conditioned 3D scene synthesis. Additionally, it effectively handles partial object constraints via a corruption-and-masking strategy without task-specific training, demonstrating advantages in scene completion and furniture arrangement tasks.
NERVE: Neighbourhood & Entropy-Guided Random-Walk for Training Free Open-Vocabulary Segmentation
Authors: Kunal Mahatha; Jose Dolz; Christian Desrosiers
Venue: WACV 2026 (main)

Abstract: Despite recent advances in Open-Vocabulary Semantic Segmentation (OVSS), existing training-free methods face several limitations: use of computationally expensive affinity refinement strategies, ineffective fusion of transformer attention maps due to equal weighting or reliance on fixed-size Gaussian kernels to reinforce local spatial smoothness, enforcing isotropic neighborhoods. We propose a strong baseline for training-free OVSS termed as NERVE (Neighbourhood & Entropy-guided Random-walk for open-Vocabulary sEgmentation), which uniquely integrates global and fine-grained local information, exploiting the neighbourhood structure from the self-attention layer of a stable diffusion model. We also introduce a stochastic random walk for refining the affinity rather than relying on fixed-size Gaussian kernels for local context. This spatial diffusion process encourages propagation across connected and semantically related areas, enabling it to effectively delineate objects with arbitrary shapes. Whereas most existing approaches treat self-attention maps from different transformer heads or layers equally, our method uses entropy-based uncertainty to select the most relevant maps. Notably, our method does not require any conventional post-processing techniques like Conditional Random Fields (CRF) or Pixel-Adaptive Mask Refinement (PAMR). Experiments are performed on 7 popular semantic segmentation benchmarks, yielding an overall state-of-the-art zero-shot segmentation performance, providing an effective approach to open-vocabulary semantic segmentation.
PSA-MIL: A Probabilistic Spatial Attention-Based Multiple Instance Learning for Whole Slide Image Classification
Authors: Sharon Peled; Yosef E. Maruvka; Moti Freiman
Venue: WACV 2026 (main)

Abstract: Whole Slide Images (WSIs) are high-resolution digital scans widely used in medical diagnostics. Due to their immense size, WSI classification is typically approached using Multiple Instance Learning (MIL), where a slide is partitioned into individual tiles, disrupting its spatial structure. Recent MIL methods often incorporate spatial context through rigid spatial assumptions (e.g. fixed kernels), which limit their ability to capture the intricate tissue structures crucial for an accurate diagnosis. To address this limitation, we propose Probabilistic Spatial Attention MIL (PSA-MIL), a novel attention-based MIL framework that integrates spatial context into the attention mechanism through learnable distance-decayed priors, formulated within a probabilistic interpretation of self-attention as a posterior distribution. This formulation enables a dynamic inference of spatial relationships during training, eliminating the need for predefined assumptions often imposed by previous approaches. Furthermore, we introduce a diversity loss that promotes complementary spatial representations across attention heads and a spatial posterior-pruning strategy that reduces computational cost for long WSI sequences while preserving performance. Extensive experiments across multiple datasets and tasks show that PSA-MIL outperforms current baselines and achieves state-of-the-art results with substantially lower computational overhead. Our code is available at https://github.com/SharonPeled/PSA-MIL.
Diverse Sketch Colorization with Content-Enhanced Style Representation and Recolorization Distillation
Authors: Shuangming Mao; Haixiang Zhu
Venue: WACV 2026 (main)

Abstract: Sketch colorization is highly demanded in the field of art, as it offers a valuable tool for artists, designers, and illustrators to explore novel possibilities and express their creativity. Given a sketch, it can be colorized in various styles, such as rendering a facial sketch with different hair colors. To achieve this, previous works often resorted to sampling style codes from a simple Gaussian distribution to produce varied images. However, due to the inherent scarcity of information in sketches, the resulting colorized images often exhibit noticeable artifacts and lack of diversity. In this paper, our aim is to generate informative style representations that can effectively compensate for missing content information in sketches while enabling diverse generations. We improve style granularity by extracting style information from CLIP, and achieve the disentanglement of content and style by establishing semantic correspondence between sketches and color images in the CLIP space. Furthermore, to alleviate the artifacts and blending issues caused by semantic deficiency, we simultaneously train a recolorization model in an end-to-end manner. The recolorization model shares the same style space as the colorization model. In this way, we can construct multiple pseudo-sketch-image pairs for each sketch, which are used to provide pixel-level supervision for the colorization model, thus significantly facilitating the learning of semantic correspondence. Experiments demonstrate that our method effectively mitigates artifacts in colorized results and produces more semantically rich colors.
STEG-AIW: Spatio-Temporal Gating and Adaptive-Timestep Inference for Efficient Spiking Neural Networks
Authors: Gulfam Ahmed Saju; Anton Spirkin; Felipe Marcelino; Yuchou Chang
Venue: WACV 2026 (main)

Abstract: Spiking neural networks (SNNs) are efficient, yet modern systems still waste compute by propagating redundant activations within a timestep and by using a fixed temporal horizon regardless of input difficulty. We present STEG-AIW, a training and inference framework that addresses both issues. The Spatio-Temporal Efficient Gate (STEG) is a lightweight gating module placed at residual stages. It suppresses non-salient activations while preserving temporal dynamics. The Adaptive Inference Window (AIW) module accumulates per-timestep evidence and converts it to halting probabilities for sample-wise early termination. We train the model end-to-end with a loss that balances task accuracy, an efficiency term proportional to the expected number of timesteps, and a sparsity term on gate activations. A simple complexity analysis links these choices to fewer synaptic operations. On static image benchmarks, STEG-AIW attains state-of-the-art accuracy with 34-88% fewer timesteps than the strongest baselines. On neuromorphic datasets, it matches or exceeds the best accuracy with 43-73% fewer timesteps and reduces synaptic operations accordingly. Overall, STEG-AIW provides a backbone-agnostic path to accurate, low-power inference. This moves SNNs closer to practical deployment.
Perception-Inspired Color Space Design for Photo White Balance Editing
Authors: Yang Cheng; Ziteng Cui; Lin Gu; Shenghan Su; Zenghui Zhang
Venue: WACV 2026 (main)

Abstract: White balance (WB) is a key step in the image signal processor (ISP) pipeline that mitigates color casts caused by varying illumination and restores the scene's true colors. Currently, sRGB-based WB editing for post-ISP WB correction is widely used to address color constancy failures in the ISP pipeline when the original camera RAW is unavailable. However, additive color models (e.g., sRGB) are inherently limited by fixed nonlinear transformations and entangled color channels, which often impede their generalization to complex lighting conditions. To address these challenges, we propose a novel framework for WB correction that leverages a perception-inspired Learnable HSI (LHSI) color space. Built upon a cylindrical color model that naturally separates luminance from chromatic components, our framework further introduces dedicated parameters to enhance this disentanglement and learnable mapping to adaptively refine the flexibility. Moreover, a new Mamba-based network is introduced, which is tailored to the characteristics of the proposed LHSI color space. Experimental results on benchmark datasets demonstrate the superiority of our method, highlighting the potential of perception-inspired color space design in computational photography. The source code is available at https://github.com/YangCheng58/WB_Color_Space.
A Framework for Real-Time Surgical Phase Recognition with Application to Robot-Assisted Partial Nephrectomy
Authors: Marco Mezzina; Tom Vercauteren; Tinne Tuytelaars; Matthew B. Blaschko
Venue: WACV 2026 (main)

Abstract: Surgical practice has increasingly integrated advanced technologies to improve procedural outcomes, efficiency, and safety in modern operating rooms. Within this evolving landscape, Automated Surgical Phase Recognition (SPR) leverages Artificial Intelligence to temporally segment surgical workflows into key events, thereby supporting both real-time decision-making and off-line analysis. Despite the potential of SPR, previous research focused on short and linear surgeries, paying limited attention to the development, assessment, and deployment of real-time systems for complex surgical workflows. This work addresses these gaps by targeting the highly-complex and non linear workflow of Robot-Assisted Partial Nephrectomy (RAPN). We develop a real-time SPR system trained on 143 annotated RAPN surgical videos spanning 15 phases. The system incorporates a trainable canonical calibration error estimator combined with Viterbi decoding for more reliable outcomes. Additionally, we introduce a novel assessment framework designed to simultaneously evaluate off-line, real-time, and averaged SPR performance, synthesising historical phase predictions over time. For deployment, we implement the SPR pipeline as an end-to-end application using the NVIDIA Holoscan platform. The system was successfully tested during three live RAPN cases on human patients in a collaborating hospital, achieving an average inference latency of 16.65 ms and an accuracy of 68.2%. Results indicate that Viterbi decoding boosts performance in this complex surgery, while canonical calibration does not significantly increase overall performance but enhances classification reliability. We show the feasibility of deploying a real-time SPR pipeline for RAPN, which holds promise for optimising OR planning. The application is available at https://github.com/nvidia-holoscan/holohub/tree/main/applications/orsi
WiSE-OD: Benchmarking Robustness in Infrared Object Detection
Authors: Heitor R. Medeiros; Atif Belal; Masih Aminbeidokhti; Eric Granger; Marco Pedersoli
Venue: WACV 2026 (main)

Abstract: Object detection (OD) in infrared (IR) imagery is critical for low-light and nighttime applications. However, the scarcity of large-scale IR datasets forces models to rely on weights pre-trained on RGB images. While fine-tuning on IR improves accuracy, it often compromises robustness under distribution shifts due to the inherent modality gap between RGB and IR. To address this, we introduce LLVIP-C and FLIR-C, two cross-modality out-of-distribution (OOD) benchmarks built by applying corruptions to standard IR datasets. Additionally, to fully leverage the complementary knowledge from RGB and infrared-trained models, we propose WiSE-OD, a weight-space ensembling method with two variants: WiSE-OD_ ZS , which combines RGB zero-shot and IR fine-tuned weights, and WiSE-OD_ LP , which blends zero-shot and linear probing. Evaluated across four RGB-pretrained detectors and two robust baselines on our benchmark and in the real-world out-of-distribution M3FD dataset, WiSE-OD improves both cross-modality and corruption robustness in synthetic and real-world distribution shifts without any additional training or inference costs. Our code is available at: https://github.com/heitorrapela/wiseod