WACV 2026 论文列表

Recent advances in image generation models have enabled the production of high-quality images, making synthetic images a promising alternative to real images for dataset construction. However, a critical challenge remains in that the performance of Vision-Language Models (VLMs) tends to degrade as the proportion of synthetic images in a dataset increases in conventional approaches. To alleviate the challenge, we introduce a plug-and-play dataset construction framework that enhances text-to-image diffusion models by optimizing their initial noise. Our method treats the initial noise as a learnable parameter and iteratively updates it to maximize text-image alignment based on multiple embedding models without retraining the generator. Since the initial noise plays a crucial role in determining the quality of the synthetic image, its optimization enables the search for initial conditions that yield semantically faithful and realistic images. By improving FID and text-image alignment compared to conventional latent diffusion model (LDM)-based methods, our approach produces synthetic images better suited for training. When CLIP models were trained on such images, they achieved up to +5.09% higher Average R@1 in zero-shot retrieval, +2.88% higher Average top-1 accuracy in zero-shot classification, and +5.05% higher performance in linear-probing. These results demonstrate that initial noise optimization is an effective and scalable strategy for enabling robust VLM training with synthetic images.

Point clouds are widely used in various fields, including augmented reality (AR) and robotics, where relighting and texture editing are crucial for realistic visualization. Achieving these tasks requires accurately separating albedo from shade. However, performing this separation on point clouds presents two key challenges: (1) the non-grid structure of point clouds makes conventional image-based decomposition models ineffective, and (2) point-cloud models designed for other tasks do not explicitly consider global-light direction, resulting in inaccurate shade. In this paper, we introduce Intrinsic Point-Cloud Decomposition (IPCD), which extends image decomposition to the direct decomposition of colored point clouds into albedo and shade. To overcome challenge (1), we propose IPCD-Net that extends image-based model with point-wise feature aggregation for non-grid data processing. For challenge (2), we introduce Projection-based Luminance Distribution (PLD) with a hierarchical feature refinement, capturing global-light ques via multi-view projection. For comprehensive evaluation, we create a synthetic outdoor-scene dataset. Experimental results demonstrate that IPCD-Net reduces cast shadows in albedo and enhances color accuracy in shade. Furthermore, we showcase its applications in texture editing, relighting, and point-cloud registration under varying illumination. Finally, we verify the real-world applicability of IPCD-Net.

Gaussian Splatting (GS) are promising scene representations for visual localization and SLAM. Recent works have explored loop closure detection via Gaussian registration, improving map consistency and accuracy. However, achieving reliable registration given two GS representations from different acquisitions remains challenging.In this paper, we propose a complete pipeline to perform the matching and registration given two GS maps. The proposed method is grounded in generating orthographic bird's-eye views (BEVs) of optimized Gaussian models. The proposed approach leverages photometric and geometric information extracted directly from the GS to provide a trade-off of accuracy and invariance to different viewing changes (e.g., as type of GS maps, seasons or illumination). Unlike 3D registration methods, which become inefficient as the number of Gaussians grows, our approach leverages 2D orthographic renders thus considerably reducing the registration complexity. Experiments on two public datasets demonstrate that our method achieves higher accuracy than several existing baselines, while also maintaining better registration results when dealing with GS maps learned by different techniques (e.g., 3DGS to LightGaussian), or GS maps presenting viewing changes such as varying illumination conditions. Code and evaluation setup will be made publicly available.

Generative AI (GenAI) holds significant promise for automating everyday image editing tasks, especially following the recent release of GPT-4o on March 25, 2025. However, what subjects do people most often want edited? What kinds of editing actions do they want to perform (e.g., removing or stylizing the subject)? Do people prefer precise edits with predictable outcomes or highly creative ones? By understanding the characteristics of real-world requests and the corresponding edits made by freelance photo-editing wizards, can we draw lessons for improving AI-based editors and determine which types of requests can currently be handled successfully by AI editors? In this paper, we present a unique study addressing these questions by analyzing 83k requests from the past 12 years (2013-2025) on the Reddit community, which collected 305k PSR-wizard edits. According to human ratings, approximately only 33% of requests can be fulfilled by the best AI editors (including GPT-4o, Gemini-2.0-Flash, SeedEdit). Interestingly, AI editors perform worse on low-creativity requests that require precise editing than on more open-ended tasks. They often struggle to preserve the identity of people and animals, and they frequently make non-requested touch-ups. On the other side of the table, VLM judges (e.g., o1) perform differently from human judges and may prefer AI edits more than human edits.

Scene understanding and reasoning has been a fundamental problem in 3D computer vision, requiring models to identify objects, their properties, and spatial or comparative relationships among the objects. Existing approaches enable this by creating scene graphs using multiple inputs such as 2D images, depth maps, object labels, and annotated relationships from specific reference view. However, these methods often struggle with generalization and produce inaccurate spatial relationships like "left/right", which become inconsistent across different viewpoints. To address these limitations, we propose Viewpoint-Invariant ZerO-shot scene graph generation for 3D scene Reasoning (VIZOR). VIZOR is a training-free, end-to-end framework that constructs dense, viewpoint-invariant 3D scene graphs directly from raw 3D scenes. The generated scene graph is unambiguous, as spatial relationships are defined relative to each object's front-facing direction, making them consistent regardless of the reference view. Furthermore, it infers open-vocabulary relationships that describe spatial and proximity relationships among scene objects without requiring annotated training data. We conduct extensive quantitative and qualitative evaluations to assess the effectiveness of VIZOR on scene graph generation and downstream tasks, such as query-based object grounding. VIZOR outperforms state-of-the-art methods, showing clear improvements in scene graph generation and achieving 22% and 4.81% gains in zero-shot grounding accuracy on the Replica and Nr3D datasets, respectively.

Although out-of-distribution (OOD) detection has been extensively studied, it continues to face challenges in handling OOD data semantically similar to In-Distribution (ID) data. Part of the difficulty arises from the model's inability to learn superior ID class discriminative features. We propose to improve this by segmenting input images into foreground and background views and combining them with the original input image (original view) in a multi-view learning approach. We present a novel method, called CASOD (Cross-view Attention of Segmented views for OOD Detection), that learns better discriminative information from all three views and subsequently, fuses them through a novel stacked cross-view attention mechanism to produce the final predictive feature representation. A feature-based method is then applied to the final fused feature for OOD detection, giving major improvements over a range of strong baselines on various near- and far-OOD datasets. CASOD achieves state-of-the-art performance in various experimental settings with challenging ID and OOD datasets. Code for CASOD can be found at https://github.com/alexp205/CASOD.

Existing deepfake detection techniques struggle to keep-up with the ever-evolving novel, unseen forgeries methods. This limitation stems from their reliance on statistical artifacts learned during training, which are often tied to specific generation processes that may not be representative of samples from new, unseen deepfake generation methods encountered at test time. We propose that incorporating language guidance can improve deepfake detection generalization by integrating human-like commonsense reasoning -- such as recognizing logical inconsistencies and perceptual anomalies -- alongside statistical cues. To achieve this, we train an expert deepfake vision encoder by combining discriminative classification with image-text contrastive learning, where the text is generated by generalist MLLMs using few-shot prompting. This allows the encoder to extract both language-describable, commonsense deepfake artifacts and statistical forgery artifacts from pixel-level distributions. To further enhance robustness, we integrate data uncertainty learning into vision-language contrastive learning, mitigating noise in image-text supervision. Our expert vision encoder seamlessly interfaces with an LLM, further enabling more generalized and interpretable deepfake detection while also boosting accuracy. The resulting framework, AuthGuard, achieves state-of-the-art deepfake detection accuracy in both in-distribution and out-of-distribution settings, achieving AUC gains of 6.15% on the DFDC dataset and 16.68% on the DF40 dataset. Additionally, AuthGuard significantly enhances deepfake reasoning, improving performance by 24.69% on the DDVQA dataset.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.