paperlist.ai

AI conference papers

AI conference paper library

Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

SpatialBoost: Enhancing Visual Representation through Language-Guided Reasoning

Byungwoo Jeon, Dongyoung Kim, Huiwon Jang, Insoo Kim, Jinwoo Shin

Despite the remarkable success of large-scale pre-trained im-age representation models (i.e., vision encoders) across various visiontasks, they are predominantly trained on 2D image data and thereforeoften fail to capture 3D spatial relationships between objects and back-grounds in the real world, constraining their effectiveness in many down-stream applications. To address this, we propose SpatialBoost, a scalableframework that enhances the spatial awareness of existing pre-trained vi-sion encoders by injecting 3D spatial knowledge expressed in linguisticdescriptions. The core idea involves converting dense 3D spatial infor-mation from 2D images into linguistic expressions, which is then usedto inject such spatial knowledge into vision encoders through a LargeLanguage Model (LLM). To this end, we adopt a multi-turn Chain-of-Thought (CoT) reasoning process that progressively incorporates densespatial knowledge and builds hierarchical spatial understanding. To val-idate effectiveness, we adapt SpatialBoost to state-of-the-art vision en-coders such as DINOv3, and evaluate its performance gains on a widerange of benchmarks requiring both 3D perception and general visionabilities. For instance, SpatialBoost improves DINOv3 performance from55.9 to 59.7 mIoU on ADE20K, achieving state-of-the-art performancewith 3.8%p gain over the pre-trained DINOv3. Project page.

ECCV 2026

SPAR: A Sequential Primacy and Attribution Ranking Framework for Skill Determination

Hui Yu, Xiao Ke, Zhihong Zeng, Huangbiao Xu, Huanqi Wu, Yaru Su

Skill determination in video understanding remains challeng-ing due to the intricate temporal dynamics of skill performance and thecomplex cognitive processes inherently underlying human evaluation. Ex-isting approaches often overlook the progressive evolution of evaluators’cognitive states and the influence of key action stages on overall per-formance. To address this limitation, we propose Sequential Primacyand Attribution Ranking Modeling (SPAR). SPAR captures the tem-poral evolution of cognitive states throughout the evaluation process,enabling iterative refinement of evaluative understanding and producingprogressively enhanced representations of overall performance quality.The framework integrates adaptive weighting based on the global tempo-ral context, emphasizing critical segments through localized interactionmodeling and multi-scale temporal aggregation. To correct inconsisten-cies in intermediate recognition signals, SPAR employs a bias-mitigationloss to ensure a faithful and consistent reflection of intrinsic action qual-ity. Extensive experiments on five public datasets validate the effective-ness and generalization capability of the proposed framework.

ECCV 2026

HiChor: Hierarchical Choreography Generation from Pop Music with Choreographic Primitives

Jungsu Kim, Jungwoo Huh, Jeongwook Choi, Wen-Huang Cheng, Jian-Yu Jiang-Lin, Weisi Lin, Sanghoon Lee

Choreography is a structured creative process grounded inmusical form, rather than a mere sequence of improvised movements.Professional choreographers design choreography following a set of chore-ographic primitives: (1) organizing movements at the phrase level, (2)refining movements by aligning them with musical beats and energy,and (3) stylizing phrases through semantic text descriptions. However,existing dance generation methods overlook these primitives, leadingto dances that feel structurally incomplete and resemble improvisationrather than professional choreography. Here, we present HiChor: Hi-erarchical Choreography generation from pop music, a framework in-spired by the structured workflow of professional choreographers, whichis grounded in choreographic primitives. HiChor hierarchically generateschoreography from pop music by (1) generating phrase-level choreogra-phy aligned with beats, (2) enhancing choreography by aligning withmusic’s energy, and (3) concatenating all phrase-level choreography andenhancing it through plausibility enhancement to ensure smooth transi-tions between phrases. Furthermore, we introduce a semantic text styl-ization module that converts semantic text descriptions into low-levelchoreography feature captions using an LLM, enabling semantic control.To assess how well the generated choreography adheres to choreographicprinciples, we introduce new metrics—Phrase Diversity, Feature Align-ment, and Text Stylization Score. Experiments demonstrate that HiChoroutperforms existing methods in both choreographic quality and seman-tic stylization.

ECCV 2026

Sound-based Multi-Person 3D Pose Estimation

Yusuke Oumi, Yuto Shibata, Go Irie, Akisato Kimura, Yoshimitsu Aoki, Mariko Isogawa

Can we recover the 3D poses of multiple people using onlysound? This paper presents the first attempt to estimate multi-person3D poses solely from acoustic signals. Estimating the poses of multipleindividuals using acoustic signals is inherently challenging due to the su-perposition of motion-dependent signal variations. Unlike single-personscenarios, the presence of multiple subjects leads to overlapping acousticsignatures, making it difficult to attribute specific signal changes to anindividual’s pose. Furthermore, the complexity is compounded by inter-person reflections, which introduce intricate propagation delays that ob-scure the temporal motion-acoustic relationship. To address these issues,we propose SoundMHPE (Sound-based Multi-person Human Pose Es-timator), a novel encoder-decoder framework consisting of two key com-ponents. First, the Acoustic Multi-scale Encoder captures diverse tem-poral and fine-grained frequency features to isolate subtle acoustic sig-natures from complex, overlapping signals. Second, the Temporal PoseDecoder employs an attention mechanism to disentangle multi-personinformation across successive frames. By jointly accounting for tempo-ral dynamics and inter-person dependencies, this component preciselyreconstructs frame-wise individual poses. To validate our approach, weconstructed the 6-hour Acoustic Multi-person Pose (AMP) dataset con-sisting of 432K synchronized frames of multi-person pose and acous-tic data, and demonstrated that our SoundMHPE outperforms baselinemodels. Project page: https://oumi03.github.io/sound-mhpe/

ECCV 2026

SFDATrack: Generalized Source-Free Domain Adaptive Tracking Under Adverse Weather Conditions

Siyuan Yao, Ziqi Wang, Junqi Huang, Ruiqi Yu, Wenqi Ren, Xiaochun Cao

Domain adaptive visual object tracking under adverse weatherconditions has garnered significant attention in recent years. Despitethe impressive performance, existing methods heavily rely on the large-scale video frames from both source and target domains, which is im-practical under rigid resource constraints where source data is unavail-able. To overcome this limitation, we propose SFDATrack, a general-ized source-free domain adaptive tracker that merely leverages adverseweather samples from the target domain for robust state estimation.Specifically, SFDATrack first employs a mean-teacher backbone withDual Interactive Mamba (DIM) blocks to distill the candidate targettokens that are resilient to weather variations from classified, augmentedsamples. Afterwards, we introduce a hyperspherical prototype projec-tion (HPP) module to project these tokens onto multi-domain proto-types within a latent hyperspherical space. By enforcing both domain-specific and domain-invariant properties of the multi-domain prototypes,SFDATrack can be seamlessly adapted to diverse weather conditionswith powerful generalizability. Extensive experiments evaluated on vari-ous benchmarks demonstrate that SFDATrack achieves superior perfor-mance compared to state-of-the-art approaches. The code is available athttps://github.com/watcherBR0/sfdatrack.

ECCV 2026

Self-transcendence: Is External Feature Guidance Indispensable for Accelerating Diffusion Transformer Training?

Lingchen Sun, Rongyuan Wu, Zhengqiang ZHANG, Ruibin Li, YUJING SUN, Shuaizheng Liu, Lei Zhang

Recent works such as REPA have shown that guiding dif-fusion models with external semantic features (e.g., DINO) can signifi-cantly accelerate the training of diffusion transformers (DiTs). However,the use of pretrained external features as guidance signals introducesadditional dependencies. We argue that DiTs actually have the powerto guide the training of themselves, and propose Self-Transcendence,an effective method that achieves fast convergence using internal featuresupervision only. The desired internal guidance features should meet tworequirements: structurally clean to help shallow blocks separate noisefrom signal, and semantically discriminative to help shallow layers learneffective representations. With this consideration, we first align the DiTfeatures with the clean VAE latent features, a native component of la-tent diffusion, for a short training phase (e.g., 40 epochs) to improvetheir structural representations, then apply the classifier-free guidanceto the intermediate features, enhancing their discriminative capabilityand semantic expressiveness. These enriched internal features, learnedentirely within the model, are used as supervision signals to guide a newDiT training from scratch. Compared to existing self-contained meth-ods, our approach achieves a significant performance boost. It can evensurpass REPA, which uses the external DINO features as guidance, inboth generation quality and convergence speed for both class-to-imageand text-to-image generation tasks. Codes and models can be found athttps://github.com/csslc/Self-Transcendence.

ECCV 2026

Seeing to Ground: Visual Attention for Hallucination-Resilient MDLLMs

Vishal Narnaware, Animesh Gupta, Kevin Zhai, Zhenyi Wang, Shah Mubarak

Multimodal Diffusion Large Language Models (MDLLMs)achieve high-concurrency generation through parallel masked decoding,yet the architectures remain prone to multimodal hallucinations. Thisstructural vulnerability stems from an algorithmic flaw: the decoderranks candidate tokens based on textual likelihood without verifyinglocalized visual support. We establish that this language-only rankinginduces an objective mismatch, where language probability mass acts asa misspecified proxy for the intended multimodal task. Consequently, wereinterpret hallucination as a localized optimization error, a phenomenonwhere the decoder exploits language shortcuts to maximize a proxy scoreat the expense of visual grounding. To address this objective mismatch,we introduce VISAGE, a training-free decoding framework that cali-brates the objective at inference time. VISAGE estimates the proxydiscrepancy by quantifying the spatial entropy of cross-attention distri-butions. By enforcing a localization consensus across attention heads, themethod penalizes spatially uniform distributions and re-ranks token com-mitments to favor visually grounded outcomes. We provide an analyti-cal stability guarantee establishing that VISAGE maintains a boundedobjective loss under estimation error. Evaluations across hallucination-sensitive and general-purpose benchmarks demonstrate the robustnessof the framework, yielding relative gains of 8.59% on MMMU-val and7.75% on HallusionBench.

ECCV 2026

Revisiting Scene Graph Generation from the Perspective of Detector-Conditioned Reachability

Runfeng Qu, Pia Bideau, Ole Hall, Julie Ouerfelli-Ethier, Klaus Obermayer, Olaf Hellwich

Scene graph generation (SGG) approaches can be broadlyclassified into detector-based and query-based methods according to theirunderlying reasoning mechanisms. However, the discrepancy in their pre-dictive behaviors, induced by these distinct mechanisms, has not beensystematically analyzed. In this work, we design a controlled experimen-tal setup to examine prediction discrepancies from the perspective ofdetector-conditioned reachability. The results suggest clear complemen-tary clues. Motivated by this observation, we introduce a Dual-SGGmethod that consolidates both reasoning mechanisms via a dual-querydesign, thereby leveraging the complementary predictive behaviors ofboth detector-based and query-based methods. Extensive experimentson the Visual Genome, Open Images v6, and GQA-200 datasets demon-strate the effectiveness of the proposed method. Code is available at:Dual-SGG.

ECCV 2026

RefracGS: Novel View Synthesis Through Refractive Water Surfaces with 3D Gaussian Ray Tracing

Yiming Shao, Qiyu Dai, Chong Gao, Guanbin Li, Yequan Wang, He Sun, Qiong Zeng, Baoquan Chen, Wenzheng Chen

Novel view synthesis (NVS) through non-planar refractivesurfaces presents fundamental challenges due to severe, spatially varyingoptical distortions. While recent representations like NeRF and 3D Gaus-sian Splatting (3DGS) excel at NVS, their assumption of straight-lineray propagation fails under these conditions, leading to significant arti-facts. To overcome this limitation, we introduce RefracGS, a frameworkthat jointly reconstructs the refractive water surface and the scene be-neath the interface. Our key insight is to explicitly decouple the refractiveboundary from the target objects: the refractive surface is modeled via aneural height field, capturing wave geometry, while the underlying sceneis represented as a 3D Gaussian field. We formulate a refraction-awareGaussian ray tracing approach that accurately computes non-linear raytrajectories using Snell’s law and efficiently renders the underlying Gaus-sian field while backpropagating the loss gradients to the parameterizedrefractive surface. Through end-to-end joint optimization of both rep-resentations, our method ensures high-fidelity NVS and view-consistentsurface recovery. Experiments on both synthetic and real-world sceneswith complex waves demonstrate that RefracGS outperforms prior re-fractive methods in visual quality, while achieving ∼15× faster trainingand real-time rendering at 200 FPS.

ECCV 2026

Recurrent Cross-View Object Geo-Localization

Xiaohan Zhang, Siyuan Cao, Xiaokai Bai, Yiming Li, Zhangkai Shen, Zhe Wu, Lun Luo, Qi Ming, Xiaoxi Hu, Hui-liang Shen

Cross-view object geo-localization (CVOGL) aims to deter-mine the location of a specific object in high-resolution satellite imagerygiven a query image with a point prompt. Existing approaches treatCVOGL as a one-shot detection process, directly regressing object loca-tions from cross-view information aggregation, but they are vulnerableto feature noise and lack mechanisms for error correction. In this paper,we propose ReCOT, a Recurrent Cross-view Object geo-localizationTransformer, which models CVOGL as a recurrent localization process.ReCOT introduces a set of learnable tokens that encode task-specificintent from the query image and prompt embeddings, and iteratively at-tend to the reference features to refine the predicted location. To enhancethis recurrent process, we incorporate two complementary modules: (1) aSAM-based knowledge distillation strategy that transfers segmentationpriors from the Segment Anything Model (SAM) to provide clearer se-mantic guidance without additional inference cost, and (2) a ReferenceFeature Enhancement Module (RFEM) that introduces hierarchical at-tention to emphasize object-relevant regions in the reference features.Extensive experiments on CVOGL benchmarks demonstrate that Re-COT achieves state-of-the-art (SOTA) performance while significantlyreducing parameters compared to previous SOTA approaches. Our codeis available at https://github.com/Temperature-ai/ReCOT.git.

ECCV 2026

Rectified Embedding Flow Learning for Aerial Multi-view  Geo-localization

Hao Ruan, Jinliang Lin, Yingxin Lai, Zhiming Luo, Shaozi Li, Yu Zang, Cheng Wang

Aerial geo-localization is increasingly essential for large-scale spatial retrieval. To overcome the limitations of single-retrieval systems caused by modality-specific uncertainty in open environments, this paper introduces the unified Aerial Multi-view Geo-localization (AMGL) task. However, when applying universal multimodal retrieval paradigms to this task, the semantic bias introduced solely by textual instructions fails to resolve and reconstruct the differentiated distribution structures necessary for cross-view alignment. Consequently, this paper proposes the Rectified Embedding Flow Learning (REFL) framework, formulating crossdomain alignment as a directed conditional distribution transport process. Initially, REFL employs velocity-prior flow learning to fit continuous ordinary differential equation trajectories, deriving transformation priors that map single-view distributions to a latent shared manifold. Subsequently, a trajectory-guided embedding rectification mechanism continuously transports query features to the target view distribution, explicitly compensating for distribution shifts. Extensive evaluations on the Aerial MVGL benchmark demonstrate that REFL achieves state-of-theart performance, yielding an average R@1 of 44.83% and R@10 of 65.85%. The code and benchmark are available at https://github.com/rhaohur/REFL.

ECCV 2026

QWERTY: Training-Free Motion Control via Query-Warped Video Diffusion Transformers

Kyobin Choo, Youngmin Kim, Hyunkyung Han, Geunrip Park, Chanyoung Kim, Sunyoung Jung, Seong Jae Hwang

Video diffusion transformers (DiTs) generate high-fidelityand temporally coherent videos, yet motion control remains implicit,primarily relying on text prompts. As a result, achieving desired motionoften requires extensive prompt engineering and repeated resampling.While fine-tuning models with additional spatial prompts (e.g., bound-ing boxes or point trajectories) enables explicit control, it demands sub-stantial data curation and computation, and may compromise the gener-ative capabilities of pretrained models. Consequently, training-free mo-tion control using such spatial prompts has been explored in U-Net–basedvideo diffusion models, but remains largely unexplored for DiTs. We in-troduce Qwerty, a training-free framework that enables flexible motioncontrol in pretrained image-to-video DiTs via user-defined object warpingand optical flow. We carefully manipulate the 3D full attention of DiTsby warping the frame-invariant semantic subspace of queries. We findthat the noise predicted by the query-warped DiT naturally guides thediffusion trajectory toward the desired motion, and further show thatleveraging this noise as self-guidance for latent optimization improvescontrol stability and visual quality. Experiments show that Qwertyachieves the most effective motion control among existing training-freeapproaches on a recent image-to-video DiT, with performance compara-ble to fine-tuning–based methods.

ECCV 2026

Proximity-Constrained Counterfactual Decoding for Hallucination-Robust Medical VQA

Dwarikanath Mahapatra, Abhijit Das, Manish Pandey, Joy Dhar, Sudipta Roy, Zongyuan Ge, Behzad Bozorgtabar, Imran Razzak

Hallucinations in medical vision language models arise whenlanguage priors override subtle visual evidence during decoding. Existingtraining-free contrastive decoding methods suppress these priors by con-trasting logits against a globally perturbed image, but global perturba-tions destroy the very diagnostic cues they are meant to protect —stan-dard VCD blur flips nearly one in five CheXpert labels on held-out chestradiographs. We propose Proximity-Constrained CounterfactualDecoding (PCCD), which admits a counterfactual view only whenit satisfies both a global and an object-masked feature-space similaritybound, constraining diagnostic drift while increasing the contrastive mar-gin for visually grounded tokens to first order under explicitly stated as-sumptions. Two complementary branches address heterogeneous halluci-nation drivers: Object-Aware VCD preserves attribution-indicated lesionregions while attenuating contextual co-occurrence priors, and Latent-Disentangled VCD applies structure-preserving style edits to suppressattribute biases. When neither branch passes the proximity gate, PCCDfalls back to greedy decoding, guaranteeing it never degrades below theunmitigated baseline. Across radiology VQA, chest X-ray report gener-ation, and a new CXR existence probe (Med-POPE), PCCD improvesGREEN AUC, QAAS, and RadGraph F1 by up to →+2.5 pp over SOTAwith one auxiliary forward pass per token and no weight updates.

ECCV 2026

PRISM-VO: Scale-Aware Visual Odometry Using Photometric Plenoptic Bundle Adjustment

Aymeric Fleith, Julian Zirbel, Daniel Cremers, Niclas Zeller

We introduce PRISM-VO, a novel pure optimization-basedsparse photometric visual odometry framework for focused plenopticcameras. The core of PRISM-VO is a novel photometric plenoptic bun-dle adjustment which jointly optimizes camera poses and inverse depthvalues of points in a sliding window. By combining geometric depth froma single plenoptic image with temporal multi-view constraints, PRISM-VO achieves accurate and drift-resilient motion estimation. Through ex-plicit modeling of the plenoptic projection, PRISM-VO provides reliablemetric-scale reconstructions, overcoming the scale ambiguity of monocu-lar SLAM algorithms. Importantly, our approach relies solely on a singleplenoptic sensor and avoids complex initialization, as depth priors arecomputed directly from plenoptic imaging.Experiments show that PRISM-VO outperforms the current state-of-the-art plenoptic visual odometry method on indoor and outdoor scenes.The proposed approach rivals other optimization- and learning-basedmethods while accurately and reliably recovering a metric scale of thescene.Project page: https://prism-vo.github.io/.

ECCV 2026

MV2GF: Multi-view Pedestrian Detection with a Visual Geometric Foundation Model

Taiga Yamane, Satoshi Suzuki, Ryo Masumura, Shota Orihashi, Tomohiro Tanaka, Mana Ihori, Naoki Makishima

Multi-View Pedestrian Detection (MVPD) aims to detectpedestrians in the form of a bird’s eye view map from multi-view images.Recent MVPD methods adopt a unified framework that projects 2D im-age features into a 3D world space and aggregates them into a singlefeature. Although they are effective, they struggle to generalize to un-seen camera configurations during training due to two main issues. First,they are difficult to capture accurate visual geometry across views in un-seen camera configurations. Second, they make detection models highlydependent on distortion patterns during training arising from their im-age feature projection. To address these, we leverage a visual geometricfoundation model and propose MV2GF. This foundation model has ex-hibited strong generalization in capturing visual geometry across viewsand predicting accurate 3D attributes in diverse camera configurations.MV2GF fuses task-specific features with general-purpose geometric fea-tures extracted by the foundation model to effectively capture the visualgeometry even in unseen camera configurations. Furthermore, MV2GFprojects each pixel in the image features to an appropriate 3D locationusing 3D pointmaps predicted by the foundation model, preventing thedetection model from depending on distortion patterns during training.Our experiments demonstrate the effectiveness of leveraging a visual geo-metric foundation model for MVPD and that MV2GF generalizes betterthan existing methods.

ECCV 2026

Memory-Supported Synergistic Adaptation for Training-Free Test-Time Medical Image Segmentation

Lingrui Li, Nan Pu, Dong Zhao, Wenjing Li, Andrew French, Xin Chen, Zhun Zhong

Test-time adaptation (TTA) aims to mitigate distributionshifts by adapting models with unlabeled target data at inference time.While TTA with vision-language models (VLMs) has shown promisingresults in classification, extending it to medical image segmentation re-mains challenging. In this setting, the adaptation gains from optimizingon VLM-generated predictions are often outweighed by the degradationto the VLM’s strong pretrained features caused by noisy, update-drivenlearning, resulting in limited and unstable improvements. We thereforepropose Memory-Supported Synergistic Adaptation (MSSA), anovel training-free TTA framework for medical image segmentation.Without updating model parameters, MSSA dynamically selects reli-able image–text predictions to construct an online memory, uses themas text-guided semantic priors, and couples them with cross-image struc-tural alignment for robust adaptation. Specifically, MSSA consists of (i) anoise-aware memory construction module that filters and stabilizes cross-modal predictions, and (ii) a relevance-driven prototype alignment mod-ule that aligns the target sample with structurally consistent memorysamples and their reliable predictions to improve adaptation. Extensiveexperiments on multiple medical segmentation benchmarks demonstratethat MSSA consistently improves VLM-based segmentation models andoutperforms existing fine-tuning-based TTA methods by a clear mar-gin, with gains of up to 12.2% DSC and 11.7% mIoU. Project Page:https://lingrayy.github.io/MSSA/.

ECCV 2026

LogicIR: Logic Gate Networks for Image Restoration

Hongjae Lee, Myungjun Son, Jaeseong Yu, Seung-Won Jung

Image restoration aims to reconstruct high-quality images from degraded low-quality inputs. As the computational demands of image restoration models continue to rise, there is growing interest in lightweight architectures optimized for fast and efficient inference. Logic gate networks (LGNs), which operate using fundamental logic operations such as NAND and XOR, have recently emerged as a promising direction for achieving highly efficient computation. However, their potential remains largely untapped in the domain of image restoration. In this work, we introduce LogicIR, the first LGN specifically designed for image restoration tasks. LogicIR incorporates a UNet-inspired architecture composed entirely of logic gates. In addition, we propose a differentiable bit decoding layer and an index shuffling mechanism that improves information propagation across logic gates. Experimental results across multiple image restoration benchmarks demonstrate that LogicIR achieves strong performance with significantly reduced computational cost, establishing LogicIR as a viable and efficient alternative for image restoration. The source code is available at our project page https://github.com/jimmy9704/LogicIR.

ECCV 2026

LightFusion: A Light-weighted, Double Fusion Framework for Unified Multimodal Understanding and Generation

Zeyu Wang, Zilong Chen, Chenhui Gou, Feng Li, Chaorui Deng, Deyao Zhu, Kunchang Li, Weihao Yu, Haoqin Tu, Haoqi Fan, Cihang Xie

Unified multimodal models have recently shown remarkablegains in both capability and versatility, yet most leading systems are stilltrained from scratch and require substantial computational resources. Inthis paper, we show that competitive performance can be obtained farmore efficiently by strategically fusing publicly available models special-ized for either generation or understanding. Our key design is to re-tain the original blocks while additionally interleaving multimodal self-attention blocks throughout the networks. This double fusion mechanism(1) effectively enables rich multi-modal fusion while largely preservingthe original strengths of the base models, and (2) catalyzes synergisticfusion of high-level semantic representations from the understanding en-coder with low-level spatial signals from the generation encoder. By train-ing with only ∼35B tokens, this approach achieves strong results acrossmultiple benchmarks: 0.91 on GenEval for compositional text-to-imagegeneration, 82.16 on DPG-Bench for complex text-to-image generation,6.06 on GEditBench, and 3.77 on ImgEdit-Bench for image editing. Wewill fully release the entire suite of code, model weights, and datasets tosupport future research on unified multimodal modeling.

ECCV 2026

Learning Spectral and Polarimetric Clues for One-to-Multimodal Novel View Synthesis

Federico Lincetto, Gianluca Agresti, Mattia Rossi, Piergiorgio Sartor, Pietro Zanuttigh

Neural rendering techniques allow for accurate reconstruc-tion of the geometry and color appearance of 3D scenes. Some methodshave extended their use to additional imaging modalities, such as multi-spectral, infrared, or polarimetric data. However, all of these approachesrequire expensive sensors and calibrated setups to capture new multi-modal frames for each new scene. We propose Spectral and PolarimetricImplicit Learned Representation (SPoILeR), a novel method to obtainmulti-view consistent renderings of unconventional modalities for sceneswhere either only RGB frames or very few of the additional modali-ties are available. Thanks to a multimodal pre-training phase, the modellearns the mutual correlation between different modalities. This step al-lows predicting accurate renderings of unconventional modalities duringa fine-tuning phase supervised only by RGB images. Experimental resultsshow that the approach can accurately render infrared, polarimetric, andmultispectral frames for scenes where no input sample captured by thesetypes of sensors is provided.

ECCV 2026

Inductive Visual Logic for Few-Shot Out-Of-Distribution Adaptation in VLMs

Hung-Jen Chen, Yu-Heng Ho, Ting-Yao Huang, Po-Hsiang Hsu, Liyu Chen, Chun-Yi Lee, Min Sun

Generative vision-language models (VLMs) such as Qwen-VLand LLaVA achieve strong zero-shot performance on tasks overlappingwith their pretraining distribution, yet fail on specialized domains wherethe required discriminative features were never learned, a regime weterm distant out-of-distribution (OOD). Standard adaptation methodscannot overcome this representational absence because they operatewithin the encoder’s existing feature space. However, VLMs retain arobust descriptive capacity even when discrimination collapses: a modelthat cannot classify a medical scan can still articulate its visual patterns.Exploiting this asymmetry, we introduce Inductive Visual Logic (IVL), atraining-free framework that constructs classification knowledge from themodel’s surviving descriptive ability. IVL extracts visual traits from few-shot support images through dual-mode prompting, combining semanticdescriptions with primitive visual observations, and organizes them intoper-class trait dictionaries. At inference, hierarchical filtering identifiesspatially grounded trait evidence for classification. Across multiple distant-OOD benchmarks, IVL achieves the highest aggregate accuracy under twoVLM backbones while producing interpretable, trait-traceable predictions.

Keep browsing

More papers from top AI conferences

30+ top conferences, by conference, year, and topic.