GenAI4ELab/papercli-papers huggingface dataset:
Phrase-grounded APO for Improving Chest X-ray Report Generation
Authors: Raziuddin Mahmood; Tanveer Syeda-Mahmood
Venue: CVPR 2026 (main)

Abstract: The deployment of automatic radiology report generator (RRG) models in clinical workflows is being hampered by the lack of factual correctness in the produced reports. Existing methods to improve the report generators use alignment approaches that require pairs of ground truth preferred and dis-preferred responses. As these are not available at inference time in clinical workflows, new alignment methods are needed to improve report quality at inference time. In this paper, we present a new phrase-grounded automatic preference optimization (APO) alignment method which offers such improvement during inference without needing additional ground truth. Specifically, the method generates surrogate ground truth preference data for alignment automatically from the RRG model response itself though fact-checking and LLM-prompted correction. We also develop a novel APO loss function that combines preference response alignment loss with phrasal grounding loss paying attention to both the description of the finding and its image location. We show that this method of alignment, on the average, improves the report quality at inference time by 30-40% across various SOTA report generators as tested on multi-institutional chest X-ray datasets.
Unleashing Stealthy Backdoor Pandemic by Infecting a Single Diffusion Model
Authors: Mohaiminul Al Nahian; Abeer Matar Almalky; Sabbir Ahmed; Abdullah Al Arafat; Mamshad Nayeem Rizve; Adnan Siraj Rakin
Venue: CVPR 2026 (main)

Abstract: The remarkable success of modern Deep Neural Networks (DNNs) can be primarily attributed to having access to compute resources and high-quality labeled data, which is often costly and challenging to acquire. Recently, text-to-image Diffusion Models (DMs) have emerged as powerful data generators to augment training datasets. Machine learning practitioners often utilize off-the-shelf third-party DMs for generating synthetic data without domain-specific expertise or adaptation. Such a practice leads to a novel and insidious threat: a diffusion model infected with a backdoor can effectively spread into a large number of downstream models, causing a backdoor pandemic. To achieve this for the first time, we propose Eidolon, designed and optimized to stealthily transfer the backdoor injected into a single diffusion model into virtually an unlimited number of downstream models without any active attacker role in the downstream training tasks. Proposed Eidolon not only makes the attack stealthier and effective, but it also enforces a strict threat model for injecting a backdoor into the downstream model compared to conventional backdoor attacks. We propose four necessary tests that a successful backdoor attack on the diffusion model should pass to cause a backdoor pandemic. Our evaluation across a wide range of benchmark datasets and model architectures exhibits that only our attack successfully passes these tests, causing widespread pandemic across many downstream models. Code is available at https://github.com/ML-Security-Research-LAB/Eidolon
Joint-Aligned Latent Action: Towards Scalable VLA Pretraining in the Wild
Authors: Hao Luo; Ye Wang; Wanpeng Zhang; Haoqi Yuan; Yicheng Feng; Haiweng Xu; Sipeng Zheng; Zongqing Lu
Venue: CVPR 2026 (main)

Abstract: Despite progress, Vision-Language-Action models (VLAs) are limited by a scarcity of large-scale, diverse robot data. While human manipulation videos offer a rich alternative, existing methods are forced to choose between small, precisely-labeled datasets and vast in-the-wild footage with unreliable hand tracking labels. We present JALA, a pretraining framework that learns Jointly-Aligned Latent Actions. JALA bypasses full visual dynamic reconstruction, instead learns a predictive action embedding aligned with both inverse dynamics and real actions. This yields a transition-aware, behavior-centric latent space for learning from heterogeneous human data. We scale this approach with UniHand-Mix, a 7.5M video corpus (>2,000 hours) blending laboratory and in-the-wild footage. Experiments demonstrate that JALA generates more realistic hand motions in both controlled and unconstrained scenarios, significantly improving downstream robot manipulation performance in both simulation and real-world tasks. These results indicate that jointly-aligned latent actions offer a scalable pathway for VLA pretraining from human data.
SIGMA: Selective-Interleaved Generation with Multi-Attribute Tokens
Authors: Xiaoyan Zhang; Zechen Bai; Haofan Wang; Yiren Song
Venue: CVPR 2026 (main)

Abstract: Recent unified models such as Bagel demonstrate that paired image-edit data can effectively align multiple visual tasks within a single diffusion transformer. However, these models remain limited to single-condition inputs and lack the flexibility needed to synthesize results from multiple heterogeneous sources. We present SIGMA (Selective-Interleaved Generation with Multi-Attribute Tokens), a unified post-training framework that enables interleaved multi-condition generation within diffusion transformers. SIGMA introduces selective multi-attribute tokens, including style, content, subject and identity tokens, which allow the model to interpret and compose multiple visual conditions in an interleaved text-image sequence. Through post-training on the Bagel unified backbone with 700K interleaved examples, SIGMA supports compositional editing, selective attribute transfer and fine-grained multimodal alignment. Extensive experiments show that SIGMA improves controllability, cross-condition consistency and visual quality across diverse editing and generation tasks, with substantial gains over Bagel on compositional tasks. Code is available at https://github.com/auihund/SIGMA.
TaskForce: Cooperative Multi-agent Reinforcement Learning for Multi-task Optimization
Authors: Wonhyeok Choi; Kyumin Hwang; Jihun Park; Kyoungmin Lee; Seunghun Lee; Jaeyeul Kim; Minwoo Choi; Sunghoon Im
Venue: CVPR 2026 (main)

Abstract: Multi-task learning (MTL) involves the simultaneous optimization of multiple task-specific losses, often leading to gradient conflicts and scale imbalances that result in negative transfer. While existing multi-task optimization methods attempt to mitigate these challenges, they either lack the stochasticity needed to escape poor local minima or fail to explicitly address conflicts at the gradient level. In this work, we propose TaskForce, a novel multi-task optimization framework incorporating cooperative multi-agent reinforcement learning (MARL), where agents learn to find an effective joint optimization strategy based on their respective task gradients and losses. To keep the optimization process compact yet informative, agents observe a summary of the training dynamics that consists of the gradient Gram matrix---capturing both gradient magnitudes and pairwise alignments---and task loss values. Each agent then predicts the balancing parameters that determine the weight of their contribution to the final gradient update. Crucially, we design a hybrid reward function that incorporates both gradient-based signals and loss improvement dynamics, enabling agents to effectively resolve gradient conflicts and avoid poor convergence by considering both direct gradient information and the resulting impact on loss reduction. TaskForce achieves consistent improvements over state-of-the-art MTL baselines on NYU-v2, Cityscapes, and QM9, demonstrating the promise of cooperative MARL in complex multi-task scenarios.
MMLandmarks: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding
Authors: Oskar Kristoffersen; Alba Reinders Sánchez; Morten Rieger Hannemose; Anders Bjorholm Dahl; Dim P. Papadopoulos
Venue: CVPR 2026 (main)

Abstract: Geo-spatial analysis of our world benefits from a multimodal approach, as every single geographic location can be described in numerous ways (images from various viewpoints, textual descriptions, geographic coordinates, etc.). Current benchmarks have limited coverage across modalities, leading to specialized models that perform well in their respective domains, but do not fully take advantage of other geo-spatial modalities. We introduce the Multi-Modal Landmark dataset (MMLandmarks), a benchmark composed of four modalities: 197k high-resolution aerial images, 329k ground-view images, textual information, and geographic coordinates for 18,557 distinct landmarks in the United States. The MMLandmarks dataset has a one-to-one landmark level correspondence across every modality, which enables training and benchmarking models for various geo-spatial tasks, including cross-view Ground-to-Satellite retrieval, ground and satellite geolocalization, Text-to-Image, and Text-to-GPS retrieval. We show that current specialized and off-the-shelf foundation models cannot be trivially used to solve this variety of geo-spatial tasks, illustrating a gap where multimodal datasets lead to broader geo-spatial understanding. We employ a simple CLIP-inspired baseline that reflects versatility and broad generalization when trained with MMLandmarks.
Flow Matching for Multimodal Distributions
Authors: Gaoxiang Luo; Frank Cole; Sihang Zhang; Yuxiang Wan; Yulong Lu; Ju Sun
Venue: CVPR 2026 (main)

Abstract: Recently, vision foundation models have been shown to boost the efficiency of flow-based generative models by revealing the intrinsic union-of-manifold structures and lowering the complexity of the latent/target distribution. In this paper, we exploit the multimodality aspect of the union-of-manifold structures, and aim to further improve the learning and inference efficiency for flow-matching models. To this end, we propose an efficient source and coupling co-design method termed Mixture-Modeling Flow Matching (MM-FM), by integrating a data-adaptive multimodal source distribution (implemented as Gaussian mixture models) and mode-dependent data coupling. The former shortens the distance between the source and the target, and the latter promotes local and straighter flows. We also derive theoretical results to confirm our intuition in a quantitative sense. In our experiments on ImageNet256x256 with multimodal DINOv2-B latents, MM-FM exhibits superior learning efficiency and state-of-the-art unconditional generation quality: FID=2.74 with autoguidance in only 80 epochs.
From Attraction to Equilibrium: Physics-Inspired Semantic Gravitons for Zero-Shot Anomaly Detection
Authors: Yuwen Pan; Yuan Wang; Shaohui Li; Zhi Li; Yu Liu; You He
Venue: CVPR 2026 (main)

Abstract: Zero-shot anomaly detection (ZSAD) aims to identify unseen anomalies without abnormal supervision, which is essential for open-world scenarios. Recent vision-language models such as CLIP enable anomaly reasoning through shared visual-textual embeddings, but existing methods often rely on coarse prompt fusion, leading to unstable alignment and imprecise localization under domain shifts. To address this issue, we propose the Semantic Graviton Network (SGNet), a physics-inspired framework that models multimodal alignment as an adaptive potential field. We introduce semantic gravitons, learnable dynamic mediators that bridge visual and textual modalities by establishing localized semantic equilibria through attraction and equilibrium forces. A graviton interaction network alternates text-to-graviton and vision-to-graviton coupling to progressively refine multimodal correspondence, while an energy-based potential regularization further stabilizes the interaction process. Extensive experiments on ten industrial and medical benchmarks show that SGNet achieves state-of-the-art performance for zero-shot anomaly detection.
Spatia: Video Generation with Updatable Spatial Memory
Authors: Jinjing Zhao; Fangyun Wei; Zhening Liu; Hongyang Zhang; Chang Xu; Yan Lu
Venue: CVPR 2026 (main)

Abstract: Existing video generation models struggle to maintain long-term spatial and temporal consistency due to the dense, high-dimensional nature of video signals. To overcome this limitation, we propose Spatia, a spatial memory-aware video generation framework that explicitly preserves a 3D scene point cloud as persistent spatial memory. Spatia iteratively generates video clips conditioned on this spatial memory and continuously updates it through visual SLAM. This dynamic-static disentanglement design enhances spatial consistency throughout the generation process while preserving the model's ability to produce realistic dynamic entities. Furthermore, Spatia enables applications such as explicit camera control and 3D-aware interactive editing, providing a geometrically grounded framework for scalable, memory-driven video generation.
SegGBC: Justifiable Coarse-to-Fine Granular-Ball Computing for Enhancing Clustering Image Segmentation
Authors: Qianpeng Chong; Wenyi Zeng; Xiuxuan Shen; Jiajie Li; Qian Yin; Xin Zheng
Venue: CVPR 2026 (main)

Abstract: As an emerging multi-granularity clustering paradigm, granular-ball computing (GBC) hierarchically represents samples through granular-balls (GBs) to capture compact, multi-scale features. Nevertheless, its effective application to clustering-based segmentation methods (CSMs) remains challenging due to two key issues: representing intrinsic uncertainties and defining a justifiable, semantics-aware quality criterion. To address them, the first segmentation framework based on GBC (SegGBC) is proposed to alleviate the single-granularity limitation of existing CSMs. Concretely, we leverage intuitionistic fuzzy sets (IFS) to explicitly quantify image uncertainty: membership and non-membership encode evidence, and the IFS hesitation degree models residual ambiguity. In addition, a semantic compactness metric criterion (SCM_GB) is designed to characterize semantic information by considering the "stable region" in conjunction with the overall density of GBs. The proposal of "stable region" ensures robust semantics concurrently with high computational efficiency. Extensive experiments demonstrate that the proposed SegGBC achieves promising performance for segmentation. The proposed segmentation GB representation is a plug-and-play front-end, significantly boosting the performance of CSMs by >+3.25% SA and >+3.92% mIoU on standard image and COCO benchmarks. Code is available at supplementary material.
GaussianMatch: Semi-Supervised Regression with Pseudo-Label Filtering via Multi-View Gaussian Consistency
Authors: Yin Wang; Hao Lu; Zixuan Wang; Zhen Qin; Li Kuang; Mengchu Zhou; Shuiguang Deng
Venue: CVPR 2026 (main)

Abstract: Semi-Supervised Regression (SSR) is essential in domains like sentiment analysis and healthcare where labeled data is limited but unlabeled data is plentiful. Despite its practical importance, SSR remains underexplored due to the lack of effective pseudo-labeling strategies for continuous outputs. Unlike classification, regression lacks inherent confidence measures, making it harder to filter and trust pseudo-labels. This limitation permits low-quality pseudo-labels to propagate during training without proper validation, significantly  amplifying prediction errors in SSR frameworks. In this work, we propose GaussianMatch, a novel SSR framework enabling high-quality pseudo-label filtering, which selects reliable pseudo-labels through multi-view prediction consistency under feature-space smoothness assumptions. It has introduces two key innovations: 1) Gaussian Consistency Filter (GCF) that quantifies prediction consistency across weakly augmented views through Gaussian similarity scoring, retaining pseudo-labels only when all predictions fall within a confidence interval; 2) Adaptive Gaussian Standard Deviation Smoothing (AGDS) that enhances GCF's robustness through a Bayesian-regularized curriculum that phases confidence intervals from warm-up conservative bounds to progressively tightened thresholds. The use of AGDS ensures stable and reliable pseudo-label filtering throughout training. Extensive experiments demonstrate that GaussianMatch performs strongly across varying data conditions, showing notable robustness under extreme label scarcity. For instance, it outperforms the state of the art on UTKFace (30 labels), reducing error by 15.36% and improving the Coefficient of Determination by 50.21%. Our code is available at https://github.com/pywin/GaussianMatch.
Fresco: Frequency-Spatial Consistent Optimization for Fine-Grained Head Avatar Modeling
Authors: Shikun Zhang; Yong Li; Yiqun Wang; Qiuhong Ke; Cunjian Chen
Venue: CVPR 2026 (main)

Abstract: We propose Fresco, a unified optimization pipeline designed to mitigate early over-sharpening, and cross-view drifting in head avatar reconstruction. Fresco combines a Laplacian-pyramid-based frequency curriculum with UV-space consistency regularization to progressively enhance reconstruction quality. The optimization begins by stabilizing low-frequency appearance in the image domain, which suppresses spurious details and promotes reliable convergence. As learning proceeds, consistency across different viewpoints is reinforced through pixel-level alignment on shared UV texture coordinates. Finally, high-frequency components are refined under explicit frequency-band constraints, and seam boundary regularization is applied to preserve local continuity. By optimizing in a frequency- and UV-aligned space, Fresco achieves robust convergence without pseudo high-frequency artifacts and yields consistent, high-fidelity results across views. Experiments on the NeRSemble dataset validate the effectiveness of our design, outperforming previous state-of-the-art methods.
Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation
Authors: Tianming Liang; Haichao Jiang; Yuting Yang; Chaolei Tan; Shuai Li; Wei-Shi Zheng; Jian-Fang Hu
Venue: CVPR 2026 (main)

Abstract: Referring video object segmentation (RVOS) aims to identify, track and segment the objects in a video based on language descriptions, which has received great attention in recent years. However, existing datasets remain focus on short video clips within several seconds, with salient objects visible in most frames. To advance the task towards more practical scenarios, we introduce Long-RVOS, a large-scale benchmark for long-term referring video object segmentation. Long-RVOS contains 2,000+ videos of an average duration exceeding 60 seconds, covering a variety of objects that undergo occlusion, disappearance-reappearance and shot changing. The objects are manually annotated with three different types of descriptions to individually evaluate the understanding of static attributes, motion patterns and spatiotemporal relationships. Moreover, we introduce two new metrics to assess the temporal and spatiotemporal consistency. We benchmark 7 state-of-the-art methods on Long-RVOS to show that current approaches struggle severely with the long-video challenges. We further propose ReferMo, a promising baseline method that integrates motion information to expand the temporal receptive field, and employs a local-to-global architecture to capture both short-term dynamics and long-term dependencies. We hope that Long-RVOS and our baseline can drive future RVOS research towards more realistic and long-form videos. Our dataset and code is available at https://isee-laboratory.github.io/Long-RVOS.
ORV: 4D Occupancy-centric Robot Video Generation
Authors: Xiuyu Yang; Bohan Li; Shaocong Xu; Nan Wang; Chongjie Ye; Zhaoxi Chen; Minghan Qin; Yikang Ding; Zheng Zhu; Xin Jin; Hang Zhao; Hao Zhao
Venue: CVPR 2026 (main)

Abstract: Recent embodied intelligence suffers from data scarcity, while conventional simulators lack visual realism. Controllable video generation is emerging as a promising data engine, yet current action-conditioned methods still fall short: generated videos are limited in fidelity and temporal consistency, poorly aligned with controls, and often constrained to singleview settings. We attribute these issues to the representational gap between sparse control inputs and dense pixel outputs. Thus, we introduce ORV, a 4D occupancy-centric framework for robot video generation that couples action priors with occupancy-derived visual priors. Concretely, we align chunked 7-DoF actions with video latents via an Action-Expert AdaLN modulation, and inject 2D renderings of 4D semantic occupancy into the generation process as soft guidance. Meanwhile, a central obstacle is the lack of occupancy data for embodied scenarios; we therefore curate ORV-Data, a large-scale, high-quality 4D semantic occupancy dataset of robot manipulation. Across BridgeV2, DROID, and RT-1, ORV improves video generation quality and controllability, achieving 18.8% lower FVD than state of the art, +3.5% success rate on visual planning, and +6.4% success rate on policy learning. Beyond singleview generation, ORV natively supports multiview consistent synthesis and enables simulation-to-real transfer despite significant domain gaps. Code, models, and data will be released upon acceptance.
ParkGaussian: Surround-view 3D Gaussian Splatting for Autonomous Parking
Authors: Xiaobao Wei; Zhangjie Ye; Yuxiang Gu; Zunjie Zhu; Yunfei Guo; Yingying Shen; Shan Zhao; Ming Lu; Haiyang Sun; Bing Wang; Guang Chen; Rongfeng Lu; Hangjun Ye
Venue: CVPR 2026 (main)

Abstract: Parking is a critical task for autonomous driving systems (ADS), with unique challenges in crowded parking slots and GPS-denied environments. However, existing works focus on 2D parking slot perception, mapping, and localization, 3D reconstruction remains underexplored, which is crucial for capturing complex spatial geometry in parking scenarios. Naively improving the visual quality of reconstructed parking scenes does not directly benefit autonomous parking, as the key entry point for parking is the slots perception module. To address these limitations, we curate the first benchmark named ParkRecon3D, specifically designed for parking scene reconstruction. It includes sensor data from four surround-view fisheye cameras with calibrated extrinsics and dense parking slot annotations. We then propose ParkGaussian, the first framework that integrates 3D Gaussian Splatting (3DGS) for parking scene reconstruction. To further improve the alignment between reconstruction and downstream parking slot detection, we introduce a slot-aware reconstruction strategy that leverages existing parking perception methods to enhance the synthesis quality of slot regions. Experiments on ParkRecon3D demonstrate that ParkGaussian achieves state-of-the-art reconstruction quality and better preserves perception consistency for downstream tasks. The code and dataset will be released.
Hearing the Room Through the Shape of the Drum: Modal-Guided Sound Recovery from Multi-Point Surface Vibrations
Authors: Shai Bagon; Matan Kichler; Mark Sheinin
Venue: CVPR 2026 (main)

Abstract: Optical vibration sensing enables recovering the scene sound directly from the surface vibration of nearby objects, turning everyday objects into "visual microphones". However, most prior methods had focused on capturing the vibrations of specific objects with highly favorable vibration responses. These include objects where the surface vibrations are generated by the object itself (e.g., speaker membrane or guitar body) or objects consisting of a thin membrane which is highly reactive to sound (e.g., a chip bag or the leaf of a plant).In this paper, we tackle sound recovery for a more challenging class of solid objects whose vibration responses are poor or highly resonant. We simultaneously capture vibrations for multiple surface points on the object using a speckle-based vibrometry imaging system. Then, we derive a novel physics-guided vibration formation model that relates the scene sound source to the captured multi-point multi-axis vibrations via the object's vibrational modes. The model is then used to reverse the resonant transfer function of the vibrating object, fusing the plurality of vibration signals to estimate the original sound source of the scene. We evaluate our approach by recovering sound from a variety of everyday objects, demonstrating that it significantly outperforms traditional single-point speckle vibrometry in challenging scenarios where it performs poorly.