GenAI4ELab/papercli-papers huggingface dataset:
Interpretable Prompts made Edit-Friendly: Token-to-Token Similarity Reduction in dLLMs for Edit-Friendly Hard Prompt Inversion
Authors: Naresh Kumar Devulapally; Shruti Agarwal; Vishal Asnani; Vishnu Suresh Lokhande
Venue: CVPR 2026 (main)

Abstract: Crafting prompts via Prompt Engineering that steer a model's internal representations toward specific and pre-defined outcomes can be time-consuming, often requiring multiple iterations. Hard Prompt Inversion offers a complementary workflow: start from a reference image and generate a prompt that conditions a text-to-image (T2I) model to reconstruct the reference image. Existing inversion methods either yield incoherent text, or produce prompts that are overly sensitive to downstream token edits. We propose a dLLM-based prompt inversion framework that yield prompts that are (i) more interpretable to humans, (ii) better aligned with the reference image, and (iii) designed for downstream token swap and token append operations (aka edit-friendly prompts). The method is plug-and-play, requiring no finetuning of either the T2I model or the dLLM. Experiments across three datasets show a ~10xreduction in inversion time relative to existing prompt-inversion baselines, higher interpretability scores, and significantly higher prompt editability, as measured by TIFA, GPT-V preference scoring, and controlled user studies, all while preserving high-fidelity image generation. By coupling diffusion-time sampling with token-similarity control inside a dLLM decoder, our approach extends prompt inversion beyond reconstruction to downstream token-editing tasks, enabling faster, more transferable prompts that generalize across multiple T2I models.
Egocentric Visibility-Aware Human Pose Estimation
Authors: Peng Dai; Yu Zhang; Feng Yiqiang; Zhen Fan; Yang Zhang
Venue: CVPR 2026 (main)

Abstract: Egocentric human pose estimation (HPE) using a head-mounted device is crucial for various VR and AR applications, but it faces significant challenges due to keypoint invisibility. Nevertheless, none of the existing egocentric HPE datasets provide keypoint visibility annotations, and the existing methods often overlook the invisibility problem, treating visible and invisible keypoints indiscriminately during estimation. As a result, their capacity to accurately predict visible keypoints is compromised. In this paper, we first present Eva-3M, a large-scale egocentric visibility-aware HPE dataset comprising over 3.0M frames, with 435K of them annotated with keypoint visibility labels. Additionally, we augment the existing EMHI dataset with keypoint visibility annotations to further facilitate the research in this direction. Furthermore, we propose EvaPose, a novel egocentric visibility-aware HPE method that explicitly incorporates visibility information to enhance pose estimation accuracy. Extensive experiments validate the significant value of ground-truth visibility labels in egocentric HPE settings, and demonstrate that our EvaPose achieves state-of-the-art performance in both Eva-3M and EMHI datasets.
CoLoGen: Progressive Learning of Concept-Localization Duality for Unified Image Generation
Authors: Yuxin Song; Yu Lu; Haoyuan Sun; Huanjin Yao; Fanglong Liu; Yifan Sun; Haocheng Feng; Hang Zhou; Jingdong Wang
Venue: CVPR 2026 (main)

Abstract: Unified conditional image generation remains difficult because different tasks depend on fundamentally different internal representations. Some require conceptual understanding for semantic synthesis, while others rely on localization cues for spatial precision. Forcing these heterogeneous tasks to share a single representation leads to concept-localization representational conflict.To address this issue, we propose CoLoGen, a unified diffusion framework that progressively learns and reconciles this concept-localization duality. CoLoGen uses a staged curriculum that first builds core conceptual and localization abilities, then adapts them to diverse visual conditions, and finally refines their synergy for complex instruction-driven tasks. Central to this process is the Progressive Representation Weaving (PRW) module, which dynamically routes features to specialized experts and stably integrates their outputs across stages.Experiments on editing, controllable generation, and customized generation show that CoLoGen achieves competitive or superior performance, offering a principled representational perspective for unified image generation.
Topology-aware Feature Propagation for Unsupervised Non-rigid Point Cloud Correspondence
Authors: Haozhe Chen; Rui Li; Zhengbao Wang; Xinhao Zhu; Linjie Li; Tianyu Xiong; Xuan Ouyang; Jiaqi Yang
Venue: CVPR 2026 (main)

Abstract: Unsupervised non-rigid point cloud correspondence aims to predict point-to-point correspondences without annotations. Existing methods leverage the spatial-relation-based feature propagation strategy that includes non-physical connections, which are sensitive to non-rigid deformation. To address this issue, we advocate to learn shape topology robust to non-rigid deformation, and propose the topology-aware feature propagation module integrated into a coarse-to-fine propagation and optimization pipeline. To extract point features robust to non-rigid deformation, we estimate keypoints as superpoints and encode superpoint features with topology weights, which learns reasonable topologies under non-rigid deformation. The vector quantization codebook is leveraged to enhance the original superpoint features with stored representative features across the dataset, improving feature robustness against shape variance. Robust point-wise correspondence is yielded after coarse-to-fine feature fusion and efficient test-time optimization. Extensive experiments on multiple benchmarks demonstrate the state-of-the-art performance of our method.
LifeEval: A Multimodal Benchmark for Assistive AI in Egocentric Daily Life Tasks
Authors: Hengjian Gao; Kaiwei Zhang; Shibo Wang; Mingjie Chen; Qihang Cao; Xianfeng Wang; Yucheng Zhu; Xiongkuo Min; Wei Sun; Dandan Zhu; Guangtao Zhai
Venue: CVPR 2026 (main)

Abstract: The rapid progress of Multimodal Large Language Models (MLLMs) marks a significant step toward artificial general intelligence, offering great potential for augmenting human capabilities. However, their ability to provide effective assistance in dynamic, real-world environments remains largely underexplored. Existing video benchmarks predominantly assess passive understanding through retrospective analysis or isolated perception tasks, failing to capture the interactive and adaptive nature of real-time user assistance. To bridge this gap, we introduce LifeEval, a multimodal benchmark designed to evaluate real-time, task-oriented human-AI collaboration in daily life from an egocentric perspective. LifeEval emphasizes three key aspects: task-oriented holistic evaluation, egocentric real-time perception from continuous first-person streams, and human-assistant collaborative interaction through natural dialogues. Constructed via a rigorous annotation pipeline, the benchmark comprises 4,075 high-quality question-answer pairs across 6 core capability dimensions. Extensive evaluations of 26 state-of-the-art MLLMs on LifeEval reveal substantial challenges in achieving timely, effective and adaptive interaction, highlighting essential directions for advancing human-centered interactive intelligence.
VesMamba: 3D Pulmonary Vessel Segmentation from CT images via Mamba with Structural Perception and Scale-aware Filtering
Authors: Zhipeng Liu; Guilian Chen; Zheng Jiang; Huisi Wu; Jing Qin
Venue: CVPR 2026 (main)

Abstract: Automated 3D pulmonary vessel segmentation from CT images is crucial for improving early screening and assessment of pulmonary vessel related diseases. However, it remains an extremely challenging task due to the complex and tree-like structures of vessels, large scale-variations, and the existence of highly similar tissues in the background. Existing segmentation models either cannot sufficiently capture long-range structural dependencies, which are of great importance in vessel segmentation, or are constrained by insufficient computational resources in clinical settings. In this paper, we propose VesMamba, a novel model for 3D pulmonary vessel segmentation that comprehensively addresses these challenges. Specifically, we first devise a spatial-gated structural perception (SSP) module, which employs Mamba to efficiently capture long-range dependencies. In SSP, we design dynamic spatial attention convolutions (DSAC) for dynamically learning the tree-like 3D vessel structures, providing Mamba with the spatial perception capability to better track the complicated topologies of vessels. Second, we propose an innovative bidirectional scale-aware filter (BSF) module to strengthen the representation capability of the encoder, facilitating our model to focus on vessels of different scales under noise. Moreover, we apply a mask-constrained decoder to further improve segmentation consistency and accuracy, which constrains the inference of adjacent low-layer decoders directly by high-layer masks. Extensive experiments on the public Parse22 and internal Lung79 datasets demonstrate that our method can achieve better performance than SOTAs. Code is available at https://github.com/Lzpbright/VesMamba.
FSFSplatter: Geometrically Accurate Reconstruction with Free Sparse-view Images within 2 minutes
Authors: Yibin Zhao; Yihan Pan; Jun Nan; Liwei Chen; Jianjun Yi
Venue: CVPR 2026 (main)

Abstract: Gaussian Splatting has become a leading reconstruction technique, known for its high-quality novel view synthesis and detailed reconstruction. However, most existing methods require dense, calibrated views. Reconstruction from free sparse-view images often leads to poor surface due to limited overlap and overfitting.We introduce FSFSplatter for fast geometrically accurate reconstruction from free sparse-view images. Our method integrates end-to-end dense Gaussian scene initialization and geometry-enhanced scene optimization.Specifically, FSFSplatter employs a large transformer to encode multi-view images and generates a dense and geometrically consistent Gaussian scene initialization via a batch based self-splitting Gaussian head. It eliminates local floaters through contribution-based pruning and mitigates overfitting by leveraging depth and multi-view feature supervision, along with differentiable camera parameters within 2 minutes.FSFSplatter outperforms current state-of-the-art methods on widely used DTU, Replica, and BlendedMVS datasets.
Visual Grounding for Object Questions
Authors: Martin Nicolas Everaert; Xiruo Liu; Hiroyuki Takeda; Raja Bala; Vivek Yadav; Vidya Narayanan
Venue: CVPR 2026 (main)

Abstract: Current visual grounding research remains limited for practical applications, because existing techniques primarily focus on direct visual queriesCurrent visual grounding research remains limited for practical applications, because existing tasks primarily focus on direct visual queries (e.g., "find the red car") or reading visible text (e.g., "what is the title of this book?"), rather than supporting general questions about objects (e.g., "how comfortable are these earbuds?"). We introduce the novel problem of Visual Grounding for Object Questions (VGOQ). Unlike previous tasks that ground only what is directly visible in images, VGOQ handles open-ended general questions about objects, including concepts such as ease and comfort of use, and aims to identify visual evidence or context that would support an answer. This unexplored problem has immediate practical value, particularly in designing and optimizing product imagery in e-commerce stores. As initial steps toward this task, we develop two automated data generation techniques, which serve to train a lightweight visual grounding model, and to evaluate visual grounding approaches on the resulting synthetic benchmarks, ABO-VGOQ and VizWiz-VGOQ. Our results provide initial evidence that VGOQ represents a meaningful research direction: current SoTA visual grounding performance decreases from 52% gIoU to 37% gIoU when questions are rephrased from visual questions (segmentation of the answer) to general object questions (segmentation of visual evidence). On our new benchmarks, our lightweight model outperforms prior models while being much smaller. Project page: https://martin-ev.github.io/vgoq.
HySeg: Learning Generative Priors for Structure-Aware Remote Sensing Segmentation
Authors: Jie Qiu; Xin Li; Fan Yang; Yan Wang; Dong Yu; Changying Wang; Linwei Dai; Yongxiang Chen; Youqin Chen; Jianzhang Chen
Venue: CVPR 2026 (main)

Abstract: High-resolution remote sensing imagery exhibits complex spatial regularities where topology, continuity, and region adjacency  govern semantic organization. However, existing remote sensing image semantic segmentation (RSISS) networks, being predominantly discriminative, estimate strong posteriors from data while lacking generative priors that encode such structural dependencies. This imbalance leads to fragmented boundaries, texture overfitting, and poor cross-dataset generalization. We address this challenge by reformulating RSISS as posterior inference grounded in generative structural priors, introducing  HySeg , a hybrid generative-discriminative segmentation paradigm that learns structure-consistent priors through generative modeling and guides posterior inference for remote sensing segmentation. At its core, the MeanStruct module, a MeanFlow-based generative prior learner, models semantic topology as a continuous stochastic field, while the Prior-to-Affinity Projection (P2A) dynamically transforms this field into topology-aware, class-conditional affinities that guide posterior inference in the Dynamic Affinity-driven Segmentation (DAS) head. Our approach is model-agnostic and seamlessly integrates with diverse backbones, consistently improving structural coherence and generalization. Across four challenging RSISS benchmarks, HySeg achieves state-of-the-art performance and advances remote sensing segmentation from appearance-based perception to structural reasoning. Our code  is available at https://github.com/HeryJie/HySeg.
Authorize-on-Demand: Dynamic Authorization with Legality-Aware Intellectual Property Protection for VLMs
Authors: Lianyu Wang; Meng Wang; Huazhu Fu; Daoqiang Zhang
Venue: CVPR 2026 (main)

Abstract: The rapid adoption of vision-language models (VLMs) has heightened the demand for robust intellectual property (IP) protection of these high-value pretrained models. Effective IP protection should proactively confine model deployment within authorized domains and prevent unauthorized transfers. However, existing methods rely on static training-time definitions, limiting flexibility in dynamic environments and often producing opaque responses to unauthorized inputs. To address these limitations, we propose a novel dynamic authorization with legality-aware intellectual property protection (AoD-IP) for VLMs, a framework that supports authorize-on-demand and legality-aware assessment. AoD-IP introduces a lightweight dynamic authorization module that enables flexible, user-controlled authorization, allowing users to actively specify or switch authorized domains on demand at deployment time. This enables the model to adapt seamlessly as application scenarios evolve and provides substantially greater extensibility than existing static-domain approaches. In addition, AoD-IP incorporates a dual-path inference mechanism that jointly predicts input legality-aware and task-specific outputs. Comprehensive experimental results on multiple cross-domain benchmarks demonstrate that AoD-IP maintains strong authorized-domain performance and reliable unauthorized detection, while supporting user-controlled authorization for adaptive deployment in dynamic environments.
R4Det: 4D Radar-Camera Fusion for High-Performance 3D Object Detection
Authors: Zhongyu Xia; Yousen Tang; Yongtao Wang; Zhifeng Wang; Weijun Qin
Venue: CVPR 2026 (main)

Abstract: 4D radar-camera sensing configuration has gained increasing importance in autonomous driving. However, existing 3D object detection methods that fuse 4D Radar and camera data confront several challenges. First, their absolute depth estimation module is not robust and accurate enough, leading to inaccurate 3D localization. Second, the performance of their temporal fusion module will degrade dramatically or even fail when the ego vehicle's pose is missing or inaccurate. Third, for some small objects, the sparse radar point clouds may completely fail to reflect from their surfaces. In such cases, detection must rely solely on visual unimodal priors. To address these limitations, we propose R4Det, which enhances depth estimation quality via the Panoramic Depth Fusion module, enabling mutual reinforcement between absolute and relative depth. For temporal fusion, we design a Deformable Gated Temporal Fusion module that does not rely on the ego vehicle's pose. In addition, we built an Instance-Guided Dynamic Refinement module that extracts semantic prototypes from 2D instance guidance. Experiments show that R4Det achieves state-of-the-art 3D object detection results on the TJ4DRadSet and VoD datasets.
Decision Boundary-aware Generation for Long-tailed Learning
Authors: Jiacheng Yang; Ruichi Zhang; Chikai Shang; Mengke Li; Xinyi Shang; Junlong Gao; Yonggang Zhang; Yang Lu
Venue: CVPR 2026 (main)

Abstract: Long-tailed data bias decision boundaries toward head classes and degrade tail class accuracy. Diffusion-based generative augmentation address this problem by generating additional data, while head-to-tail transfer further mitigate the generator bias inherit from long-tailed dataset. However, we show that while head-to-tail transfer helps balance the decision space of the classifier, it also induces latent non-local feature mixing that entangles inter-class features, causing decision boundary overlap and tail class distribution shift. To address this, we first identify the problem of boundary ambiguity and then propose Decision Boundary-aware Generation (DBG) framework, which promotes near-boundary representation learning by generating informative near-boundary samples. Overall, DBG rebalances the long-tailed dataset while yielding more separable decision space for long-tailed learning. Across standard long-tailed benchmarks, DBG consistently improves tail class and overall accuracy with less inter-class overlap. The code of DBG is available at https://github.com/keepdigitalabc-svg/DBG.
When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness
Authors: Sunoh Kim; Daeho Um
Venue: CVPR 2026 (main)

Abstract: Vision-language models such as CLIP have achieved remarkable zero-shot recognition capabilities, yet their robustness against adversarial perturbations remains limited. Test-time counterattack (TTC) was recently proposed to improve CLIP's robustness by perturbing an input image to steer it away from a corrupted state during inference. However, TTC remains fragile under strong attacks because its counterattack relies on a directly corrupted original view and employs a noise-driven hard-gating scheme that cannot adapt to varying corruption severity. To address these limitations, we introduce Multi-view guided Adaptive Counterattack (MAC), which performs counterattacks for multi-view with corruption-aware soft weighting. Specifically, MAC first constructs augmented views of an input image to obtain diverse embeddings. It then performs counterattacks to refine corrupted embeddings of views. Next, MAC adaptively scales the counterattack intensity for each view based on its estimated corruption degree. Finally, the adaptively counterattacked views are aggregated to yield a robust final prediction. Extensive experiments across 20 datasets and diverse attack scenarios demonstrate that MAC substantially improves robustness while preserving high inference speed and memory efficiency with its tuning-free design. Our code is available at https://github.com/sunoh-kim/MAC.
LLaDA-MedV: Exploring Large Language Diffusion Models for Biomedical Image Understanding
Authors: Xuanzhao Dong; Wenhui Zhu; Xiwen Chen; Zhipeng Wang; Peijie Qiu; Shao Tang; Xin Li; Yalin Wang
Venue: CVPR 2026 (main)

Abstract: Autoregressive models (ARMs) have long dominated the landscape of biomedical vision-language models (VLMs). Recently, masked diffusion models such as LLaDA have emerged as promising alternatives, yet their application in the biomedical domain remains largely underexplored. To bridge this gap, we introduce LLaDA-MedV, the first large language diffusion model tailored for biomedical image understanding through vision instruction tuning. LLaDA-MedV achieves relative performance gains of 7.855% over LLaVA-Med and 1.867% over LLaDA-V in the open-ended biomedical visual conversation task, and sets new state-of-the-art accuracy on the closed-form subset of three VQA benchmarks: 84.93% on VQA-RAD, 92.31% on SLAKE, and 95.15% on PathVQA. Furthermore, a detailed comparison with LLaVA-Med suggests that LLaDA-MedV is capable of generating reasonably longer responses by explicitly controlling response length, which can lead to more informative outputs. We also conduct an in-depth analysis of both the training and inference stages, highlighting the critical roles of initialization weight selection, fine-tuning strategies, and the interplay between sampling steps and response repetition. The code and model weight is released at https://github.com/LLM-VLM-GSL/LLaDA-MedV.
Attribution-Guided Model Rectification of Unreliable Neural Network Behaviors
Authors: Peiyu Yang; Naveed Akhtar; Jiantong Jiang; Ajmal Mian
Venue: CVPR 2026 (main)

Abstract: The performance of neural network models deteriorates due to their unreliable behavior on non-robust features of corrupted samples. Owing to their opaque nature, rectifying models to address this problem often necessitates arduous data cleaning and model retraining, resulting in huge computational and manual overhead. In this work, we leverage rank-one model editing to establish an attribution-guided model rectification framework that effectively locates and corrects model unreliable behaviors. We first distinguish our rectification setting from existing model editing, yielding a formulation that corrects unreliable behavior while preserving model performance and reducing reliance on large budgets of cleansed samples. We further reveal a bottleneck of model rectifying arising from heterogeneous editability across layers. To target the primary source of misbehavior, we introduce an attribution-guided layer localization method that quantifies layer-wise editability and identifies the layer most responsible for unreliabilities. Extensive experiments demonstrate the effectiveness of our method in correcting unreliabilities observed for neural Trojans, spurious correlations and feature leakage. Our method shows remarkable performance by achieving its editing objective with as few as a single cleansed sample, which makes it appealing for practice.