GenAI4ELab/papercli-papers huggingface dataset:
CARD: A Multi-Modal Automotive Dataset for Dense 3D Reconstruction in Challenging Road Topography
Authors: Gasser Elazab; Frank Neuhaus; Tilman Koß; Malte Splietker; Aditya Date; Michael Unterreiner; Maximilian Jansen; Olaf Hellwich
Venue: CVPR 2026 (main)

Abstract: Autonomous driving must operate reliably across diverse surfaces to enable safe mobility. However, most driving datasets are captured on well-paved flat roads. Moreover, recent driving datasets primarily provide sparse LiDAR ground truth for images, which is insufficient for assessing fine-grained geometry in depth estimation and completion. To address these gaps, we introduce CARD, a multi-modal driving dataset that delivers quasi-dense 3D ground truth across continuous sequences rich in speed bumps, potholes, irregular surfaces and off-road segments. Our sensor suite includes synchronized global-shutter stereo cameras, front and rear LiDARs, 6-DoF poses from LiDAR-inertial odometry, per-wheel motion traces, and full calibration. Notably, our multi-LiDAR fusion yields  500K valid depth pixels per frame, about 6.5x more than KITTI Depth Completion and 10x more on average than other public driving datasets. The dataset spans  110 km and 4.7 hours across Germany and Italy. In addition, CARD provides 2D bounding boxes targeting road-topography irregularities, enabling accurate benchmarking for both geometry and perception tasks. Furthermore, we introduce a standardized evaluation protocol for road surface irregularities and a stereo-guided depth completion variant that achieves leading performance on CARD. Moreover, we benchmark state-of-the-art depth estimation models to establish strong baselines. We host CARD on Hugging Face with an open source SDK and standardized splits to enable public leaderboards and reproducible evaluation. The CARD dataset is hosted on (https://huggingface.co/CARD-Data).
Next-Scale Prediction: A Self-Supervised Approach for Real-World Image Denoising
Authors: Yiwen Shan; Haiyu Zhao; Peng Hu; Xi Peng; Yuanbiao Gou
Venue: CVPR 2026 (main)

Abstract: Self-supervised real-world image denoising remains a fundamental challenge, arising from the antagonistic trade-off between decorrelating spatially structured noise and preserving high-frequency details. Existing blind-spot network (BSN) methods rely on pixel-shuffle downsampling (PD) to decorrelate noise, but aggressive downsampling fragments fine structures, while milder downsampling fails to remove correlated noise. To address this, we introduce Next-Scale Prediction (NSP), a novel self-supervised paradigm that decouples noise decorrelation from detail preservation. NSP constructs cross-scale training pairs, where BSN takes low-resolution, fully decorrelated sub-images as input to predict high-resolution targets that retain fine details. As a by-product, NSP naturally supports super-resolution of noisy images without retraining or modification. Extensive experiments demonstrate that NSP achieves state-of-the-art self-supervised denoising performance on real-world benchmarks, significantly alleviating the long-standing conflict between noise decorrelation and detail preservation. The code is available at https://github.com/XLearning-SCU/2026-CVPR-NSP.
TEAR: Temporal-aware Automated Red-teaming for Text-to-Video Models
Authors: Jiaming He; Guanyu Hou; Hongwei Li; Zhicong Huang; Kangjie Chen; Yi Yu; Wenbo Jiang; Guowen Xu; Tianwei Zhang
Venue: CVPR 2026 (main)

Abstract: Text-to-Video (T2V) models are capable of synthesizing high-quality, temporally coherent dynamic video content, but the diverse generation also inherently introduces critical safety challenges. Existing safety evaluation methods, which focus on static image and text generation, are insufficient to capture the complex temporal dynamics in video generation. To address this, we propose a TEmporal-aware Automated Red-teaming framework, named TEAR, an automated framework designed to uncover safety risks specifically linked to the dynamic temporal sequencing of T2V models. TEAR employs a temporal-aware test generator optimized via a two-stage approach: initial generator training and temporal-aware online preference learning, to craft textually innocuous prompts that exploit temporal dynamics to elicit policy-violating video output. And a refine model is adopted to improve the prompt stealthiness and adversarial effectiveness cyclically. Extensive experimental evaluation demonstrates the effectiveness of TEAR across open-source and commercial T2V systems with an over 80% attack success rate, a significant boost from the prior best result of 57%.
GGPT: Geometry-Grounded Point Transformer
Authors: Yutong Chen; Yiming Wang; Xucong Zhang; Sergey Prokudin; Siyu Tang
Venue: CVPR 2026 (main)

Abstract: Recent feed-forward networks have achieved remarkable progress in sparse-view 3D reconstruction by predicting dense point maps directly from RGB images. However, they often suffer from geometric inconsistencies and limited fine-grained accuracy due to the absence of explicit multi-view constraints. We introduce the Geometry-Grounded Point Transformer (GGPT), a framework that augments feed-forward reconstruction with reliable sparse geometric guidance. We first propose an improved Structure-from-Motion pipeline based on dense feature matching and lightweight geometric optimisation to efficiently estimate accurate camera poses and partial 3D point clouds from sparse input views. We propose a geometry-guided 3D point transformer that refines dense point maps under explicit partial-geometry supervision using an optimised guidance encoding. Extensive experiments demonstrate that our method provides a principled mechanism for integrating geometric priors with dense feed-forward predictions, producing reconstructions that are both geometrically consistent and spatially complete, recovering fine structures and filling gaps in textureless areas.Trained solely on ScanNet++ with VGGT predictions, GGPT generalises across architectures and datasets, substantially outperforming state-of-the-art feed-forward 3D reconstruction models in both in-domain and out-of-domain settings.
Bulk RNA-seq Guided Multi-modal Detection of Anomalous Regions in Human Cancer via Spatial Transcriptomics
Authors: Hang Shi; Ruocheng Yang; Wenjie You; Zhilin Huang; Daoqiang Zhang; Wei Shao
Venue: CVPR 2026 (main)

Abstract: Spatial transcriptomics (ST) has emerged as a revolutionary approach in the field of tissue analysis that can offer spatial resolved molecular insights for the identification of anomalous regions (AR) on human cancers. Current ST-based methods for detecting AR focus narrowly on the molecular features of local tissue spots, overlooking the matched bulk RNA-seq data that contains crucial diagnostic information. This oversight limits their effectiveness in identifying subtle or heterogeneous tumors, where accurate detection depends on broader genetic context. Besides the genomic signatures, the pathological images can also provide rich visual information to reflect the morphology of AR. To utilize the patient-level diagnostic knowledge and harness complementary information from both histology images and ST, we develop a Bulk RNA-seq Guided Multi-modal Anomalous Regions Detection method (BRGMAR) for the identification of AR from human tissues. Specifically, to effectively model the dependencies in ST, we introduce a Dynamic Multi-Relational Graph Learning (DMRGL) module to adaptively capture complex relationships in ST, including both spatial proximity and gene expression similarity. Then, we design an Optimal Transportation-based Gene Module Alignment (OTGMA) approach to align ST data with patient-level bulk RNA-seq data by matching the compositional and functional similarities of their corresponding gene modules. Finally, we combine the learned genomic features with pathological image representations for accurate AR detection. We evaluate our method on three public available ST datasets for the purpose of identifying cancerous regions from normal tissues, and the experimental results demonstrate the advantage of our method in comparison with the existing studies.
Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysis
Authors: Boyang Dai; Chaoqi Chen; Yizhou Yu
Venue: CVPR 2026 (main)

Abstract: Out-of-distribution (OOD) detection is crucial for ensuring the reliability of deep learning models. Existing methods mostly focus on regular entangled representations to discriminate in-distribution (ID) and OOD data, neglecting the rich contextual information within images. This issue is particularly challenging for detecting near-OOD, as models with simplicity bias struggle to learn discriminative features in disentangled representations. The human visual system can use the co-occurrence of objects in the natural environment to facilitate scene understanding. Inspired by this, we propose an Object-Centric OOD detection framework that learns to capture Object CO-occurrence (OCO) patterns within images. The proposed method introduces a new OOD detection paradigm that understands object co-occurrence within an image by predicting disentangled representations for the test sample, then adaptively divides patterns into three scenarios based on object co-occurrence patterns observed in ID training data, and finally performs OOD detection in a divide-and-conquer manner. By doing so, OCO can distinguish near-OOD by considering the semantic contextual relationships present in their images, avoiding the tendency to focus solely on simple, easily learnable regions. We evaluate OCO through experiments across challenging and full-spectrum OOD settings, demonstrating competitive results and confirming its ability to address both semantic and covariate shifts.
UIKA: Fast Universal Head Avatar from Pose-Free Images
Authors: Zijian Wu; Boyao Zhou; Liangxiao Hu; Hongyu Liu; Yuan Sun; Xuan Wang; Xun Cao; Yujun Shen; Hao Zhu
Venue: CVPR 2026 (main)

Abstract: We present UIKA, a feed-forward animatable Gaussian head model from an arbitrary number of pose-free inputs, including a single image, multi-view captures, and smartphone-captured videos. Unlike the traditional avatar method, which requires a studio-level multi-view capture system and reconstructs a human-specific model through a long-time optimization process, we rethink the task through the lenses of model representation, network design, and data preparation. First, we introduce a UV-guided avatar modeling strategy, in which each input image is associated with a pixel-wise facial correspondence estimation. Such correspondence estimation allows us to reproject each valid pixel color from screen space to UV space, which is independent of camera pose and character expression. Furthermore, we design learnable UV tokens on which the attention mechanism can be applied at both the screen and UV levels. The learned UV tokens can be decoded into canonical Gaussian attributes using aggregated UV information from all input views. To train our large avatar model, we additionally prepare a large-scale, identity-rich synthetic training dataset. Our method significantly outperforms existing approaches in both monocular and multi-view settings. See more details in our project page: https://zijian-wu.github.io/uika-page/.
GroundingME: Exposing the Visual Grounding Gap in MLLMs through Multi-Dimensional Evaluation
Authors: Rang Li; Lei Li; Shuhuai Ren; Hao Tian; Shuhao Gu; Shicheng Li; Zihao Yue; Yudong Wang; Wenhan Ma; Zhe Yang; Jingyuan Ma; Zhifang Sui; Fuli Luo
Venue: CVPR 2026 (main)

Abstract: Visual grounding, localizing objects from natural language descriptions, represents a critical bridge between language and vision understanding. While multimodal large language models (MLLMs) achieve impressive scores on existing benchmarks, a fundamental question remains: can MLLMs truly visually ground with human-like sophistication, or are they merely pattern-matching on simplified datasets? Current benchmarks fail to capture real-world complexity where humans effortlessly navigate intricate references and recognize when grounding is impossible. To rigorously assess MLLMs' true capabilities, we introduce GroundingME, a benchmark that systematically challenges models across four critical dimensions: (1) Discriminative: distinguishing highly similar objects, (2) Spatial: understanding complex relational descriptions, (3) Limited: handling occlusions or tiny objects, and (4) Rejection: recognizing ungroundable queries. Through careful curation combining automated generation with human verification, we create 1,005 challenging examples mirroring real-world complexity. Evaluating 25 state-of-the-art MLLMs reveals a profound capability gap: the best model achieves only 45.1% accuracy, while most score 0% on rejection tasks. We explore two strategies for improvements: (1) test-time scaling selects optimal response by thinking trajectory to improve overall performance by up to 4.5%, and (2) data-mixture training boosts rejection accuracy from 0% to 27.9%. GroundingME thus serves as both a diagnostic tool revealing current limitations in MLLMs and a roadmap toward human-level visual grounding. Project page: https://groundingme.github.io
Aesthetic Camera Viewpoint Suggestion with 3D Aesthetic Field
Authors: Sheyang Tang; Armin Shafiee Sarvestani; Jialu Xu; Xiaoyu Xu; Zhou Wang
Venue: CVPR 2026 (main)

Abstract: The aesthetic quality of a scene depends strongly on camera viewpoint. Existing approaches for aesthetic viewpoint suggestion are either single-view adjustments, predicting limited camera adjustments from a single image without understanding scene geometry, or 3D exploration approaches, which rely on dense captures or prebuilt 3D environments coupled with costly reinforcement learning (RL) searches. In this work, we introduce the notion of 3D aesthetic field that enables geometry-grounded aesthetic reasoning in 3D with sparse captures, allowing efficient viewpoint suggestions in contrast to costly RL searches. We opt to learn this 3D aesthetic field using a feedforward 3D Gaussian Splatting network that distills high-level aesthetic knowledge from a pretrained 2D aesthetic model into 3D space, enabling aesthetic prediction for novel viewpoints from only sparse input views. Building on this field, we propose a two-stage search pipeline that combines coarse viewpoint sampling with gradient-based refinement, efficiently identifying aesthetically appealing viewpoints without dense captures or RL exploration. Extensive experiments show that our method consistently suggests viewpoints with superior framing and composition compared to existing approaches, establishing a new direction toward 3D-aware aesthetic modeling.
AGFT: Alignment-Guided Fine-Tuning for Zero-Shot Adversarial Robustness of Vision-Language Models
Authors: Yubo Cui; Xianchao Guan; Zijun Xiong; Zheng Zhang
Venue: CVPR 2026 (main)

Abstract: Pre-trained vision-language models (VLMs) exhibit strong zero-shot generalization but remain vulnerable to adversarial perturbations. Existing classification-guided adversarial fine-tuning methods often disrupt pre-trained cross-modal alignment, weakening visual-textual correspondence and degrading zero-shot performance. In this paper, we propose an Alignment-Guided Fine-Tuning (AGFT) framework that enhances zero-shot adversarial robustness while preserving the cross-modal semantic structure. Unlike label-based methods that rely on hard labels and fail to maintain the relative relationships between image and text, AGFT leverages the probabilistic predictions of the original model for text-guided adversarial training, which aligns adversarial visual features with textual embeddings via soft alignment distributions, improving zero-shot adversarial robustness. To address structural discrepancies introduced by fine-tuning, we introduce a distribution consistency calibration mechanism that adjusts the robust model's output to match a temperature-scaled version of the pre-trained model's predictions. Extensive experiments across multiple zero-shot benchmarks demonstrate that AGFT outperforms state-of-the-art methods while significantly improving zero-shot adversarial robustness. Code is available at https://github.com/YuboCui/AGFT.
GEM-TFL: Bridging Weak and Full Supervision for Forgery Localization through EM-Guided Decomposition and Temporal Refinement
Authors: Xiaodong Zhu; Yuanming Zheng; Suting Wang; Junqi Yang; Yuhong Yang; Weiping Tu; Zhongyuan Wang
Venue: CVPR 2026 (main)

Abstract: Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments within videos or audio streams, providing interpretable evidence for multimedia forensics and security. While most existing TFL methods rely on dense frame-level labels in a fully supervised manner, Weakly Supervised TFL (WS-TFL) reduces labeling cost by learning only from binary video-level labels. However, current WS-TFL approaches suffer from mismatched training and inference objectives, limited supervision from binary labels, gradient blockage caused by non-differentiable top-k aggregation, and the absence of explicit modeling of inter-proposal relationships. To address these issues, we propose GEM-TFL (Graph-based EM-powered Temporal Forgery Localization), a two-phase classification-regression framework that effectively bridges the supervision gap between training and inference. Built upon this foundation, (1) we enhance weak supervision by reformulating binary labels into multi-dimensional latent attributes through an EM-based optimization process; (2) we introduce a training-free temporal consistency refinement that realigns frame-level predictions for smoother temporal dynamics; and (3) we design a graph-based proposal refinement module that models temporal-semantic relationships among proposals for globally consistent confidence estimation. Extensive experiments on benchmark datasets demonstrate that GEM-TFL achieves more accurate and robust temporal forgery localization, substantially narrowing the gap with fully supervised methods.
Coupling Liquid Time-Constant Encoders with Modern Hopfield Memory
Authors: Bishal Ranjan Swain; Kyung Joo Cheoi; Jaepil Ko
Venue: CVPR 2026 (main)

Abstract: Continuous-time neural networks provide adaptive dynamics, but rely on a single hidden state to encode both fast input fluctuations and longer-term context. This shared representation forces rapidly changing inputs to overwrite slower contextual signals, causing the model to lose past information as new observations arrive. In contrast, biological perceptual systems maintain stable behaviour under evolving sensory input by integrating ongoing signals with stored associative patterns rather than relying on a single evolving state.Motivated by this distinction, we study a simple coupling of Liquid Time-Constant Networks (LTCs) with a Modern Hopfield Network (MHN) that serves as a content-addressable memory. At each time step, the liquid state is projected into a query, the MHN retrieves a memory vector, and the two representations are concatenated before a readout layer. We analyse this coupling under standard norm and Lipschitz assumptions and show that the combined representation remains bounded. We further show that the retrieval map contracts gradients for parameters upstream of the memory query, which provides a mechanism for reducing curvature in the loss landscape.On public time-series benchmarks, the coupled LTC-MHN model improves mean accuracy by 2.3% over competitive recurrent and continuous-time baselines and reduces the estimated Hessian trace by about an order of magnitude relative to a standalone LTC encoder, with the largest gains on classification tasks and competitive performance on a regression task. Qualitative analyses of training curves, loss landscapes, and latent embeddings support the interpretation that Hopfield retrieval smooths optimization and encourages more compact, linearly separable class manifolds. Code will be released upon publication.
OmniGen2: Towards Instruction-Aligned Multimodal Generation
Authors: Chenyuan Wu; Jiahao Wang; Pengfei Zheng; Ruiran Yan; Shitao Xiao; Xin Luo; Yueze Wang; Wanli Li; Xiyan Jiang; Yexin Liu; Junjie Zhou; Ziyi Xia; Ze Liu; Chaofan Li; Haoge Deng; Kun Luo; Bo Zhang; Jiajun Zhang; Dong Liu; Defu Lian; Xinlong Wang; Zhongyuan Wang; Tiejun Huang; Zheng Liu
Venue: CVPR 2026 (main)

Abstract: Multimodal generative models can process instructions in various modalities and demonstrate outstanding performance across a wide range of image generation tasks. However, their robustness in complex real-world scenarios remains limited due to insufficient generalized instruction alignment. We introduce OmniGen2, a unified multimodal generator designed to follow complex, fine-grained instructions. Our core contribution is a two-stage design that first builds a strong, world-knowledge-grounded foundation model and then aligns it using a progressive, multi-task instruction tuning strategy. The foundation model features a streamlined architecture with decoupled decoding for versatile multimodal generation and a novel positional encoding scheme to improve learning efficiency. We ground this model in real-world knowledge using large-scale data construction pipelines. Building on this foundation, we propose a progressive, reinforcement-based alignment process. This phase carefully schedules training tasks and reward signals to foster cross-task knowledge transfer, significantly improving the model's instruction-following capabilities. Our models demonstrate competitive performance on standard benchmarks and our dedicated in-context generation benchmark, OmniContext. We have released our models, code, benchmark, and training datasets at https://github.com/VectorSpaceLab/OmniGen2.
VLM-Loc: Localization in Point Cloud Maps via Vision-Language Models
Authors: Shuhao Kang; Youqi Liao; Peijie Wang; Wenlong Liao; Qilin Zhang; Benjamin Busam; Xieyuanli Chen; Yun Liu
Venue: CVPR 2026 (main)

Abstract: Text-to-point-cloud (T2P) localization aims to infer precise spatial positions within 3D point cloud maps from natural language descriptions, reflecting how humans perceive and communicate spatial layouts through language. However, existing methods largely rely on shallow text-point cloud correspondence without effective spatial reasoning, limiting their accuracy in complex environments. To address this limitation, we propose VLM-Loc, a framework that leverages the spatial reasoning capability of large vision-language models (VLMs) for T2P localization. Specifically, we transform point clouds into bird's-eye-view (BEV) images and scene graphs that jointly encode geometric and semantic context, providing structured inputs for the VLM to learn cross-modal representations bridging linguistic and spatial semantics. On top of these representations, we introduce a partial node assignment mechanism that explicitly associates textual cues with scene graph nodes, enabling interpretable spatial reasoning for accurate localization. To facilitate systematic evaluation across diverse scenes, we present CityLoc, a benchmark built from multi-source point clouds for fine-grained T2P localization. Experiments on CityLoc demonstrate VLM-Loc achieves superior accuracy and robustness compared to state-of-the-art methods. Our code, model, and dataset are available at \href https://github.com/MCG-NKU/nku-3d-vision  repository .