GenAI4ELab/papercli-papers huggingface dataset:
SIF: Semantically In-Distribution Fingerprints for Large Vision-Language Models
Authors: Yifei Zhao; Qian Lou; Mengxin Zheng
Venue: CVPR 2026 (main)

Abstract: The public accessibility of Large Vision-Language Models (LVLMs) raises serious concerns about unauthorized model reuse and intellectual property infringement. Existing ownership verification approaches often rely on semantically abnormal queries or out-of-distribution responses as fingerprints, which are easily recognized and removed by adversaries. We first expose this vulnerability through the Semantic Divergence Attack (SDA), which detects and filters fingerprint checks by measuring semantic divergence between a stolen model and a reference model, showing that existing fingerprints are not semantic-preserving, easy to detect and bypass, and lacking robustness. To address these weaknesses, we propose SIF (Semantically In-Distribution Fingerprints), a non-intrusive ownership verification framework requiring no parameter modification. SIF introduces Semantic-Aligned Fingerprint Distillation (SAFD), which distills text-generation watermark signals--originally designed for text ownership protection rather than model protection--into the visual modality, enabling semantically coherent yet fingerprinted responses. Robust-Fingerprint Optimization (RFO) further simulates worst-case representation perturbations, ensuring resilience to model modifications such as fine-tuning and quantization. Extensive experiments on LLaVA-1.5 and Qwen2.5-VL demonstrate that SIF achieves superior stealthiness and robustness, providing a practical solution for LVLM copyright protection.
Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models
Authors: Yexing Xu; Wei Feng; Shen Zhang; Haohan Wang; Yuxin Qin; Yaoyu Li; Ao Ma; Yuhao Luo; Lu Wang; Xudong Ren; Haoran Wang; Run Ling; Zheng Zhang; Jingjing Lv; Junjie Shen; Ching Law; Longguang Wang; Yulan Guo
Venue: CVPR 2026 (main)

Abstract: Generating realistic and user-preferred advertisements is a key challenge in e-commerce. Existing approaches utilize multiple independent models driven by click-through-rate (CTR) to controllably create attractive image or text advertisements. However, their pipelines lack cross-modal perception and rely on CTR that only reflects average preferences. Therefore, we explore jointly generating personalized image-text advertisements from historical click behaviors. We first design a Unified Advertisement Generative model (Uni-AdGen) that employs a single autoregressive framework to produce both advertising images and texts. By incorporating a foreground perception module and instruction tuning, Uni-AdGen enhances the realism of the generated content. To further personalize advertisements, we equip Uni-AdGen with a coarse-to-fine preference understanding module that effectively captures user interests from noisy multimodal historical behaviors to drive personalized generation. Additionally, we construct the first large-scale Personalized Advertising image-text dataset (PAd1M) and introduce a Product Background Similarity (PBS) metric to facilitate training and evaluation. Extensive experiments show that our method outperforms baselines in general and personalized advertisement generation. The dataset and codes will be released after acceptance.
Taxonomy-Aware Representation Alignment for Hierarchical Visual Recognition with Large Multimodal Models
Authors: Hulingxiao He; Zhi Tan; Yuxin Peng
Venue: CVPR 2026 (main)

Abstract: A high-performing, general-purpose visual understanding model should map visual inputs to a taxonomic tree of labels, identify novel categories beyond the training set for which few or no publicly available images exist. Large Multimodal Models (LMMs) have achieved remarkable progress in fine-grained visual recognition (FGVR) for known categories. However, they remain limited in hierarchical visual recognition (HVR) that aims at predicting consistent label paths from coarse to fine categories, especially for novel categories. To tackle these challenges, we propose Taxonomy-Aware Representation Alignment (TARA), a simple yet effective strategy to inject taxonomic knowledge into LMMs. TARA leverages representations from biology foundation models (BFMs) that encode rich biological relationships through hierarchical contrastive learning. By aligning the intermediate representations of visual features with those of BFMs, LMMs are encouraged to extract discriminative visual cues well structured in the taxonomy tree. Additionally, we align the representations of the first answer token with the ground-truth label, flexibly bridging the gap between contextualized visual features and categories of varying granularity according to user intent. Experiments demonstrate that TARA consistently enhances LMMs' hierarchical consistency and leaf node accuracy, enabling reliable recognition of both known and novel categories within complex biological taxonomies. Code is available at https://github.com/PKU-ICST-MIPL/TARA_CVPR2026.
WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
Authors: Ao Liang; Lingdong Kong; Tianyi Yan; Hongsi Liu; Yu Yang; Ziqi Huang; Wei Yin; Jialong Zuo; Yixuan Hu; Dekai Zhu; Dongyue Lu; Youquan Liu; Guangfeng Jiang; Linfeng Li; Xiangtai Li; Long Zhuo; Lai Xing Ng; Benoit R. Cottereau; Changxin Gao; Liang Pan; Wei Tsang Ooi; Ziwei Liu
Venue: CVPR 2026 (main)

Abstract: Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally. Despite rapid progress, the field still lacks a unified way to assess whether generated worlds preserve geometry, obey physics, or support reliable control. We introduce **WorldLens**, a full-spectrum benchmark evaluating how well a model builds, understands, and behaves within its generated world. It spans five aspects - Generation, Reconstruction, Action-Following, Downstream Task, and Human Preference - jointly covering visual realism, geometric consistency, physical plausibility, and functional reliability. Across these dimensions, no existing world model excels universally: those with strong textures often violate physics, while geometry-stable ones lack behavioral fidelity. To align objective metrics with human judgment, we further construct **WorldLens-26K**, a large-scale dataset of human-annotated videos with numerical scores and textual rationales, and develop **WorldLens-Agent**, an evaluation model distilled from these annotations to enable scalable, explainable scoring. Together, the benchmark, dataset, and agent form a unified ecosystem for measuring world fidelity - standardizing how future models are judged not only by how real they look, but by how real they behave.
DiffGraph: An Automated Agent-driven Model Merging Framework for In-the-Wild Text-to-Image Generation
Authors: Zhuoling Li; Hossein Rahmani; Jiarui Zhang; Yu Xue; Majid Mirmehdi; Jason Kuen; Jiuxiang Gu; Jun Liu
Venue: CVPR 2026 (main)

Abstract: The rapid growth of the text-to-image (T2I) community has fostered a thriving online ecosystem of expert models, which are variants of pretrained diffusion models specialized for diverse generative capabilities. Yet, existing model merging methods remain limited in fully leveraging abundant online expert resources and still struggle to meet diverse in-the-wild user needs. We present DiffGraph, a novel automated agent-driven graph-based model merging framework, which automatically harnesses online experts and flexibly merges them for diverse user needs. Our DiffGraph constructs a scalable graph and organizes ever-expanding online experts within it through node registration and calibration. Then, DiffGraph dynamically activates specific subgraphs based on user needs, enabling flexible combinations of different experts to achieve user-desired generation. Extensive experiments show the efficacy of our method.
ReFTA: Breaking the Weight Reconstruction Bottleneck in Tensorized Parameter-Efficient Fine-Tuning
Authors: Jingjing Zheng; Anda Tang; Qiangqiang Mao; Zhouchen Lin; Yankai Cao
Venue: CVPR 2026 (main)

Abstract: Tensor-based methods have attracted growing interest due to their ability to reduce trainable parameters and offer advantages over matrix-based approaches  in parameter-efficient fine-tuning (e.g., LoRA and PiSSA), particularly in capturing inter-layer correlations. However, directly applying  tensor methods requires repeated reconstruction of model weights during training, introducing substantial overhead and becoming a key bottleneck for  deployment. To address this limitation, we propose Reconstruction-Free Tensor-Based Adaptation (ReFTA), which offers four advantages: (1) eliminating weight tensor reconstruction via  tensor algebraic properties; (2) reducing initialization quantization error  by fine-tuning only the principal tensor components; (3) providing a rigorous generalization guarantee rooted in tensor product algebra; and  (4) enabling a unified design controlled by a single tensor rank configuration. Extensive experiments on both image classification (IC) and natural language understanding (NLU) tasks demonstrate that ReFTA achieves the best accuracy-efficiency trade-off among all evaluated methods. Across most cases, ReFTA attains the highest average accuracy with the fewest trainable parameters. On IC tasks with the ViT-Large model, ReFTA surpasses  LoRA by 5.6%   in average accuracy, while reducing the number of trainable parameters by up to 96% compared to LoRA. On NLU tasks with RoBERTa-Large, ReFTA improves the average accuracy by approximately 5% over most existing methods while using only 86.4% fewer parameters than LoRA (r=1) and  97.5% fewer than PiSSA. The code  is available at https://github.com/jzheng20/ReFTA.
From Exploration to Exploitation: A Two-Stage Entropy RLVR Approach for Noise-Tolerant MLLM Training
Authors: Donglai Xu; Hongzheng Yang; Yuzhi Zhao; Pingping Zhang; Jinpeng Chen; Wenao Ma; Zhijian Hou; Mengyang Wu; Xiaolei Li; Senkang Hu; Ziyi Guan; Jason Chun Lok Li; Lai-Man Po
Venue: CVPR 2026 (main)

Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) for Multimodal Large Language Models (MLLMs) is highly dependent on high-quality labeled data, which is often scarce and prone to substantial annotation noise in real-world scenarios. Existing unsupervised RLVR methods, including pure entropy minimization, can overfit to incorrect labels and limit the crucial reward ranking signal for Group-Relative Policy Optimization (GRPO). To address these challenges and enhance noise tolerance, we propose a novel two-stage, token-level entropy optimization method for RLVR. This approach dynamically guides the model from exploration to exploitation during training. In the initial exploration phase, token-level entropy maximization promotes diverse and stochastic output generation, serving as a strong regularizer that prevents premature convergence to noisy labels and ensures sufficient intra-group variation--enabling more reliable reward gradient estimation in GRPO. As training progresses, the method transitions into the exploitation phase, where token-level entropy minimization encourages the model to produce confident and deterministic outputs, thereby consolidating acquired knowledge and refining prediction accuracy. Empirically, across three MLLM backbones-Qwen2-VL-2B, Qwen2-VL-7B, and Qwen2.5-VL-3B--spanning diverse noise settings and multiple tasks, our phased strategy consistently outperforms prior approaches by unifying and enhancing external, internal, and entropy-based methods, delivering robust and superior performance across the board.
Tracking-Guided 4D Generation: Foundation-Tracker Motion Priors for 3D Model Animation
Authors: Su Sun; Cheng Zhao; Himangi Mittal; Gaurav Mittal; Rohith Kukkala; Yingjie Victor Chen; Mei Chen
Venue: CVPR 2026 (main)

Abstract: Generating dynamic 4D objects from sparse inputs is difficult because it demands joint preservation of appearance and motion coherence across views and time while suppressing artifacts and temporal drift. We hypothesize that the view discrepancy arises from supervision limited to pixel- or latent-space video-diffusion losses, which lack explicitly temporally aware, feature-level tracking guidance.We present Track4DGen, a two-stage framework that couples a multi-view video diffusion model with a foundation point tracker and a hybrid 4D Gaussian Splatting (4D-GS) reconstructor. The central idea is to explicitly inject tracker-derived motion priors into intermediate feature representations for both multi-view video generation and 4D-GS. In Stage One, we enforce dense, feature-level point correspondences inside the diffusion generator, producing temporally consistent features that curb appearance drift and enhance cross-view coherence. In Stage Two, we reconstruct a dynamic 4D-GS using a hybrid motion encoding that concatenates co-located diffusion features (carrying Stage-One tracking priors) with Hex-plane features, and augment them with 4D Spherical Harmonics for higher-fidelity dynamics modeling.Track4DGen surpasses baselines on both multi-view video generation and 4D generation benchmarks, yielding temporally stable, text-editable 4D assets. Lastly, we curate Sketchfab28, a high-quality dataset for benchmarking object-centric 4D generation and fostering future research.
Revisiting Monocular SLAM with Spatio-Temporal Scene Modeling
Authors: Valter Piedade; Lalit Manam; Masashi Yamazaki; Pedro Miraldo
Venue: CVPR 2026 (main)

Abstract: Visual SLAM is one of the most fundamental problems in computer vision, with direct applications to real-time localization tasks such as AR/VR, robotics, and 3D scene reconstruction. Although significant progress has been made in both sparse and dense approaches, real-time monocular SLAM remains challenging--particularly in the uncalibrated setting, where existing methods are often inefficient and lack modularity. In this paper, we present a new visual SLAM pipeline, called SLAM-MER, which is implemented from scratch in C++ explicitly leveraging the spatio-temporal structure of the SLAM problem for improved localization, and has modular design to easily integrate off-the-shelf components. We introduce a temporal representation based on a buffer of recent keyframes that preserves short-term scene continuity. We complement this by incorporating a spatial representation based on a 3D cell-based scene model, enabling efficient retrieval of relevant 3D points from previously reconstructed regions. Leveraging recent feed-forward geometry estimators, our hybrid design combines sparse keypoint-based localization with a semi-dense anchor-point-driven spatial representation. This integration allows us to achieve real-time performance (exceeding 80 FPS) and a substantial efficiency improvement compared to existing uncalibrated monocular SLAM pipelines, while maintaining or improving localization accuracy.
RobustVisRAG: Causality-Aware Vision-Based Retrieval-Augmented Generation under Visual Degradations
Authors: I-Hsiang Chen; Yu-Wei Liu; Tse-Yu Wu; Yu-Chien Chiang; Jen-Chieh Yang; Wei-Ting Chen
Venue: CVPR 2026 (main)

Abstract: Vision-based Retrieval-Augmented Generation (VisRAG) leverages vision-language models (VLMs) to jointly retrieve relevant visual documents and generate grounded answers based on multimodal evidence. However, existing VisRAG models degrade in performance when visual inputs suffer from distortions such as blur, noise, low light, or shadow, where semantic and degradation factors become entangled within pretrained visual encoders, leading to errors in both retrieval and generation stages. To address this limitation, we introduce RobustVisRAG, a causality-guided dual-path framework that improves VisRAG robustness while preserving efficiency and zero-shot generalization. RobustVisRAG uses a non-causal path to capture degradation signals through unidirectional attention and a causal path to learn purified semantics guided by these signals. Together with the proposed Non-Causal Distortion Modeling and Causal Semantic Alignment objectives, the framework enforces a clear separation between semantics and degradations, enabling stable retrieval and generation under challenging visual conditions. To evaluate robustness under realistic conditions, we introduce the Distortion-VisRAG dataset, a large-scale benchmark containing both synthetic and real-world degraded documents across seven domains, with 12 synthetic and 5 real distortion types that comprehensively reflect practical visual degradations. Experimental results show that RobustVisRAG improves retrieval, generation, and end-to-end performance by 7.35%, 6.35%, and 12.40%, respectively, on real-world degradations, while maintaining comparable accuracy on clean inputs.
Dual-Level Hypergraph Generation for Addressing Feature Scarcity in Whole-Slide Image Classification
Authors: Shuilian Yao; Qi Jia; Yu Liu; Pengshuo Zhang; Lili Sun; Weimin Wang; Yanmei Zhu; Bo Zhang; Xin Fan
Venue: CVPR 2026 (main)

Abstract: Lymph node metastasis diagnosis in pathological images is a highly challenging four-class classification task, comprising macrometastasis, micrometastasis, isolated tumor cells (ITC), and negative lesions.Unlike conventional classification settings, this four-class scenario simultaneously suffers from inter-class and intra-slide scarcity of minority information.Existing approaches based on CNNs or GNNs primarily emphasize node-level feature learning, making it difficult to capture high-order feature interactions and topological dependencies among cells, while also overlooking the representational insufficiency induced by class scarcity.To address these challenges, we propose a dual-level generative framework that integrates class-prompt priors with high-order structural modeling to enhance the representation capacity of minority classes.At the hypergraph level, we develop a prompt-guided hierarchical hypergraph variational autoencoder (HGVAE) capable of generating diverse and topologically consistent hypergraph representations for minority classes.At the hypernode level, we introduce an anchor-diffusion mixup strategy to enrich the minority node features of high-attention positive anchor nodes.Extensive experiments on the four-class NIMM dataset, as well as TCGA datasets, demonstrate that the proposed framework effectively alleviates feature scarcity and significantly boosts the classification performance of minority classes.
Edit2Perceive: Image Editing Diffusion Models Are Strong Dense Perceivers
Authors: Yiqing Shi; Yiren Song; Mike Zheng Shou
Venue: CVPR 2026 (main)

Abstract: Recent advances in diffusion transformers have shown remarkable generalization in visual synthesis, yet most dense perception methods still rely on text-to-image (T2I) generators designed for stochastic generation. We revisit this paradigm and show that image editing diffusion models are inherently image-to-image consistent, providing a more suitable foundation for dense perception task. We introduce Edit2Perceive, a unified diffusion framework that adapts editing models for depth, normal, and matting. Built upon the FLUX.1 Kontext architecture, our approach employs full-parameter fine-tuning and a pixel-space consistency loss to enforce structure-preserving refinement across intermediate denoising states. Moreover, our single-step deterministic inference yields up to faster runtime while training on relatively small datasets.Extensive experiments demonstrate comprehensive state-of-the-art results across all three tasks, revealing the strong potential of editing-oriented diffusion transformers for geometry-aware perception.
Will Multimodal Models Be Dazzled by Multi-Image Visual Puzzles?
Authors: Zhi Zhu; YaoQi Fan; Zhe Chen; Yue Cao; Yangzhou Liu; Tong Lu
Venue: CVPR 2026 (main)

Abstract: The rapid advancement of Multimodal Large Language Models (MLLMs) has revealed the limitations of existing benchmarks in evaluating complex reasoning over multiple images. To address this gap, we introduce MIRACLE, a novel benchmark for Multi-Image complex Reasoning And Comprehension Logic Evaluation, featuring 4,000 questions across diverse reasoning types such as visual comparison, temporal sequencing, and spatial relations. MIRACLE emphasizes strong inter-image dependencies through a systematic data collection process, followed by delicate instance grouping and question design that enforce cross-image reasoning.Evaluation on leading MLLMs shows that even top-performing models like Gemini-2.5-Pro achieve only 55.91% points, highlighting the significant challenges of multi-image reasoning. Moreover, in scenarios characterized by high visual information density, such as puzzle tasks and ultra multi-image input conditions, all models exhibit a significant drop in performance, which highlights the limitations of MLLMs in handling complex structural relations and collaborative reasoning, revealing deficiencies in their cognitive capabilities under high-load visual reasoning settings. We hope MIRACLE will inspire the community to push the boundaries of multi-image reasoning. MIRACLE is available at https://huggingface.co/datasets/queyuecanyang/MIRACLE.
Unpaired Image Deraining Using Reward-Guided Self-Reinforcement Strategy
Authors: Yinghao Chen; Yeying Jin; Xiang Chen; Yanyan Wei; Ziyang Yan; Yaowen Fu
Venue: CVPR 2026 (main)

Abstract: Unsupervised deraining has attracted attention for its ability to learn the real-world distribution of rain without paired supervision. However, the lack of strong constraints makes it difficult for the network to converge, especially with the complex diversity of rain degradation. A key motivation is that high-quality deraining results occasionally emerge during training, which can be leveraged to guide the optimization process. To overcome these challenges, we introduce RGSUD (Reward-Guided Self-Reinforcement Unsupervised Image Deraining), comprising two key stages: reward recycling and self-reinforcement (SR) training. In the reward recycling stage, we propose an Image Quality Assessment (IQA)-based dynamic reward recycling mechanism that selects optimal derained outputs during training and continuously collects high-quality deraining images. In the SR training stage, we incorporate these rewards into the model's optimization process, constraining the optimization space and improving alignment between derained outputs and clean images. By leveraging IQA-based self reinforced loss and dynamically updated rewards, we enhance the quality of synthesized pseudo-paired data and stabilize the optimization. Extensive experiments demonstrate that our method achieves SOTA performance across multiple datasets, including paired synthetic, paired real, and unpaired real images, outperforming existing unsupervised deraining approaches in both subjective and objective IQA metrics. Additionally, we show that the Self Reinforcement Strategy is adaptable to other unsupervised deraining methods.