GenAI4ELab/papercli-papers huggingface dataset:
STAR: Test-Time Adaptation Can Enhance Universal Prompt Learning for Vision-Language Models
Authors: Yiwei Fu; Hui Wan; Xiao Luo; Minghua Deng
Venue: CVPR 2026 (main)

Abstract: This paper studies the problem of universal test-time prompt learning for vision-language models (VLMs) which aims to enhance prompt learning for a pre-trained VLM via unlabeled target data containing out-of-distribution (OOD) samples. However, existing test-time adaptation approaches often overlook class-specific diversity in the target domain and rely on unreliable pseudo-labels due to inadequate uncertainty estimation, which may result in additional adaptation bias during test time. Towards this end, we propose a novel framework named Separability-aware Conjugate Optimization with Prototypical Retrieval (STAR) for universal test-time prompt learning of VLMs. The core of our STAR is to incorporate a separability-aware gating mechanism into conjugate optimization for reliable pseudo-learning with OOD samples. In particular, we first compute the Fisher score to quantify the separability between in-distribution (ID) and OOD samples, which guides our soft gating mechanism for divided training. Then, we employ conjugate optimization to derive reliable pseudo-labels of unlabeled data for test-time adaptation. To further mitigate biases in OOD detection, we maintain a dynamic memory bank which stores high-confidence samples to build class-wise prototypes, which would serve as queries for prototypical retrieval to calibrate OOD detection. Extensive experiments on multiple benchmarks demonstrate that STAR consistently outperforms competing baseline methods.
Anti-Degradation Lifelong Multi-View Clustering
Authors: Xingfeng Li; Hao Pan; Honglin Yuan; Yuan Sun; Xujian Zhao; Jiaqi Lin; Zhenwen Ren
Venue: CVPR 2026 (main)

Abstract: In real-world scenarios, new views are continuously collected over time, forming a dynamic view stream. To handle such evolving data, a lifelong multi-view clustering framework is needed instead of a static model. However, large discrepancies across views make it challenging to learn new knowledge while preserving previously acquired information. There are few methods use consistency alignment or knowledge distillation to align new knowledge with old ones. However, these strategies cannot fundamentally prevent knowledge degradation, since new knowledge inevitably interferes with the learned representation space. To overcome this limitation, we propose a new Anti-degradation Lifelong Multi-view Clustering (ALMC) framework. Specifically, we innovatively propose a null-space-projection knowledge base anti-degradation technique, which ensures that new knowledge updates to the model only occur in directions orthogonal to the retained knowledge, thus preventing catastrophic forgetting of knowledge and degradation of clustering performance, and provides theoretical proof for this. Extensive experiments on multiple multi-view benchmark datasets demonstrate superior performance in multi-view clustering.
FedSDR: Federated Graph Learning with Structural Noise Detection and Reconstruction
Authors: Jiaqi Liu; Zihan Tan; Guancheng Wan; Wenke Huang; He Li; Mang Ye
Venue: CVPR 2026 (main)

Abstract: Federated Graph Learning (FGL) has emerged as a principled framework for decentralized training of Graph Neural Networks (GNNs) while preserving data privacy. In subgraph-FL scenarios, however, structural noise arising from data collection and storage can damage the GNN message-passing scheme of clients, leading to conflicts in collaboration. Existing approaches exhibit two critical limitations: 1) Globally, they fail to identify corrupted clients, causing destructive knowledge inconsistencies. 2) Locally, the global GNN performs poorly on these clients due to structural noise, limiting their ability to benefit from federated collaboration. To address these challenges, we propose FedSDR, a spectra-based FGL framework against high-structural-noise scenarios. Specifically, Structural Noise-Aware Aggregation (SNAA) introduces a structural fidelity evaluation metric to detect corrupted clients and reduce their contributions, thereby mitigating the impact of noise on the global GNN. Furthermore, Robust Local Structure Reconstruction (RLSR) leverages the knowledge from the healthy global model to repair locally corrupted graph structures. Extensive experiments demonstrate that FedSDR outperforms state-of-the-art methods across various scenarios under structural noise. The code is available at https://github.com/Subtleazure/FedSDR.
SpeeDe3DGS: Speedy Deformable 3D Gaussian Splatting with Temporal Pruning and Motion Grouping
Authors: Allen Tu; Haiyang Ying; Alex Hanson; Yonghan Lee; Tom Goldstein; Matthias Zwicker
Venue: CVPR 2026 (main)

Abstract: Dynamic extensions of 3D Gaussian Splatting (3DGS) achieve high-quality reconstructions through neural motion fields, but per-Gaussian neural inference makes these models computationally expensive. Building on DeformableGS, we introduce Speedy Deformable 3D Gaussian Splatting (SpeeDe3DGS), which bridges this efficiency-fidelity gap through three complementary modules: Temporal Sensitivity Pruning (TSP) removes low-impact Gaussians via temporally aggregated sensitivity analysis, Temporal Sensitivity Sampling (TSS) perturbs timestamps to suppress floaters and improve temporal coherence, and GroupFlow distills the learned deformation field into shared SE(3) transformations for efficient groupwise motion. On the 50 dynamic scenes in MonoDyGauBench, integrating TSP and TSS into DeformableGS accelerates rendering by 6.78x on average while maintaining neural-field fidelity and using 10x fewer primitives. Adding GroupFlow culminates in 13.71x faster rendering and 2.53x shorter training, surpassing all baselines in speed while preserving superior image quality.
Semantic Context Matters: Improving Conditioning for Autoregressive Models
Authors: Dongyang Jin; Ryan Xu; Jianhao Zeng; Rui Lan; Yancheng Bai; Lei Sun; Xiangxiang Chu
Venue: CVPR 2026 (main)

Abstract: Recently, autoregressive (AR) models have shown strong potential in image generation, offering better scalability and easier integration with unified multi-modal models compared to diffusion methods.However, extending AR models to controllable image editing remains challenging due to weak and inefficient conditioning strategies, which often lead to suboptimal semantic alignment and visual quality.To address this limitation, we present SCAR, a Semantic-Context-driven method for AutoregRessive models.SCAR introduces Compressed Semantic Prefilling and Semantic Alignment Guidance that jointly enhance contextual understanding and generation coherence. Unlike prior methods that rely on sparse visual tokens or decoding stage injection, SCAR enables strong semantic guidance from the input stage, while remaining model-agnostic and applicable to both next-token and next-scale AR paradigms.Extensive experiments on instruction editing and controllable generation demonstrate that our method significantly improves visual fidelity and semantic alignment, outperforming existing AR-based methods while maintaining controllability. Code will be released at https://github.com/AMAP-ML/SCAR.
SRA-Det: Learning Omni-Grained Open-Vocabulary Detection Beyond Category Names
Authors: Li Yang; Boyu Cai; Wei Liu; Yan Wang; Chunfeng Yuan; Bing Li; Weiming Hu
Venue: CVPR 2026 (main)

Abstract: Open-vocabulary object detection (OVD) aims to detect objects described by arbitrary text, but most existing methods operate at a coarse category level and struggle with fine-grained, attribute-sensitive queries. We address this from both model and data perspectives. We propose a Semantic-Retrieval-Augmented Detector (SRA-Det) that uses an attention-based module to retrieve multiple semantic facets from token-level text features, and a soft-min matching rule that behaves like a differentiable logical AND over these facets, ensuring that all key attributes are satisfied. In parallel, we introduce an automatic attribute-augmented data pipeline that uses an LLM to generate category-specific visual attributes and a dual CLIP-based similarity check to verify them at the instance level. With a Swin-T backbone, our approach achieves 54.9 mAP in the zero-shot setting on FG-OVD and 40.4 AP on LVIS, establishing strong fine-grained and general OVD performance.
ReScene4D: Temporally Consistent Semantic Instance Segmentation of Evolving Indoor 3D Scenes
Authors: Emily Steiner; Jianhao Zheng; Henry Howard-Jenkins; Chris Xie; Iro Armeni
Venue: CVPR 2026 (main)

Abstract: Indoor environments evolve as objects move, appear, or leave the scene. Capturing these dynamics requires maintaining temporally consistent instance identities across intermittently captured 3D scans, even when changes are unobserved. We introduce and formalize the task of temporally sparse 4D indoor semantic instance segmentation (SIS), which jointly segments, identifies, and temporally associates object instances. This setting poses a challenge for existing 3DSIS methods, which require a discrete matching step due to their lack of temporal reasoning, and for 4D LiDAR approaches, which perform poorly due to their reliance on high-frequency temporal measurements that are uncommon in the longer-horizon evolution of indoor environments. We propose ReScene4D, a novel method that adapts 3DSIS architectures for 4DSIS without needing dense observations. Our method enables temporal information sharing--using spatiotemporal contrastive loss, masking, and serialization--to adaptively leverage geometric and semantic priors across observations. This shared context enables consistent instance tracking and improves standard 3DSIS performance. To evaluate this task, we define a new metric, t-mAP, that extends mAP to reward temporal identity consistency. ReScene4D achieves state-of-the-art performance on the 3RScan dataset, establishing a new benchmark for understanding evolving indoor scenes.
Thinking with Frames: Generative Video Distortion Evaluation via Frame Reward Model
Authors: Yuan Wang; Borui Liao; Huijuan Huang; Jinda Lu; Ouxiang Li; Kuien Liu; Meng Wang; Xiang Wang
Venue: CVPR 2026 (main)

Abstract: Recent advances in video reward models and post-training strategies have improved text-to-video (T2V) generation. While these models typically assess visual quality, motion quality, and text alignment, they often overlook key structural distortions, such as abnormal object appearances and interactions, which can degrade the overall quality of the generative video.To address this gap, we introduce REACT, a frame-level reward model designed specifically for structural distortions evaluation in generative videos. REACT assigns point-wise scores and attribution labels by reasoning over video frames, focusing on recognizing distortions. To support this, we construct a large-scale human preference dataset, annotated based on our proposed taxonomy of structural distortions, and generate additional data using a efficient Chain-of-Thought (CoT) synthesis pipeline. REACT is trained with a two-stage framework: (1) supervised fine-tuning with masked loss for domain knowledge injection, followed by (2) reinforcement learning with Group Relative Policy Optimization (GRPO) and pairwise rewards to enhance reasoning capability and align output scores with human preferences. During inference, a dynamic sampling mechanism is introduced to focus on frames most likely to exhibit distortion.We also present REACT-Bench, a benchmark for generative video distortion evaluation. Experimental results demonstrate that REACT complements existing reward models in assessing structural distortion, achieving both accurate quantitative evaluations and interpretable attribution analysis.
LiteVGGT: Boosting Vanilla VGGT via Geometry-aware Cached Token Merging
Authors: Zhijian Shu; Cheng Lin; Tao Xie; Wei Yin; Ben Li; Zhiyuan Pu; Weize Li; Yao Yao; Xun Cao; Xiaoyang Guo; Xiao-Xiao Long
Venue: CVPR 2026 (main)

Abstract: 3D vision foundation models like Visual Geometry Grounded Transformer (VGGT) have advanced greatly in geometric perception. However it is time-consuming and memory-intensive for long sequences, limiting application to large-scale scenes beyond hundreds of images. To address this, we propose LiteVGGT, achieving up to 10x speedup and substantial memory reduction, enabling efficient processing of 1000-image scenes. We derive two key insights for 3D reconstruction: 1) tokens from local image regions have inherent geometric correlations, leading to high similarity and computational redundancy; 2) token similarity across adjacent network layers remains stable, allowing for reusable merge decisions. Guided by these, we design a simple yet efficient strategy, dubbed geometry-aware cached token merging . We analyze each token's geometric importance, optimizing anchor token selection to better preserve key information for reconstruction. We also cache and reuse merge indices across layers, substantially reducing latency with minimal accuracy impact. This strategy retains VGGT's core performance, enabling efficient fine-tuning and FP8 quantization for further gains. Extensive experiments validate LiteVGGT's effectiveness, scalability, and robustness.
Lipschitz Optimization for Formal Verification of Homographies
Authors: Jean-Guillaume Durand; Panagiotis Kouvaros; Maxime Gariel; Alessio Lomuscio
Venue: CVPR 2026 (main)

Abstract: The adoption of vision neural networks in regulated industries requires formal robustness guarantees, especially in safety-critical domains such as healthcare, autonomous vehicles, and aerospace. However, current approaches are confined to incomplete statistical verification or robustness to p-norm and affine transforms, which cover only a narrow subset of perturbations to the image formation process. In particular, robustness to camera motion remains an open problem despite being key to deploy many vision applications. We present a formal verification approach that targets robustness against 3D motion perturbations of the capturing camera. We first establish a closed-form mapping from camera pose to pixel values. By analyzing the continuity properties of the resulting homographies, we show that recent work on Lipschitz optimization and piecewise continuity can be extended to derive tight linear bounds on perturbed pixel values. Our approach applies to scenes with predominantly planar structure, such as ground planes in augmented reality, road markings and traffic signs in autonomous driving, or planar workspaces in robotic manipulation. This enables the first formal verification of projective geometry transforms, without complex simulation, surrogate networks, or explicit image-formation models. Our implementation achieves up to 89% speedup and 7% tighter bounds over prior work. We evaluate our method on the VNN-COMP benchmark and reveal systematic weaknesses to projective perturbations. Finally, we demonstrate a real-world case study on a safety-critical runway classifier, addressing a key challenge in the certification of learned models. Data and code are available at https://github.com/jeangud/homography-verification.
Adapter Shield: A Unified Framework with Built-in Authentication for Preventing Unauthorized Zero-Shot Image-to-Image Generation
Authors: Jun Jia; Hongyi Miao; Yingjie Zhou; Wangqiu Zhou; Jianbo Zhang; Linhan Cao; Dandan Zhu; Hua Yang; Xiongkuo Min; Wei Sun; Guangtao Zhai
Venue: CVPR 2026 (main)

Abstract: With the rapid progress in diffusion models, image synthesis has advanced to the stage of zero-shot image-to-image generation, where high-fidelity replication of facial identities or artistic styles can be achieved using just one portrait or artwork, without modifying any model weights. Although these techniques significantly enhance creative possibilities, they also pose substantial risks related to intellectual property violations, including unauthorized identity cloning and stylistic imitation. To counter such threats, this work presents Adapter Shield, the first universal and authentication-integrated solution aimed at defending personal images from misuse in zero-shot generation scenarios. We first investigate how current zero-shot methods employ image encoders to extract embeddings from input images, which are subsequently fed into the UNet of diffusion models through cross-attention layers. Inspired by this mechanism, we construct a reversible encryption system that maps original embeddings into distinct encrypted representations according to different secret keys. The authorized users can restore the authentic embeddings via a decryption module and the correct key, enabling normal usage for authorized generation tasks. For protection purposes, we design a multi-target adversarial perturbation method that actively shifts the original embeddings toward designated encrypted patterns. Consequently, protected images are embedded with a defensive layer that ensures unauthorized users can only produce distorted or encrypted outputs. Extensive evaluations demonstrate that our method surpasses existing state-of-the-art defenses in blocking unauthorized zero-shot image synthesis, while supporting flexible and secure access control for verified users.
AsymLoc: Towards Asymmetric Feature Matching for Efficient Visual Localization
Authors: Mohammad Omama; Gabriele Berton; Eric Foxlin; Yelin Kim
Venue: CVPR 2026 (main)

Abstract: Precise and real-time visual localization is critical for applications like AR/VR and robotics, especially on resource-constrained edge devices such as smart glasses, where battery life and heat dissipation can be primary concerns. While many efficient models exist, further reducing compute without sacrificing accuracy is essential for practical deployment. To address this, we propose asymmetric visual localization: a large Teacher model processes pre-mapped database images offline, while a lightweight Student model processes the query image online. This creates a challenge in matching features from two different models without resorting to heavy, learned matchers.We introduce AsymLoc, a novel distillation framework that aligns a Student to its Teacher through a combination of a geometry-driven matching objective and a joint detector-descriptor distillation objective, enabling fast, parameter-less nearest-neighbor matching.Extensive experiments on HPatches, ScanNet, IMC2022, and Aachen show that AsymLoc achieves up to 95% of the teacher's localization accuracy using an order of magnitude smaller models, significantly outperforming existing baselines and establishing a new state-of-the-art efficiency-accuracy trade-off.
Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoning
Authors: Haozhen Gong; Xiaozhong Ji; Yuansen Liu; Wenbin Wu; Xiaoxiao Yan; Jingjing Liu; Kai Wu; Jiazhen Pan; Bailiang Jian; Jiangning Zhang; Xiaobin Hu; Hongwei Bran Li
Venue: CVPR 2026 (main)

Abstract: MLLMs are beginning to appear in clinical workflows, but their ability to perform complex medical reasoning remains unclear. We present Med-CMR, a fine-grained Medical Complex Multimodal Reasoning benchmark. Med-CMR distinguishes from existing counterparts by three core features: 1) Systematic capability decomposition, splitting medical multimodal reasoning into fine-grained visual understanding and multi-step reasoning to enable targeted evaluation; 2) Challenging task design, with visual understanding across three key dimensions (small-object detection, fine-detail discrimination, spatial understanding) and reasoning covering four clinically relevant scenarios (temporal prediction, causal reasoning, long-tail generalization, multi-source integration); 3) Broad, high-quality data coverage, comprising 20,653 Visual Question Answering (VQA) pairs spanning 11 organ systems and 12 imaging modalities, validated via a rigorous two-stage (human expert + model-assisted) review to ensure clinical authenticity. We evaluate 18 state-of-the-art MLLMs with Med-CMR, revealing GPT-5 as the top-performing commercial model: 57.81 accuracy on multiple-choice questions (MCQs) and a 48.70 open-ended score, outperforming Gemini 2.5 Pro (49.87 MCQ accuracy, 45.98 open-ended score) and leading open-source model Qwen3-VL-235B-A22B (49.34 MCQ accuracy, 42.62 open-ended score). However, specialized medical MLLMs do not reliably outperform strong general models, and long-tail generalization emerges as the dominant failure mode. Med-CMR thus provides a stress test for visual-reasoning integration and rare-case robustness in medical MLLMs, and a rigorous yardstick for future clinical systems.
PlannerRFT: Reinforcing Diffusion Planners through Closed-Loop and Sample-Efficient Fine-Tuning
Authors: Hongchen Li; Tianyu Li; Jiazhi Yang; Mingyang Shang; Gaoqiang Wu; Caojun Wang; Haochen Tian; Zengrong Lin; Zhihui Hao; XianPeng Lang; Jia Hu; Hongyang Li
Venue: CVPR 2026 (main)

Abstract: Diffusion-based planners have emerged as a promising approach for human-like trajectory generation in autonomous driving. Recent works incorporate reinforcement fine-tuning to enhance the robustness of diffusion planners through reward-oriented optimization in a generation-evaluation loop. However, they struggle to generate multi-modal, scenario-adaptive trajectories, hindering the exploitation efficiency of informative rewards during fine-tuning. To resolve this, we propose PlannerRFT, a sample-efficient reinforcement fine-tuning framework for diffusion-based planners. PlannerRFT adopts a dual-branch optimization that simultaneously refines the trajectory distribution and adaptively guides the denoising process toward more promising exploration, without altering the original inference pipeline. To support parallel learning at scale, we develop nuMax, an optimized simulator that achieves 10 times faster rollout compared to native nuPlan. Extensive experiments shows that PlannerRFT yields state-of-the-art performance with distinct behaviors emerging during the learning process.
Glove2Hand: Synthesizing Natural Hand-Object Interaction from Multi-Modal Sensing Gloves
Authors: Xinyu Zhang; Ziyi Kou; Chuan Qin; Mia Huang; Ergys Ristani; Ankit Kumar; Lele Chen; Kun He; Abdeslam Boularias; Li Guan
Venue: CVPR 2026 (main)

Abstract: Understanding hand-object interaction (HOI) is fundamental to computer vision, robotics, and AR/VR. However, conventional hand videos often lack essential physical information, such as contact forces and motion dynamics, and are prone to frequent occlusions. To address these challenges, we present Glove2Hand, a framework that translates multi-modal sensing glove data in HOI videos into photorealistic bare-hand representations, while faithfully preserving the underlying physical interaction dynamics. We introduce a novel 3D Gaussian hand model that ensures both temporal and multi-view rendering consistency. The rendered hand is seamlessly integrated into the scene using a diffusion-based hand restorer, which effectively handles complex hand-object interactions and non-rigid deformations. Leveraging Glove2Hand, we introduce HandSense, the first multi-modal HOI dataset featuring multi-view bare-hand videos with synchronized tactile and IMU signals. We demonstrate that HandSense significantly enhances downstream bare-hand applications, including video-based contact estimation and hand tracking under severe occlusion.