GenAI4ELab/papercli-papers huggingface dataset:
Solving Minimal Problems Without Matrix Inversion Using FFT-Based Interpolation
Authors: Haidong Wu; Snehal Bhayani; Janne Heikkila
Venue: CVPR 2026 (main)

Abstract: Estimating camera geometry typically involves solving minimal problems formulated as systems of multivariate polynomial equations, which often pose computational challenges when using existing Grobner-basis or resultant-based methods due to matrix inversion needed in the online solver. Here we propose a sampling-based, matrix inversion-free method that constructs the solvers using sparse hidden-variable resultants. The determinant polynomial in the hidden variable is efficiently reconstructed via inverse fast Fourier transform interpolation from sampled evaluations, avoiding symbolic expansion. Solving this polynomial yields the hidden variable, and the remaining unknowns are recovered by identifying rank-1 deficient submatrices and applying Cramer's rule. A greatest common divisor-based criterion ensures robust submatrix identification under noise. Experiments on diverse minimal problems demonstrate that the proposed solver achieves strong numerical stability and competitive runtime, particularly for small-scale problems, providing a practical alternative to traditional Grobner-basis and resultant-based solvers.
Teacher-Guided Routing for Sparse Vision Mixture-of-Experts
Authors: Masahiro Kada; Ryota Yoshihashi; Satoshi Ikehata; Rei Kawakami; Ikuro Sato
Venue: CVPR 2026 (main)

Abstract: Recent progress in deep learning has been driven by increasingly large-scale models, but the resulting computational cost has become a critical bottleneck. Sparse Mixture of Experts (MoE) offers an effective solution by activating only a small subset of experts for each input, achieving high scalability without sacrificing inference speed. Although effective, sparse MoE training exhibits characteristic optimization difficulties. Because the router receives informative gradients only through the experts selected in the forward pass, it suffers from gradient blocking and obtains little information from unselected routes. This limited, highly localized feedback makes it difficult for the router to learn appropriate expert-selection scores and often leads to unstable routing dynamics, such as fluctuating expert assignments during training. To address this issue, we propose TGR-MoE: Teacher-Guided Routing for Sparse Vision Mixture-of-Experts, a simple yet effective method that stabilizes router learning using supervision derived from a pretrained dense teacher model. TGR-MoE constructs a teacher router from the teacher's intermediate representations and uses its routing outputs as pseudo-supervision for the student router, suppressing frequent routing fluctuations during training and enabling knowledge-guided expert selection from the early stages of training. Extensive experiments on ImageNet-1K and CIFAR-100 demonstrate that TGR consistently improves both accuracy and routing consistency, while maintaining stable training even under highly sparse configurations.
DeltaQuant: 4-bit Video Diffusion Models with Spatiotemporal Delta Smoothing
Authors: Xingyang Li; Samuel Tesfai; Zhekai Zhang; Haocheng Xi; Shuo Yang; Lvmin Zhang; Yufei Sun; Kelly Peng; Maneesh Agrawala; Ion Stoica; Kurt Keutzer; Jun-Yan Zhu; Song Han; Yujun Lin; Muyang Li
Venue: CVPR 2026 (main)

Abstract: Video diffusion models have achieved remarkable generative performance, but their substantial computational and memory costs pose significant challenges for deployment, especially on consumer GPUs. As recent advances in attention optimization mitigate previous computational bottlenecks, linear layers now dominate both computational cost and inference memory. In this work, we focus on quantizing both weights and activations to 4 bits to accelerate these layers. Previous methods, such as SVDQuant, overlook the highly dynamic nature of activations across denoising timesteps, where outlier channels and magnitudes vary dramatically. However, video data inherently exhibits strong activation similarity among neighboring tokens in space and time, which we term spatiotemporal activation similarity, analogous to how video codecs exploit intra- and inter-frame redundancy. Leveraging this property, we introduce DeltaQuant, which partitions activations into local 3D spatiotemporal cubes and uses each cube's mean token as a \coretoken, quantizing only the small differences (delta tokens) to 4 bits while keeping core tokens in FP8. This decomposition substantially reduces quantization error with minimal overhead.For weight quantization, DeltaQuant incorporates SVDQuant's low-rank decomposition to further reduce quantization error.We also implement an efficient kernel that translates DeltaQuant's computational benefits into real-world speedups.Extensive experiments on Wan 2.2 I2V, Wan 2.2 T2V, and LTX-Video T2V demonstrate that DeltaQuant maintains high generation fidelity.On Wan 2.2, it compresses model size by 2.9x and reduces memory footprint by 2.3x. DeltaQuant is compatible with efficient attention mechanisms and few-step distillation. When integrated with these techniques, it achieves an additional 3.0x acceleration, for a total 111.8x end-to-end speedup. Code and models will be released upon publication.
AstraNav-Memory: Contexts Compression for Long Memory
Authors: Junjun Hu; Xinda Xue; Botao Ren; Minghua Luo; Jintao Chen; Haochen Bai; Liangliang You; Mu Xu
Venue: CVPR 2026 (main)

Abstract: Lifelong embodied navigation requires agents to accumulate, retain, and exploit spatial-semantic experience across tasks, enabling efficient exploration in novel environments and rapid goal reaching in familiar ones. While object-centric memory is interpretable, it depends on detection and reconstruction pipelines that limit robustness and scalability. We propose an image-centric memory framework that achieves long-term implicit memory via an efficient visual context compression module end-to-end coupled with a Qwen2.5-VL-based navigation policy. Built atop a ViT backbone with frozen DINOv3 features and lightweight PixelUnshuffle+Conv blocks, our visual tokenizer reduces native vision tokens by roughly 10-20x, representing each image with about 30 tokens and allowing the agent to maintain hundreds of historical frames within a single context. Experimental results on GOAT-Bench and HM3D-OVON show that our method achieves state-of-the-art navigation performance, improving exploration in unfamiliar environments and shortening paths in familiar ones. Ablation studies further reveal that moderate compression provides the best balance between efficiency and accuracy. These findings position compressed image-centric memory as a practical and scalable interface for lifelong embodied agents, enabling them to reason over long visual histories and navigate with human-inspired efficiency.
Improving Motion in Image-to-Video Models via Adaptive Low-Pass Guidance
Authors: June Suk Choi; Kyungmin Lee; Sihyun Yu; Yisol Choi; Jinwoo Shin; Kimin Lee
Venue: CVPR 2026 (main)

Abstract: Recent text-to-video (T2V) models have demonstrated strong capabilities in producing high-quality, dynamic videos. To improve the visual controllability, recent works have considered fine-tuning pre-trained T2V models to support image-to-video (I2V) generation. However, such adaptation frequently suppresses motion dynamics of generated outputs, resulting in more static videos compared to their T2V counterparts. In this work, we analyze this phenomenon and identify that it stems from the premature exposure to high-frequency details in the input image, which biases the sampling process toward a shortcut trajectory that overfits to the static appearance of the reference image. To address this, we propose adaptive low-pass guidance (ALG), a simple training-free fix to the I2V model sampling procedure to generate more dynamic videos without compromising per-frame image quality. Specifically, ALG adaptively modulates the frequency content of the conditioning image by applying a low-pass filter at the early stage of denoising. Extensive experiments show ALG significantly improves the temporal dynamics of generated videos, while preserving or even improving image fidelity and text alignment. For instance, on the VBench test suite, ALG achieves a 33% average improvement across models in dynamic degree while maintaining the original video quality. For additional visualizations and source code, see the project page.
Parameter-Efficient Semantic Augmentation for Enhancing Open-Vocabulary Object Detection
Authors: Weihao Cao; Runqi Wang; Xiaoyue Duan; Jinchao Zhang; Ang Yang; Liping Jing
Venue: CVPR 2026 (main)

Abstract: Open-vocabulary object detection (OVOD) enables models to detect any object category, including unseen ones. Benefiting from large-scale pre-training, existing OVOD methods achieve strong detection performance on general scenarios (e.g., OV-COCO) but suffer severe performance drops when transferred to downstream tasks with substantial domain shifts. This degradation stems from the scarcity and weak semantics of category labels in domain-specific task, as well as the inability of existing models to capture auxiliary semantics beyond coarse-grained category label. To address these issues, we propose HSA-DINO, a parameter-efficient semantic augmentation framework for enhancing open-vocabulary object detection.  Specifically, we propose a multi-scale prompt bank that leverages image feature pyramids to capture hierarchical semantics and select domain-specific local semantic prompts, progressively enriching textual representations from coarse to fine-grained levels. Furthermore, we introduce a semantic-aware router that dynamically selects the appropriate semantic augmentation strategy during inference, thereby preventing parameter updates from degrading the generalization ability of the pre-trained OVOD model. We evaluate HSA-DINO on OV-COCO, several vertical domain datasets, and modified benchmark settings. The results show that HSA-DINO performs favorably against previous state-of-the-art methods, achieving a superior trade-off between domain adaptability and open-vocabulary generalization.
Globally Optimal Pose from Orthographic Silhouettes
Authors: Agniva Sengupta; Dilara Kus; Jianning Li; Stefan Zachow
Venue: CVPR 2026 (main)

Abstract: We solve the problem of determining the pose of known shapes in \mathbb R ^3 from their unoccluded silhouettes. The pose is determined up to global optimality using a simple yet under-explored property of the area-of-silhouette: its continuity w.r.t trajectories in the rotation space. The proposed method utilises pre-computed silhouette-signatures, modelled as a response surface of the area-of-silhouettes. Querying this silhouette-signature response surface for pose estimation leads to a strong branching of the rotation search space, making resolution-guided candidate search feasible. Additionally, we utilise the aspect ratio of 2D ellipses fitted to projected silhouettes as an auxiliary global shape signature to accelerate the pose search. This combined strategy forms the first method to efficiently estimate globally optimal pose from just the silhouettes, without being guided by correspondences, for any shape, irrespective of its convexity and genus. We validate our method on synthetic and real examples, demonstrating significantly improved accuracy against comparable approaches.
Otil: Accelerating Diffusion Model Inference via Communication-Efficient Multi-GPU Parallelism
Authors: Xin Li; Shujun Tian; Tao Lu; Han Bao; Zonghui Wang; Wenzhi Chen
Venue: CVPR 2026 (main)

Abstract: Diffusion models (DMs) have recently achieved remarkable success across diverse modalities, including high-fidelity image and video synthesis.However, their inherent step sequential denoising process introduces substantial cumulative latency, which significantly degrades user experience. While existing multi-GPU parallelization motheds can alleviate latency, they often incur prohibitive GPU-GPU communication overhead, offsetting much of the performance gain.We present Otil (Only Transmit Informative Latents), a communication-efficient parallel framework for accelerating diffusion inference.% Otil can minimizes redundant data exchange across GPUs while preserving generation quality.Our key insight is that latent activations change only marginally between consecutive denoising process. Leveraging this property, Otil identifies and synchronizes only the most informative latent sub-blocks and introduces a dynamic polling mechanism that periodically revisits all spatial regions, ensuring complete coverage without unnecessary communication. The framework is fully plug-and-play and remains compatible with fast sample and architectural acceleration algorithms, without requiring any retraining or architectural modification.Otil reduces GPU-GPU communication up to 87.5% compared with SOTA parallelism methods, achieving 1.8xspeedup on two GPUs with Stable Diffusion v1.5 and 2.6xon four GPUs with Stable Diffusion XL. When combined with few-step samplers (30 steps) and LoRA models, the acceleration further increases to 2.46x-2.84xon 2 GPUs. These demonstrate the strong potential of Otil for scalable and efficient multi-GPU diffusion inference while preserving generation fidelity.
Computation and Communication Efficient Federated Unlearning via On-server Gradient Conflict Mitigation and Expression
Authors: Minh-Duong Nguyen; Senura Wanasekara; Le-Tuan Nguyen; Quoc-Viet Pham; Ken-Tye Yong; Nguyen H. Tran; Dung D. Le
Venue: CVPR 2026 (main)

Abstract: Federated Unlearning (FUL) aims to remove specific participants' data contributions from a trained Federated Learning model, thereby ensuring data privacy and compliance with regulatory requirements. Despite its potential, progress in FUL has been limited due to several challenges, including the cross-client knowledge inaccessibility and high computational and communication costs. To overcome these challenges, we propose Federated On-server Unlearning (FOUL), a novel framework that comprises two key stages. The learning-to-unlearn stage serves as a preparatory learning phase, during which the model identifies and encodes the key features associated with the forget clients. This stage is communication-efficient and establishes the basis for the subsequent unlearning process. Subsequently, on-server knowledge aggregation phase aims to perform the unlearning process at the server without requiring access to client data, thereby preserving both efficiency and privacy. We introduce a new data setting for FUL, which enables a more transparent and rigorous evaluation of unlearning. To highlight the effectiveness of our approach, we propose a novel evaluation metric termed time-to-forget, which measures how quickly the model achieves optimal unlearning performance. Extensive experiments conducted on three datasets under various unlearning scenarios demonstrate that FOUL outperforms the Retraining in FUL. Moreover, FOUL achieves competitive or superior results with significantly reduced time-to-forget, while maintaining low communication and computation costs.
RegionFuse: Region-Adaptive Pixel Distribution Learning for Infrared and Visible Image Fusion
Authors: Jianghan Xia; Hong Song; Jinfu Li; Yucong Lin; Shihan Ma; Jingfan Fan; Danni Ai; Tianyu Fu; Deqiang Xiao; Jian Yang
Venue: CVPR 2026 (main)

Abstract: Infrared and Visible Image Fusion (IVIF) aims to combine complementary information from infrared and visible images to overcome the limitations of a single modality. While existing methods typically employ fixed or sample-adaptive fusion paradigms where fusion weights are static or derived from global pixel distributions, they often overlook spatial inconsistencies in pixel distribution within images, leading to suboptimal performance. To address this issue, we propose RegionFuse, a Region-Adaptive Pixel Distribution Learning Network for IVIF, which dynamically generates fusion weights based on local pixel distributions to construct a region-wise adaptive fusion paradigm. RegionFuse introduces a Mixture of Region Attention (MoRA) mechanism, which assigns each region to several specialized experts, enabling region-level feature interaction tailored to specific local distributions. Furthermore, we design a Region Feature Compression Module (RFCM) and place it after each MoRA to enhance informative regions and suppress redundant ones.  Extensive experiments on various benchmarks demonstrate the superiority and robustness of RegionFuse, especially in handling non-uniform pixel distributions. Evaluations on NIR-VIS and downstream tasks further confirm its generalizability and practical utility. The code is available at https://github.com/DarkIceField/RegionFuse.
AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors
Authors: Matic Fučka; Vitjan Zavrtanik; Danijel Skočaj
Venue: CVPR 2026 (main)

Abstract: Zero-shot anomaly detection aims to detect and localise abnormal regions in the image without access to any in-domain training images. While recent approaches leverage vision-language models (VLMs), such as CLIP, to transfer high-level concept knowledge, methods based on purely vision foundation models (VFMs), like DINOv2, have lagged behind in performance. We argue that this gap stems from two practical issues: (i) limited diversity in existing auxiliary anomaly detection datasets and (ii) overly shallow VFM adaptation strategies. To address both challenges, we propose AnomalyVFM, a general and effective framework that turns any pretrained VFM into a strong zero-shot anomaly detector. Our approach combines a robust three-stage synthetic dataset generation scheme with a parameter-efficient adaptation mechanism, utilising low-rank feature adapters and a confidence-weighted pixel loss. Together, these components enable modern VFMs to substantially outperform current state-of-the-art methods. More specifically, with RADIO as a backbone, AnomalyVFM achieves an average image-level AUROC of 94.1% across 9 diverse datasets, surpassing previous methods by significant 3.3 percentage points. Code: \href https://maticfuc.github.io/anomaly_vfm/  Project Page
VDE: Training-Free Accelerating Rectified Flow Model via Velocity Decomposition and Estimation
Authors: Junwen Tan; Jinglin Liang; Hongyuan Chen; Shuangping Huang
Venue: CVPR 2026 (main)

Abstract: Though rectified flow models have achieved remarkable performance in image, video, and 3D generation, their practical deployments are challenged by slow inference speeds. Prior acceleration methods reuse cached features from previous steps, which neglects the growing mismatch between static caches and the evolving input, leading to reduced  output fidelity. This work proposes Velocity Decomposition and Estimation (VDE), a training-free acceleration method that shifts the paradigm from caching-and-reusing to decomposing-and-estimating. Specifically, VDE decomposes the model's velocity into components parallel and orthogonal to the input, exploiting their temporal predictability and directional consistency for precise, input-adaptive estimation. To prevent error accumulation, it periodically anchors the model's state via full forward passes. Extensive experiments on image and video generation tasks demonstrate that VDE achieves substantial acceleration with minimal loss in visual quality. Notably, VDE accelerates Flux by 3.22x and achieves an LPIPS of 0.069 on Qwen-Image, outperforming the best baseline with a 52.2% reduction. Code: https://github.com/Tan-Junwen/VDE
TreeTeaming: Autonomous Red-Teaming of Vision-Language Models via Hierarchical Strategy Exploration
Authors: Chunxiao Li; Lijun Li; Jing Shao
Venue: CVPR 2026 (main)

Abstract: The rapid advancement of Vision-Language Models (VLMs) has brought their safety vulnerabilities into sharp focus. However, existing red teaming methods are fundamentally constrained by an inherent linear exploration paradigm, confining them to optimizing within a predefined strategy set and preventing the discovery of novel, diverse exploits. To transcend this limitation, we introduce TreeTeaming, an automated red teaming framework that reframes strategy exploration from static testing to a dynamic, evolutionary discovery process. At its core lies a strategic Orchestrator, powered by a Large Language Model (LLM), which autonomously decides whether to evolve promising attack paths or explore diverse strategic branches, thereby dynamically constructing and expanding a strategy tree. A multimodal actuator is then tasked with executing these complex strategies. In the experiments across 12 prominent VLMs, TreeTeaming achieves state-of-the-art attack success rates on 11 models, outperforming existing methods and reaching up to 87.60% on GPT-4o. The framework also demonstrates superior strategic diversity over the union of previously public jailbreak strategies. Furthermore, the generated attacks exhibit an average toxicity reduction of 23.09%, showcasing their stealth and subtlety. Our work introduces a new paradigm for automated vulnerability discovery, underscoring the necessity of proactive exploration beyond static heuristics to secure frontier AI models. The code and data are available at: https://github.com/ChunXiaostudy/TreeTeaming. Warning: This paper contains examples of harmful texts and images, and reader discretion is recommended.
Task-Driven Implicit Representations for Automated Design of LiDAR Systems
Authors: Nikhil Behari; Aaron Young; Tzofi Klinghoffer; Akshat Dave; Ramesh Raskar
Venue: CVPR 2026 (main)

Abstract: Imaging system design is a complex, time-consuming, and largely manual process; LiDAR design, ubiquitous in mobile devices, autonomous vehicles, and aerial imaging platforms, adds further complexity through unique spatial and temporal sampling requirements. In this work, we propose a framework for automated, task-driven LiDAR system design under arbitrary constraints. To achieve this, we represent LiDAR configurations in a continuous six-dimensional design space and learn task-specific implicit densities in this space via flow-based generative modeling. We then synthesize new LiDAR systems by modeling sensors as parametric distributions in 6D space and fitting these distributions to our learned implicit density using expectation-maximization, enabling efficient, constraint-aware LiDAR system design. We validate our method on diverse tasks in 3D vision, enabling automated LiDAR system design across real-world-inspired applications in face scanning, robotic tracking, and object detection.