GenAI4ELab/papercli-papers huggingface dataset:
Gaussian Mapping for Evolving Scenes
Authors: Vladimir Yugay; Thies Kersten; Luca Carlone; Theo Gevers; Martin R. Oswald; Lukas Schmid
Venue: CVPR 2026 (main)

Abstract: Mapping systems with novel view synthesis (NVS) capabilities are widely used in computer vision, as well as in various applications, including augmented reality, robotics, and autonomous driving. Most notably, 3D Gaussian Splatting-based systems show high NVS performance; however, many current approaches are limited to static scenes. While recent works have begun addressing short-term dynamics (motion within the camera's view), long-term dynamics (the scene evolving through changes out of view) remain less explored.To overcome this limitation, we introduce a dynamic scene adaptation mechanism that continuously updates the 3D representation to reflect the latest changes. In addition, since maintaining geometric and semantic consistency remains challenging due to stale observations disrupting the reconstruction process, we propose a novel keyframe management mechanism that discards outdated observations while preserving as much information as possible. We thoroughly evaluate Gaussian Mapping for Evolving Scenes (GaME) on both synthetic and real-world datasets, achieving a 29.7% improvement in PSNR and a 3 times improvement in L1 depth error over the most competitive baseline.
MERIT: Multi-domain Efficient RAW Image Translation
Authors: Wenjun Huang; Shenghao Fu; Yian Jin; Yang Ni; Ziteng Cui; Hanning Chen; Yirui He; Yezi Liu; Sanggeon Yun; SungHeon Jeong; Ryozo Masukawa; William Youngwoo Chung; Mohsen Imani
Venue: CVPR 2026 (main)

Abstract: RAW images captured by different camera sensors exhibit substantial domain shifts due to varying spectral responses, noise characteristics, and tone behaviors, complicating their direct use in downstream computer vision tasks. Prior methods address this problem by training domain-specific RAW-to-RAW translators for each source-target pair, but such approaches do not scale to real-world scenarios involving multiple types of commercial cameras. In this work, we introduce MERIT, the first unified framework for multi-domain RAW image translation, which leverages a single model to perform translations across arbitrary camera domains. To address domain-specific noise discrepancies, we propose a sensor-aware noise modeling loss that explicitly aligns the signal-dependent noise statistics of the generated images with those of the target domain. To facilitate standardized evaluation, we introduce MDRAW, the first dataset tailored for multi-domain RAW image translation, comprising both paired and unpaired RAW captures from five diverse camera sensors across a wide range of scenes. Extensive experiments demonstrate that MERIT outperforms prior models in both quality (+5.56 dB) and scalability (80% reduction in training iterations).
Memory-Efficient Transfer Learning with Fading Side Networks via Masked Dual Path Distillation
Authors: Yutong Zhang; Jiaxin Chen; Honglin Chen; Kaiqi Zheng; Shengcai Liao; Hanwen Zhong; Weixin Li; Yunhong Wang
Venue: CVPR 2026 (main)

Abstract: Memory-efficient transfer learning (METL) approaches have recently achieved promising performance in adapting pre-trained models to downstream tasks. They avoid applying gradient backpropagation in large backbones, thus significantly reducing the number of trainable parameters and high memory consumption during fine-tuning. However, since they typically employ a lightweight and learnable side network, these methods inevitably introduce additional memory and time overhead during inference, which contradicts the ultimate goal of efficient transfer learning. To address the above issue, we propose a novel approach dubbed Masked Dual Path Distillation (MDPD) to accelerate inference while retaining parameter and memory efficiency in fine-tuning with fading side networks. Specifically, MDPD develops a framework that enhances the performance by mutually distilling the frozen backbones and learnable side networks in fine-tuning, and discard the side network during inference without sacrificing accuracy. Moreover, we design a novel feature-based knowledge distillation method for the encoder structure with multiple layers. Extensive experiments on distinct backbones across vision/language-only and vision-and-language tasks demonstrate that our method not only accelerates inference by at least 25.2% while keeping parameter and memory consumption comparable, but also remarkably promotes the accuracy compared to SOTA approaches. The source code is available at https://github.com/Zhang-VKk/MDPD.
LAOF: Robust Latent Action Learning with Optical Flow Constraints
Authors: Xizhou Bu; Jiexi Lyu; Fulei Sun; Ruichen Yang; Zhiqiang Ma; Wei Li
Venue: CVPR 2026 (main)

Abstract: Learning latent actions from large-scale videos is crucial for the pre-training of scalable embodied foundation models, yet existing methods often struggle with action-irrelevant distractors. Although incorporating action supervision can alleviate these distractions, its effectiveness is restricted by the scarcity of available action labels. Optical flow represents pixel-level motion between consecutive frames, naturally suppressing background elements and emphasizing moving objects. Motivated by this, we propose robust Latent Action learning with Optical Flow constraints (LAOF), a pseudo-supervised framework that leverages the agent's optical flow as an action-driven signal to learn latent action representations robust to distractors. Experimental results show that the latent representations learned by LAOF outperform existing methods on downstream imitation learning and reinforcement learning tasks. This superior performance arises from optical flow constraints, which substantially stabilize training and improve the quality of latent representations under extremely label-scarce conditions, while remaining effective as the proportion of action labels increases to 10%. Importantly, even without action supervision, LAOF matches or surpasses action-supervised methods trained with 1% of action labels.
Language-driven Fine-grained Retrieval
Authors: Shijie Wang; Xin Yu; Yadan Luo; Zijian Wang; Pengfei Zhang; Zi Huang
Venue: CVPR 2026 (main)

Abstract: Existing fine-grained image retrieval (FGIR) methods learn discriminative embeddings by adopting semantically sparse one-hot labels derived from category names as supervision. While effective on seen classes, such supervision overlooks the rich semantics encoded in category names, hindering the modeling of comparability among cross-category details and, in turn, limiting generalization to unseen categories. To tackle this, we introduce LaFG, a Language-driven framework for Fine-Grained Retrieval that converts class names into attribute-level supervision using large language models (LLMs) and vision-language models (VLMs). Treating each name as a semantic anchor, LaFG prompts an LLM to generate detailed, attribute-oriented descriptions. To mitigate attribute omission in these descriptions, it leverages a frozen VLM to project them into a vision-aligned space, clustering them into a dataset-wide attribute vocabulary while harvesting complementary attributes from related categories. Leveraging this vocabulary, a global prompt template selects category-relevant attributes, which are aggregated into category-specific linguistic prototypes. These prototypes supervise the retrieval model to steer it toward pinpointing visual details consistent with linguistic descriptions, thus modeling comparability among object details. Extensive evaluations show that LaFG achieves impressive performance on both fine- and coarse-grained benchmarks and generalizes well to unseen categories.
Seeing Depth Through Frequency and Motion: A Progressive Training Paradigm for Monocular Depth Estimation
Authors: Ke Li; Bolin Song; Hongbo Liu
Venue: CVPR 2026 (main)

Abstract: Self-supervised monocular depth estimation has achieved remarkable progress in recent years, yet frequency aliasing and the lack of fine-grained cross-frame motion modeling still lead to blurred depth boundaries and suboptimal camera motion estimation.To address these challenges, we propose a progressive self-supervised framework that integrates a Frequency-Guided Depth Network (FGDepth) and a PoseQuery Network (PQNet). FGDepth incorporates a plug-and-play Frequency-Guided Sampling module that explicitly enhances high-frequency details and suppresses aliasing artifacts, producing depth maps with sharper boundaries. PQNet employs channel-aligned attention to model fine-grained cross-frame motion features, enabling more accurate and robust camera motion estimation. Furthermore, we design a progressive three-stage decoupled training strategy that effectively leverages the complementarity between depth and pose estimation, further improving overall performance.Extensive experiments on the KITTI benchmark demonstrate state-of-the-art performance, achieving a 4.1% reduction in Sq Rel over strong baselines, and our method also exhibits excellent cross-dataset generalization on Make3D. Ablation studies further validate the effectiveness of each proposed component.
X-AVDT: Audio-Visual Cross-Attention for Robust Deepfake Detection
Authors: Youngseo Kim; Kwan Yun; Seokhyeon Hong; Sihun Cha; Colette Suhjung Koo; Junyong Noh
Venue: CVPR 2026 (main)

Abstract: The surge of highly realistic synthetic videos produced by contemporary generative systems has significantly increased the risk of malicious use, challenging both humans and existing detectors. Against this backdrop, we take a generator-side view and observe that internal cross-attention mechanisms in these models encode fine-grained speech-motion alignment, offering useful correspondence cues for forgery detection. Building on this insight, we propose X-AVDT, a robust and generalizable deepfake detector that probes generator-internal audio-visual signals accessed via DDIM inversion to expose these cues. X-AVDT extracts two complementary signals: (i) a video composite capturing inversion-induced discrepancies, and (ii) audio-visual cross-attention feature reflecting modality alignment enforced during generation. To enable faithful, cross-generator evaluation, we further introduce MMDF, a new multi-modal deepfake dataset spanning diverse manipulation types and rapidly evolving synthesis paradigms, including GANs, diffusion, and flow-matching. Extensive experiments demonstrate that X-AVDT achieves leading performance on MMDF and generalizes strongly to external benchmarks and unseen generators, outperforming existing methods with accuracy improved by +13.1%. Our findings highlight the importance of leveraging internal audio-visual consistency cues for robustness to future generators in deepfake detection. The code and dataset will soon be available.
SURF: Signature-Retained Fast Video Generation
Authors: Kaixin Ding; Xi Chen; Sihui Ji; Yuan Gao; Liang Hou; Xin Tao; Hengshuang Zhao
Venue: CVPR 2026 (main)

Abstract: The demand for high-resolution video generation is growing rapidly. However, the generation resolution is severely constrained by slow inference speeds. For instance, Wan2.1 requires over 50 minutes to generate a single 720p video. While previous works explore accelerating video generation from various aspects, most of them compromise the distinctive signatures (e.g., layout, semantic, motion) of the original model. In this work, we propose **SURF**, an efficient framework for generating high-resolution videos, while maximally keeping the signatures. Specifically, SURF divides video generation into two stages: First, we leverage the pretrained model to infer at optimal resolution and downsample latent to generate low-resolution previews in fast speed; then we design a Refiner to upscale the preview. In the preview stage, we identify that directly inferring a model (trained with higher resolution) on lower resolution causes severe losses in signatures. So we introduce noise reshifting, a training-free technique that mitigates this issue by conducting initial denoising steps on the original resolution and switching to low resolution in later steps. In the refine stage, we establish a mapping relationship between the preview and the high-resolution target, which significantly reduces the denoising steps. We further integrate shifting windows and carefully design the training paradigm to get a powerful and efficient Refiner. In this way, SURF enables generating high-resolution videos efficiently while maximally closer to the signatures of the given pretrained model. SURF is conceptually simple and could serve as a plug-in that is compatible with various base model and acceleration methods. For example, it achieves 12.5x speedup for generating 5-second, 16fps, 720p Wan 2.1 videos and 8.7x speedup for generating 5-second, 24fps, 720p HunyuanVideo.
See, Think, Act: Teaching Multimodal Agents to Effectively Interact with GUI by Identifying Toggles
Authors: Zongru Wu; Rui Mao; Zhiyuan Tian; Pengzhou Cheng; Tianjie Ju; Zheng Wu; Lingzhong Dong; Haiyue Sheng; Zhuosheng Zhang; Gongshen Liu
Venue: CVPR 2026 (main)

Abstract: The advent of multimodal agents facilitates effective interaction within graphical user interface (GUI), especially in ubiquitous GUI control. However, their inability to reliably execute toggle control instructions remains a key bottleneck. To investigate this, we construct a state control benchmark with binary toggle instructions derived from public datasets. Evaluation results of existing agents demonstrate their notable unreliability, particularly when the current toggle state already matches the desired state. To address the challenge, we propose State-aware Reasoning (StaR), a multimodal reasoning method that enables agents to perceive the current toggle state, infer the desired state from the instruction, and act accordingly. Experiments on four multimodal agents demonstrate that StaR can improve toggle instruction execution accuracy by over 30%. Further evaluations on three public agentic benchmarks show that StaR also enhances general agentic task performance. Finally, evaluations on a dynamic environment highlight the potential of StaR for real-world applications. Code and benchmark: https://github.com/ZrW00/StaR.
FeatureFool: Zero-Query Fooling of Video Models via Feature Map
Authors: Duoxun Tang; Xi Xiao; Guangwu Hu; Kangkang Sun; Xiao Yang; Dongyang Chen; Qing Li; Yong-jie Yin; Jiyao Wang
Venue: CVPR 2026 (main)

Abstract: The vulnerability of deep neural networks (DNNs) has been preliminarily verified. Existing black-box adversarial attacks usually require multi-round interaction with the model and consume numerous queries, which is impractical in the real-world and hard to scale to recently emerged Video-LLMs. Moreover, no attack in the video domain directly leverages feature maps to shift the clean-video feature space. We therefore propose FeatureFool, a stealthy, video-domain, zero-query black-box attack that utilizes information extracted from a DNN to alter the feature space of clean videos. Unlike query-based methods that rely on iterative interaction, FeatureFool performs a zero-query attack by directly exploiting DNN-extracted information. This efficient approach is unprecedented in the video domain. Experiments show that FeatureFool achieves an attack success rate above 70% against traditional video classifiers without any queries. Benefiting from the transferability of the feature map, it can also craft harmful content and bypass Video-LLM recognition. Additionally, adversarial videos generated by FeatureFool exhibit high quality in terms of SSIM, PSNR, and Temporal-Inconsistency, making the attack barely perceptible. This paper may contain violent or explicit content.
A Temporal and Content Co-Awareness Latent Diffusion for Controllable Hand Image Generation
Authors: Shuang Hao; Pengfei Ren; Haifeng Sun; Ting Pan; Qi Qi; Lei Zhang; Cong Liu; Jianxin Liao; Jingyu Wang
Venue: CVPR 2026 (main)

Abstract: Controllable hand image generation aims to synthesize geometrically accurate images with consistent appearance. Recently, diffusion models have been widely applied for hand image synthesis. However, through input-level fusion or feature-level modulation, existing methods inject control signals with fixed strength across all timesteps, ignoring the progressive nature of the denoising process. In this paper, we reveal that the modulation of control signals depends on the denoising state and condition complexity. Due to distinct semantic distributions and information densities, achieving effective interaction among these heterogeneous representations remains challenging. To address this, we propose a Temporal and Content Co-Awareness Latent Diffusion method that introduces a dual-driven modulation strategy. Specifically, we design a query-based interaction mechanism to mitigate information redundancy and align semantic distributions. Leveraging cross-domain interaction, the model infers required control information to dynamically adjust pose and appearance injection strengths. Furthermore, we design a Pose-Invariant Appearance Encoder that captures both global appearance consistency and local texture details. Extensive experiments validate our superiority over state-of-the-art.
StyleGallery: Training-free and Semantic-aware Personalized Style Transfer from Arbitrary Image References
Authors: Boyu He; Yunfan Ye; Chang Liu; Weishang Wu; Fang Liu; Zhiping Cai
Venue: CVPR 2026 (main)

Abstract: Despite the advancements in diffusion-based image style transfer, existing methods are commonly limited by 1) semantic gap: the style reference could miss proper content semantics, causing uncontrollable stylization; 2) reliance on extra constraints (e.g., semantic masks) restricting applicability; 3) rigid feature associations lacking adaptive global-local alignment, failing to balance fine-grained stylization and global content preservation. These limitations, particularly the inability to flexibly leverage style inputs, fundamentally restrict style transfer in terms of personalization, accuracy, and adaptability.To address these, we propose StyleGallery, a training-free and semantic-aware framework that supports arbitrary reference images as input and enables effective personalized customization.It comprises three core stages: semantic region segmentation (adaptive clustering on latent diffusion features to divide regions without extra inputs); clustered region matching (block filtering on extracted features for precise alignment); and style transfer optimization (energy function-guided diffusion sampling with regional style loss to optimize stylization).Experiments on our introduced benchmark demonstrate that StyleGallery outperforms state-of-the-art methods in content structure preservation, regional stylization, interpretability, and personalized customization, particularly when leveraging multiple style references. The source code and dataset are available at https://github.com/iiiiiiiword/StyleGallery.
Meta-Learning In-Context Enables Training-Free Cross Subject Brain Decoding
Authors: Mu Nan; Muquan Yu; Weijian Mai; Jacob S. Prince; Hossein Adeli; Rui Zhang; Jiahang Cao; Benjamin Becker; John A. Pyles; Margaret M. Henderson; Chunfeng Song; Nikolaus Kriegeskorte; Michael J. Tarr; Xiaoqing Hu; Andrew F. Luo
Venue: CVPR 2026 (main)

Abstract: Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computational models of vision. A field-wide goal is to achieve generalizable, cross-subject models. A major obstacle towards this goal is the substantial variability in neural representations across individuals, which has so far required training bespoke models or fine-tuning separately for each subject. To address this challenge, we introduce a meta-optimized approach for semantic visual decoding from fMRI that **generalizes to novel subjects without any fine-tuning**. By simply conditioning on a small set of image-brain activation examples from the new individual, our model rapidly infers their unique neural encoding patterns to facilitate robust and efficient visual decoding. Our approach is explicitly optimized for in-context learning of the new subject's encoding model and performs decoding by hierarchical inference, inverting the encoder. First, for multiple brain regions, we estimate the per-voxel visual response encoder parameters by constructing a context over multiple stimuli and responses. Second, we construct a context consisting of encoder parameters and response values over multiple voxels to perform aggregated functional inversion. We demonstrate strong cross-subject and cross-scanner generalization across diverse visual backbones without retraining or fine-tuning. Moreover, our approach requires neither anatomical alignment nor stimulus overlap. This work is a critical step towards a generalizable foundation model for non-invasive brain decoding.
fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understanding
Authors: Yuxiang Wei; Yanteng Zhang; Xi Xiao; Chengxuan Qian; Tianyang Wang; Vince D. Calhoun
Venue: CVPR 2026 (main)

Abstract: Recent advances in multimodal large language models (LLMs) have enabled unified reasoning across images, audio, and video, but extending such capability to brain imaging remains largely unexplored. Bridging this gap is essential to link neural activity with semantic cognition and to develop cross-modal brain representations. To this end, we present fMRI-LM, a foundational model that bridges functional MRI (fMRI) and language through a three-stage framework. In Stage 1, we learn a neural tokenizer that maps fMRI into discrete tokens embedded in a language-consistent space. In Stage 2, a pretrained LLM is adapted to jointly model fMRI tokens and text, treating brain activity as a sequence that can be temporally predicted and linguistically described. To overcome the lack of natural fMRI-text pairs, we construct a large descriptive corpus that translates diverse imaging-based features into structured textual descriptors, capturing the low-level organization of fMRI signals. In Stage 3, we perform multi-task, multi-paradigm instruction tuning to endow fMRI-LM with high-level semantic understanding, supporting diverse downstream applications. Across various benchmarks, fMRI-LM achieves strong zero-shot and few-shot performance, and adapts efficiently with parameter-efficient tuning (LoRA), establishing a scalable pathway toward a language-aligned, universal model for structural and semantic understanding of fMRI.