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首次观测 · 记录 #1CVF · CVPR 2026:cvf:044ed0f76303b6851f4fdbee9c0e7041CVF · CVPR 2026

Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification

Liu, Han, Georgescu, Bogdan, Zhang, Yanbo, Yoo, Youngjin, Baumgartner, Michael, Gao, Riqiang, Wang, Jianing, Zhao, Gengyan 等 11 位作者

原文摘要

3D medical image classification is essential to modern clinical workflows. Medical foundation models (FMs) have emerged as a promising approach for scaling to new tasks, yet current research suffers from three critical pitfalls: data-regime bias, suboptimal adaptation, and insufficient task coverage. In this paper, we address these pitfalls and introduce AnyMC3D, a scalable 3D classifier adapted from 2D FMs. It allows efficient scaling to new tasks by adding only lightweight plugins ( 1M parameters per task) to a single frozen backbone. Besides, this versatile framework also supports multi-view inputs, auxiliary pixel-level supervision, and interpretable heatmap generation. We establish a comprehensive benchmark of 12 tasks covering diverse pathologies, anatomies, and modalities and systematically evaluate state-of-the-art 3D classification techniques. Our analysis reveals several key insights: (1) effective adaptation is critical to unlock FM potential, (2) general-purpose FMs can match medical-specific FMs if properly adapted, and (3) 2D-based methods surpass 3D architectures for 3D classification. For the first time, we demonstrate the feasibility of achieving state-of-the-art performance across diverse applications using a single scalable framework (e.g., 1st place in the *** challenge), eliminating the need for separate task-specific 3D models.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:0fffc628efcef48ba3e05f3db5524ff5CVF · CVPR 2026

LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis

Szymanowicz, Stanislaw, Chen, Minghao, Wang, Jianyuan, Rupprecht, Christian, Vedaldi, Andrea

原文摘要

Recent work has shown that neural networks can perform 3D tasks such as Novel View Synthesis (NVS) without explicit 3D reconstruction. Even so, we argue that strong 3D inductive biases are still helpful in the design of such networks. We show this point by introducing LagerNVS, an encoder-decoder neural network for NVS that builds on `3D-aware' latent features. The encoder is initialized from a 3D reconstruction network pre-trained using explicit 3D supervision. This is paired with a lightweight decoder, and trained end-to-end with photometric losses. LagerNVS achieves state-of-the-art deterministic feed-forward Novel View Synthesis (including 31.4 PSNR on Re10k), with and without known cameras, renders in real time, generalizes to in-the-wild data, and can be paired with a diffusion decoder for generative extrapolation. See szymanowiczs.github.io/lagernvs for code, models, and examples.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:1243829dd36fc8ba201ab7402a22408aCVF · CVPR 2026

PRISM: Learning a Shared Primitive Space for Transferable Skeleton Action Representation

Yang, Di, Wang, Yaohui, Shao, Shuai, Brémond, François, Wang, Jiangtao

原文摘要

Real-world human action understanding remains challenging due to long-tailed label distributions, compositional motion patterns, and viewpoint variations. Existing skeleton-based methods often lack a structured and transferable representation of motion, and task-specific models for generation, classification, and detection are usually trained independently, resulting in fragmented pipelines and limited cross-task generalization. We present PRISM, a PRImitive-centric Skeleton Modeling framework that learns a shared motion representation from a motion generation objective and transfers it to perception tasks. PRISM represents each action sequence as a trajectory in a primitive coefficient space, which captures how a set of learned atomic motion primitives contribute to the observed motion. A structured decomposition module learns this representation in a physically grounded and view-invariant manner via motion generation. Instead of enforcing joint or unified training across tasks, PRISM provides a single primitive-centric representation that can be sequentially transferred to downstream classification and frame-wise detection through lightweight task heads. This representation introduces structure, compositionality, and improved generalization across distinct supervisions. PRISM consistently improves performance on long-tailed and multi-label datasets and enables interpretable reasoning over compositional and rare actions. Extensive experimental results show that the structured primitive space serves as a transferable and robust foundation for diverse action understanding tasks in real-world datasets.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:259d88b47a31b1168731d5f1964a0ecfCVF · CVPR 2026

Eliminate Distance Differences Induced by Backdoor Attacks: Layer-Selective Training and Clipping to Mask Backdoor Models

Li, Xuzeng, Zhang, Tao, Tang, Xiangyun, Wang, Jiacheng, Wang, Jian, Kang, Jiawen, Liu, Jiqiang, Han, Zhen 等 10 位作者

原文摘要

Federated learning (FL) enables a central server to collaboratively train a global model with multiple clients while preserving data privacy. However, the distributed nature of FL makes the paradigm vulnerable to backdoor attacks, as proved by numerous recent studies. Although existing studies improve the effectiveness of backdoor attacks through optimized triggers, they have two limitations: (1) they ignore the heterogeneous contribution of individual model layers to the success of a backdoor; (2) they induce conspicuous differences between backdoor and clean models in the early stages of poisoning. The limitations cause backdoor models to exhibit significant discrepancies from clean models, making them easily detectable. To fill these gaps, we propose LaySelFL, a novel layer-selective method to eliminate distance differences induced by the backdoor to conceal attacks in FL. Our central insight is that different layers contribute unequally to backdoor attacks, by localizing poisoning to layers that are most sensitive to backdoor objectives, an attacker can reduce the model differences substantially between the backdoor and clean models. Concretely, LaySelFL identifies sensitive layers via both dynamic and static evaluations of parameter differences between backdoor and benign models, and then applies a targeted training protocol and a regularized loss that constrains differences from the global model in each round. Finally, LaySelFL performs clipping on non-poisoning layers to further mask residual differences introduced by the attack. This strategy yields a more covert and resilient backdoor attack. Extensive experiments show that LaySelFL increases the effectiveness of attacks by 25% and reduces the effectiveness of defense methods to 4%.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:2770050cc360d6db960cc7880351e480CVF · CVPR 2026

Refacade: Editing Object with Given Reference Texture

Huang, Youze, Ruan, Penghui, Zi, Bojia, Qi, Xianbiao, Wang, Jianan, Xiao, Rong

原文摘要

Recent advances in diffusion models have brought remarkable progress in image and video editing, yet some tasks remain underexplored. In this paper, we extend Object Retexture into video domain, which transfers local textures from a reference object to a target object in images or videos. To perform this task, a straightforward solution is to use ControlNet conditioned on the source structure and the reference texture. However, this approach suffers from limited controllability due to two reasons: conditioning on the raw reference image introduces unwanted structural information, and this method fails to disentangle visual texture and structure information of the source. To address this problem, we proposed a method, namely Refacade, that consists of two key designs to achieve precise and controllable texture transfer in both images and videos. First, we employ a texture remover trained on paired textured/untextured 3D mesh renderings to remove appearance information while preserving geometry and motion of source videos. Second, we disrupt the reference's global layout using a jigsaw permutation, encouraging the model to focus on local texture statistics rather than global layout of object. Extensive experiments demonstrate superior visual quality, precise editing, and controllability, outperforming strong baselines in both quantitative and human evaluations.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:34c70944e4fda5268fd42e0ec9b90352CVF · CVPR 2026

Beyond [CLS] Token: Query-Driven Token-Level Forgery Purification for Generalizable Deepfake Detection

Wang, Changshuo, Wang, Jiangming, Zhang, Ke-Yue, Yao, Taiping, Ding, Shouhong, Wang, Shunli, Yi, Ran, Ma, Lizhuang

原文摘要

We investigate state-of-the-art deepfake detectors that leverage ViT-based vision foundation models and discover that the [CLS] token suffers from the Pre-trained Information Bias (PIB), i.e., it tends to mainly focus on global semantics due to the knowledge dominated by pre-trained model parameters, while struggling to emphasize subtle local forgery cues. To overcome this limitation, one potential way is incorporating the token-level features to reform a detection-specific token. To this end, we propose Query-Driven Token-Level Forgery Purification (QTFP) framework to better capture local forgery traces without losing useful pre-trained prior. Specifically, we introduce randomly initialized, learnable query tokens independent of the backbone and prior knowledge, which effectively aggregate multi-patch evidence into a global token for detection. To make query tokens focus on meaningful regions, we propose a theoretical fake-likelihood contrastive learning loss, which employs a weighting strategy to highlight significant fake regions while diminishing real-like patch impact. Using SNR theory, we verify that the designed weight is both reliable and informative. To further maintain useful authentic information, a real-attention alignment constraint is applied to query tokens. These designs go beyond relying solely on the [CLS] token by jointly reorganizing real and fake information across all tokens, which successfully enhance detector robustness. Extensive experiments on diverse datasets demonstrate the effectiveness of our method.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:486f0dcd9e84c1583508db7d222a0a96CVF · CVPR 2026

Beyond Text: Visual Description Assembly by Probabilistic Model for CLIP-based Weakly Supervised Semantic Segmentation

Qiu, Xianglin, Wang, Jian, Wang, Xiaolei, Zhang, Zhen, Xiao, Jimin

原文摘要

Contrastive Language-Image Pre-training (CLIP) offers a new paradigm for Weakly Supervised Semantic Segmentation (WSSS) by generating Class Activation Maps (CAMs) from text-image alignment. Existing methods primarily rely on hand-crafted templates or general attribute descriptions generated by a large language model to construct text prototypes for querying visual features. However, these strategies faces two major limitations: the inherent modality gap in CLIP prevents text prototypes achieving tight alignment with visual features; and their static text prototypes cannot adaptively respond to target instances that exhibit diverse visual attributes. To address these challenges, our key insight is to directly construct instance-specific visual description prototype as query, thereby bypassing the suboptimal static text description optimization. To this end, we propose the Visual Description Assembly (VDA) framework. It employs a probabilistic model to map complex CLIP visual features into a structured latent space. This latent space allows us to explicitly disentangle and aggregate varied visual attributes, and then dynamically assemble them into instance-specific visual prototypes. Furthermore, to enhance the robustness of this prototype, we adaptively incorporate the semantically stable text prototype into it as the final query for generating superior CAMs. Experimental results show our method outperforms existing baselines, achieving state-of-the-art performance on WSSS benchmarks.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:52d0cfb544bc9802ebc48caecd12492eCVF · CVPR 2026

LitePT: Lighter Yet Stronger Point Transformer

Yue, Yuanwen, Robert, Damien, Wang, Jianyuan, Hong, Sunghwan, Wegner, Jan Dirk, Rupprecht, Christian, Schindler, Konrad

原文摘要

Modern neural architectures for 3D point cloud processing contain both convolutional layers and attention blocks, but the best way to assemble them remains unclear. We analyse the role of different computational blocks in 3D point cloud networks and find an intuitive behaviour: convolution is adequate to extract low-level geometry at high-resolution in early layers, where attention is expensive without bringing any benefits; attention captures high-level semantics and context in low-resolution, deep layers more efficiently. Guided by this design principle, we propose a new, improved 3D point cloud backbone that employs convolutions in early stages and switches to attention for deeper layers. To avoid the loss of spatial layout information when discarding redundant convolution layers, we introduce a novel, parameter-free 3D positional encoding, PointROPE. The resulting LitePT model has 3.6x fewer parameters, runs 2x faster, and uses 2x less memory than the state-of-the-art Point Transformer V3, but nonetheless matches or outperforms it on a range of tasks and datasets.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:592309253983612a00adddd228fc8ca3CVF · CVPR 2026

Dynamics-Aware Preference Optimization for Vision-Language Models

Zhang, Jusheng, Cai, Kaitong, Yang, Jing, Wang, Jian, Wang, Keze

原文摘要

Preference-based finetuning of vision-language models (VLMs) is notoriously unstable, as trivially wrong negatives inject uninformative gradients that distort optimization and degrade calibration. This work revisits this issue through the lens of learning dynamics and identifies a core pathology, the squeezing effect, where easy negatives retain large, misaligned gradients despite having negligible loss.To address this, we propose Cooling-Weighted Direct Preference Optimization (CW-DPO), a two-stage framework that first smooths and then stabilizes the alignment process. Stage 1 employs a constrained SFT phase with low-weight "gentle negatives" to regularize overconfident distributions and flatten the loss landscape. Stage 2 introduces a competence-aware cooling weight that adaptively scales negative gradients according to the model's average per-token log-probability, suppressing uninformative updates while emphasizing hard, on-policy contrasts. This dynamics-aware weighting effectively mitigates the squeezing effect and enables smoother convergence.Extensive experiments on mainstream benchmarks--including COCO, Flickr30k, NoCaps, MMMU, and MMBench1.1--show that CW-DPO achieves state-of-the-art performance, for example +3.4 CIDEr over PPO and +2.4% absolute accuracy on MMMU, while improving calibration and halving convergence steps. This demonstrates that smoothing before cooling constitutes a simple yet general principle for robust VLM preference optimization.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:7d9cdeb50138a9e5144dac2f1cc7135fCVF · CVPR 2026

UniChange: Unifying Change Detection with Multimodal Large Language Model

Zhang, Xu, Li, Danyang, Dong, Xiaohang, Wu, Tianhao, Yu, Hualong, Wang, Jianye, Li, Qicheng, Li, Xiang

原文摘要

Change detection (CD) is a fundamental task for monitoring and analysing land cover dynamics. While recent high performance models and high quality datasets have significantly advanced the field, a critical limitation persists. Current models typically acquire limited knowledge from single-type annotated data and cannot concurrently leverage diverse binary change detection (BCD) and semantic change detection (SCD) datasets. This constraint leads to poor generalisation and limited versatility. The recent advancements in Multimodal Large Language Models (MLLMs) introduce new possibilities for a unified CD framework. We leverage the language priors and unification capabilities of MLLMs to develop UniChange, the first MLLM-based unified change detection model. UniChange integrates generative language abilities with specialised CD functionalities. We introduce three special tokens: [T1], [T2], and [CHANGE], utilising their embeddings as the key to query variations. This approach successfully accommodates both BCD and SCD tasks. Furthermore, UniChange utilises text prompts to guide the identification of change categories, eliminating the reliance on predefined classification heads. This design allows UniChange to effectively acquire knowledge from multi-source datasets, even when their class definitions conflict. Experiments on four public benchmarks (WHU-CD, S2Looking, LEVIR-CD+, and SECOND) demonstrate SOTA performance, achieving IoU scores of 90.41, 53.04, 78.87, and 57.62, respectively, surpassing all previous methods. The code is available at https://github.com/NKU-HLT/UniChange.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:88d761b99916621fed4669dab1b23b6bCVF · CVPR 2026

HandX: Scaling Bimanual Motion and Interaction Generation

Zhang, Zimu, Zhang, Yucheng, Xu, Xiyan, Wang, Ziyin, Xu, Sirui, Zhou, Kai, Zhou, Bing, Guo, Chuan 等 11 位作者

原文摘要

Synthesizing human motion has advanced rapidly, yet realistic hand motion and bimanual interaction remain underexplored. Whole-body models often miss the fine-grained cues that drive dexterous behavior, finger articulation, contact timing, and inter-hand coordination, and existing resources lack high-fidelity bimanual sequences that capture nuanced finger dynamics and collaboration. To fill this gap, we present HandX, a unified foundation spanning data, annotation, and evaluation. We consolidate and filter existing datasets for quality, and collect a new motion-capture dataset targeting underrepresented bimanual interactions with detailed finger dynamics. For scalable annotation, we introduce a decoupled paradigm that extracts representative motion features, e.g., contact events and finger flexion, and then leverages reasoning from large language models to produce fine-grained, semantically rich descriptions aligned with these features. Building on the resulting data and annotations, we benchmark diffusion and autoregressive models with versatile conditioning modes. Experiments demonstrate high-quality dexterous motion generation, supported by our newly proposed hand-focused metrics. We further observe clear scaling trends: larger models trained on larger, higher-quality datasets produce more semantically coherent bimanual motion. Our dataset is released to support future research.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:b7e7b13a4837cee1846edbc062932856CVF · CVPR 2026

VRCLIP: Multimodal Canonical Correlation Alignment for CLIP-Driven Vision-Radio Person Re-Identification

Zhang, Rui, Wang, Yaqi, Li, Yadong, Geng, Ruixu, Wang, Jianyang, Ying, Qijun, Zhang, Dongheng, Hu, Yang 等 9 位作者

原文摘要

Multimodal person Re-IDentification (ReID) aims to reliably associate specific individuals by utilizing complementary information from heterogeneous modalities. In contrast, low-frequency radio frequency (RF) signals, with their superior penetration capability and illumination invariance, provide ideal complementary information. However, a key challenge in fusing these heterogeneous modalities lies in the conventional approach that relies heavily on cross-modal distribution matching, which often over-regularizes and weakens the discriminative capacity within each modality. Rather than enforcing direct distribution alignment, canonical correlation analysis (CCA) constructs a shared subspace that maximizes cross-modal correlation, inherently balancing modality specificity and shared semantics. Inspired by this, we reformulate cross-modal alignment as a correlation maximization problem, avoiding direct constraints on feature distributions and guiding the model to harmonize intra-modal discriminative learning with cross-modal alignment. Specifically, VRCLIP first refines CLIP's visual encoder with illumination-disentangling objectives, then aligns RGB and RF embeddings in a canonical correlation subspace, and finally employs an RF-anchored reliability gate for adaptive fusion. To advance the area, we will release VRR, the first large-scale vision-radio ReID dataset with over 650K paired image-radar samples and position annotations for 31 participants. Extensive experiments show state-of-the-art 93.9% mAP and robust generalization across diverse lighting and occlusion conditions.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:c1fb4c3cb1e47f5e64fbbabc62fcfd40CVF · CVPR 2026

Universal-to-Specific: Dynamic Knowledge-Guided Multiple Instance Learning for Few-Shot Whole Slide Image Classification

Li, Junjian, Kuang, Hulin, Liu, Jin, Yue, Hailin, He, Mengshen, Wang, Jianxin

原文摘要

Multiple Instance Learning (MIL) has emerged as the dominant paradigm for the analysis of gigapixel-scale Whole Slide Images (WSIs). However, recent methods leveraging guidance from Vision-Language Models often rely on static and universal pathological descriptions. This one-size-fits-all strategy fails to account for the vast morphological heterogeneity within individual WSIs, as its uniform guidance is not tailored to slide-specific visual evidence. To address this, we propose DyKo, a Dynamic Knowledge-guided MIL framework that adapts universal knowledge to slide-specific evidence for few-shot WSI classification. The core of DyKo is the WSI-Adaptive Knowledge Instantiation module (WAKI). WAKI begins by identifying key visual prototypes within a specific WSI's histology. These slide-specific prototypes then serve as queries to retrieve relevant concepts from a pathology knowledge base. This retrieved knowledge is then used to synthesize unique, knowledge-instantiated features for each instance, effectively instantiating tailored guidance at the patch level. To ensure fidelity and prevent semantic drift, we introduce a Structural Consistency loss that enforces alignment between knowledge-instantiated and visual features. Comprehensive experiments on four public real-world cancer datasets demonstrate that DyKo achieves superior performance over state-of-the-art methods in few-shot pathology diagnosis. Code is available at https://github.com/junjianli106/DyKo.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:d7c3491ae2b69bcb3719ab4411638357CVF · CVPR 2026

SceneScribe-1M: A Large-Scale Video Dataset with Comprehensive Geometric and Semantic Annotations

Wang, Yunnan, Zheng, Kecheng, Wang, Jianyuan, Chen, Minghao, Novotny, David, Rupprecht, Christian, Xu, Yinghao, Zhu, Xing 等 11 位作者

原文摘要

The convergence of 3D geometric perception and video synthesis has created an unprecedented demand for large-scale video data that is rich in both semantic and spatio-temporal information. While existing datasets have advanced either 3D understanding or video generation, a significant gap remains in providing a unified resource that supports both domains at scale. To bridge this chasm, we introduce SceneScribe-1M, a new large-scale, multi-modal video dataset. It comprises one million in-the-wild videos, each meticulously annotated with detailed textual descriptions, precise camera parameters, dense depth maps, and consistent 3D point tracks. We demonstrate the versatility and value of SceneScribe-1M by establishing benchmarks across a wide array of downstream tasks, including monocular depth estimation, scene reconstruction, and dynamic point tracking, as well as generative tasks such as text-to-video synthesis, with or without camera control. By open-sourcing SceneScribe-1M, we aim to provide a comprehensive benchmark and a catalyst for research, fostering the development of models that can both perceive the dynamic 3D world and generate controllable, realistic video content.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:d9093f3aaaa68ae1ec4b047d2315488fCVF · CVPR 2026

VGGT-Ω

Wang, Jianyuan, Chen, Minghao, Zhang, Shangzhan, Karaev, Nikita, Schönberger, Johannes, Labatut, Patrick, Bojanowski, Piotr, Novotny, David 等 10 位作者

原文摘要

We introduce VGGT-Ω, a feed-forward model for 3D reconstruction that improves accuracy, efficiency, and capabilities for both static and dynamic scenes. Prior models such as VGGT have shown that feed-forward 3D reconstruction is, in many cases, competitive with traditional optimization-based methods. Here, we show that the accuracy and robustness of these models scale predictably with model capacity and data size. To enable training 3D reconstruction models at an unprecedented scale, we introduce architectural changes that improve training efficiency and scalability, a high-quality data annotation pipeline that supports dynamic scenes, and a self-supervised learning protocol. We significantly simplify VGGT's architecture by using a single dense prediction head with multi-task supervision, removing expensive high-resolution convolutional layers, and introducing efficient scene tokens for feature aggregation in lieu of global attention. These changes allow us to train VGGT-Ω with 15 x more supervised data than prior work and to leverage vast amounts of unlabeled videos, while requiring only ~30% of VGGT's training memory. VGGT-Ω achieves strong results for 3D reconstruction of static and dynamic scenes across multiple benchmarks, e.g., improving over the previous best camera estimation accuracy by 77% on Sintel.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e4939863dced42f0b8e1447d32b47d3dCVF · CVPR 2026

Perceiving the Near, Reasoning the Distant: Coherent Long-Horizon Trajectory Prediction for Autonomous Driving

Hu, Hua, Zhou, Zikang, Zhou, Qian, Wen, Zihao, Hu, Junjie, Chen, Xinhong, Jiang, Zhengmin, Li, Yung-Hui 等 9 位作者

原文摘要

Reliable long-horizon trajectory prediction requires both high positional accuracy and physically plausible temporal motion consistency. However, existing methods suffer from two fundamental limitations. First, they overlook the inherent difference in prediction logic: near-future trajectories are primarily governed by historical dynamics, whereas distant-future behaviors are driven by high-level semantic context. Yet, most methods employ a unified decoding pathway that blurs the temporal distinction.Second, although the near future is relatively easier to predict, existing methods lack mechanisms for coherent trajectory propagation across time horizons, often resulting in kinematically implausible predictions with inconsistent heading evolution and degraded long-horizon performance. To address these challenges, we propose NDPNet, a dual-stage architecture that decouples near- and distant-horizon modeling into specialized pathways, with a dedicated transition module ensuring smooth temporal bridging. Furthermore, we introduce a novel motion-aware coherence loss that explicitly embeds kinematic priors to enforce trajectory consistency. Extensive experiments show that NDPNet achieves SOTA performance on Argoverse 2 and WOMD. Notably, on WOMD, it ranks 1^ \text st in both minFDE _6 and minADE _6 across all standard horizons (3s, 5s, 8s) without ensemble learning or NMS post-processing, and is the first to achieve sub-1.75 minFDE _6 for 8s prediction, surpassing prior methods by a large margin. The code will be released subsequently.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e6d5afd0ac0c16f38c3f04062e192a77CVF · CVPR 2026

LiteSense: Lifting Lightweight ToF with RGB for High-Resolution Metric Depth Estimation

Li, Yusheng, Lou, Lizhi, Tang, Yan, Miao, Zekai, Zhang, Shaoming, Wang, Jianmei

原文摘要

Metric depth estimation aims to recover depth maps with absolute scale, high resolution, and cross-scene consistency from visual observations. Existing approaches either rely on large-scale models or costly sensors to preserve metric accuracy and generalization, both ill-suited to resource-constrained deployment. In this paper, we propose LiteSense, a lightweight RGB-ToF fusion framework that leverages compact normalized histogram (CNH) signals together with RGB cues to achieve efficient and reliable metric depth estimation. Specifically, LiteSense leverages a U-Net-style encoder-decoder that forms an RGB-D input by concatenating RGB with upsampled ToF depth, providing explicit metric priors. To address resolution disparity and recover fine details, we introduce the **Patch-wise CNH Spatial Injection (PCSI)** module, which leverages zone-wise histogram measurements via cross-attention to guide high-level feature fusion. Extensively evaluated on NYUv2 and SUN RGB-D, LiteSense consistently outperforms monocular baselines and DELTAR with substantially lower computational cost, and demonstrates promising zero-shot generalization. We further introduce THDR3K, the first indoor RGB-ToF-CNH dataset, where LiteSense achieves real-world accuracy comparable to--and in challenging cases surpassing--Intel RealSense. All the relevant source codes and the collected dataset are available at GitHub.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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RigMo: Unifying Rig and Motion Learning for Generative Animation

Zhang, Hao, Luo, Jiahao, Wan, Bohui, Zhao, Yizhou, Li, Zongrui, Vasilkovsky, Michael, Wang, Chaoyang, Wang, Jian 等 10 位作者

原文摘要

Despite significant progress in 4D generation, rig and motion--the core structural and dynamic components of animation--are typically modeled as separate problems. Existing pipelines rely on ground-truth skeletons and skinning weights for motion generation and treat auto-rigging as an independent process, undermining scalability and interpretability. We present RigMo, a unified generative framework that jointly learns rig and motion directly from raw mesh sequences, without any human-provided rig annotations. RigMo encodes per-vertex deformations into two compact latent spaces: a rig latent that decodes into explicit Gaussian bones and skinning weights, and a motion latent that produces time-varying SE(3) transformations. Together, these outputs define an animatable mesh with explicit structure and coherent motion, enabling feed-forward rig and motion inference for deformable objects. Beyond unified rig-motion discovery, we introduce a Motion-DiT model operating in RigMo's latent space and demonstrate that these structure-aware latents can naturally support downstream motion generation tasks. Experiments on DeformingThings4D, Objaverse-XL, and TrueBones demonstrate that RigMo learns smooth, interpretable, and physically plausible rigs, while achieving superior reconstruction and category-level generalization compared to existing auto-rigging and deformation baselines. RigMo establishes a new paradigm for unified, structure-aware, and scalable dynamic 3D modeling

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception

Wang, Jiahao, Xu, Zikun, Zhang, Yuner, Jiang, Zhongwei, Lu, Chenyang, Yang, Shuocheng, Wang, Yuxuan, Zhong, Jiaru 等 11 位作者

原文摘要

Cooperative 3D perception via Vehicle-to-Everything communication is a promising paradigm for enhancing autonomous driving, offering extended sensing horizons and occlusion resolution. However, the practical deployment of existing methods is hindered at long distances by two critical bottlenecks: the quadratic computational scaling of dense BEV representations and the fragility of feature association mechanisms under significant observation and alignment errors. To overcome these limitations, we introduce Long-SCOPE, a fully sparse framework designed for robust long-distance cooperative 3D perception. Our method features two novel components: a Geometry-guided Query Generation module to accurately detect small, distant objects, and a learnable Context-Aware Association module that robustly matches cooperative queries despite severe positional noise. Experiments on the V2X-Seq and Griffin datasets validate that Long-SCOPE achieves state-of-the-art performance, particularly in challenging 100-150m long-range settings, while maintaining highly competitive computation and communication costs.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Hybrid Token Compression for Vision-Language Models

Zhang, Jusheng, Guo, Xiaoyang, Cai, Kaitong, Lv, Qinhan, Fan, Yijia, Chai, Wenhao, Wang, Jian, Wang, Keze

原文摘要

Vision-language models (VLMs) have transformed multimodal reasoning, but feeding hundreds of visual patch tokens to LLMs incurs quadratic computational costs, straining memory and context windows. Traditional approaches face a trade-off: continuous compression dilutes high-level semantics like object identities, while discrete quantization loses granular details such as textures. We challenge this by introducing **HTC-VLM**, a hybrid framework that disentangles semantics and appearance through dual channels, i.e., a continuous pathway for fine-grained details via ViT patches and a discrete pathway for symbolic anchors using MGVQ quantization projected to four tokens. These are fused into a 580-token hybrid sequence and compressed to one token via a disentanglement attention mask and a `` bottleneck, ensuring efficient, grounded representations.HTC-VLM achieves an average performance retention of **87.2%** across seven benchmarks (GQA, VQAv2, MMBench, MME, POPE, SEED-Bench, ScienceQA-Image), outperforming the leading continuous baseline at **81.0%** with a 580-to-1 compression ratio. Attention analyses show the compressed token prioritizes the discrete anchor, validating its semantic guidance. Our work demonstrates that a minimalist hybrid can resolve the efficiency-fidelity dilemma, advancing scalable VLMs.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks

Yang, Zhichao, Wang, Jianjie, Zhang, Zhixianhe, Xie, Pangu, Sheng, Xiangfei, Chen, Pengfei, Li, Leida

原文摘要

Image aesthetic assessment (IAA) has extensive applications in content creation, album management, and recommendation systems, etc. In such applications, it is commonly needed to pick out the most aesthetically pleasing image from a series of images with subtle aesthetic variations, a topic we refer to as fine-grained IAA. Unfortunately, state-of-the-art IAA models are typically designed for coarse-grained evaluation, where images with notable aesthetic differences are evaluated independently on an absolute scale. These models are inherently limited in discriminating fine-grained aesthetic differences. To address the dilemma, we contribute FGAesthetics, a fine-grained IAA database with 32,217 images organized into 10,028 series, which are sourced from diverse categories including Natural, AIGC, and Cropping. Annotations are collected via pairwise comparisons within each series. We also devise Series Refinement and Rank Calibration to ensure the reliability of data and labels. Based on FGAesthetics, we further propose FGAesQ, a novel IAA framework that learns discriminative aesthetic scores from relative ranks through Difference-preserved Tokenization (DiffToken), Comparative Text-assisted Alignment (CTAlign), and Rank-aware Regression (RankReg). FGAesQ enables accurate aesthetic assessment in fine-grained scenarios while still maintains competitive performance in coarse-grained evaluation. Extensive experiments and comparisons demonstrate the superiority of the proposed method.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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IF-Prune: Information-Flow Guided Token Pruning for Efficient Vision-Language Models

Sun, Guohao, Wang, Yufei, Ma, Sizhuo, Xie, Yuege, Cheng, Yuting, Tao, Zhiqiang, Wang, Jian

原文摘要

Vision-language models (VLMs) with dynamic resolution vision encoders achieve strong performance, but face significant efficiency challenges due to long input sequences. A common approach is to assess the importance of tokens and prune those that are less informative. Recent methods utilizing a small VLM to provide the importance map of visual tokens have outperformed existing rule-based and similarity-driven pruning approaches, particularly under high pruning ratios. However, directly using the small VLM remains unreliable, as it utilizes the aggregated visual attention weights as importance score, which can lead to noisy guidance if the generated tokens are incorrect.To address this, we invert the approach by having it detect non-informative visual tokens according to the user's input query. By adding a variational information bottleneck in the small VLM, we can approximate the entropy of each visual token as pruning guidance. Such a posteriori-guided pruning method allows the large VLM to retain its reasoning capacity with improved efficiency. Extensive experiments on eight benchmarks demonstrate the effectiveness of our approach. With only 5% of visual tokens retained, the large VLM preserves 95% of its original performance, outperforming the state of the art by 8%.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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