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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bbefaf149402043d874cf4394bcdae80CVF · CVPR 2026

Dynamic-Static Decomposition for Novel View Synthesis of Dynamic Scenes with Spiking Neurons

Dai, Lingyun, Chen, Zehao, Liu, Yan, Gu, Shi, Lin, Peng, Ma, De, Tang, Huajin, Zheng, Qian 等 9 位作者

原文摘要

Novel view synthesis for dynamic scenes remains challenging due to complex motion variations. Recent methods represent dynamic and static regions with separate Gaussians to improve efficiency and accuracy, but inaccurate assignment of static and dynamic Gaussian primitives still limits performance. We identify two key issues, namely inaccurate mask priors and improper tag representations, which lead to boundary artifacts, loss of fine-grained motion details, and overfitting on input views, resulting in degraded side-view synthesis. To address these problems, we propose a spatio-temporally fine-grained mask field and a discontinuous dynamic-static tagging field to achieve accurate assignment of dynamic and static Gaussian primitives, enabling high-quality novel view synthesis, especially in fine-grained motions, motion boundary regions, and side viewpoints. Experiments show that our method achieves state-of-the-art rendering quality and real-time performance.

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:bbf03c89430a2730758a3a03134126f8CVF · CVPR 2026

Spectrally Distilled Representations Aligned with Instruction-Augmented LLMs for Satellite Imagery

Do, Minh Kha, Xiang, Wei, Han, Kang, Wu, Di, Phan, Khoa, Chen, Yi-Ping Phoebe, Liu, Gaowen, Kompella, Ramana Rao

原文摘要

Vision-language foundation models (VLFMs) promise zero-shot and retrieval understanding for Earth observation. While operational satellite systems often lack full multi-spectral coverage, making RGB-only inference highly desirable for scalable deployment, the adoption of VLFMs for satellite imagery remains hindered by two factors: (1) multi-spectral inputs are informative but difficult to exploit consistently due to band redundancy and misalignment; and (2) CLIP-style text encoders limit semantic expressiveness and weaken fine-grained alignment. We present SATtxt, a VLFM that operates with RGB inputs at inference while retaining spectral cues learned during training and producing rich cross-modal representations. Our framework comprises two stages. First, Spectral Representation Distillation transfers spectral priors from a frozen multi-spectral teacher to an RGB student via a lightweight projector. Second, Spectrally Grounded Alignment with Instruction-Augmented LLMs bridges the distilled visual space and an expressive LLM embedding space. Across satellite benchmarks, SATtxt improves zero-shot classification on average by 4.2%, retrieval by 5.9%, linear probing by 2.7%, and open-vocabulary segmentation by 2.8% over baselines, showing an efficient path toward spectrum-aware vision-language learning for Earth observation. Project page: https://ikhado.github.io/sattxt/

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:bbf0e9d8f13b56709e211ae4f0186d1fCVF · CVPR 2026

MORE-STEM: Long-Short MemOry REcall and Spatio-TEmporal Consistency Model for Query-Driven 3D/4D Point Cloud Segmentation

Li, Chade, Feng, Haida, Zhang, Pengju, Wu, Yihong

原文摘要

Current query-driven 3D understanding methods are constrained to static point clouds, limiting their ability to reason about dynamic scenes. To bridge this gap, we propose MORE-STEM, a unified framework for Long-Short MemOry REcall and Spatio-TEmporal Consistency Model in Query-Driven 3D/4D Point Cloud Segmentation. The framework first introduces a Cross-Frame Text-Visual Alignment module that establishes fine-grained, time-aware correspondences between linguistic queries and dynamic 3D features. Building on this, a Spatio-Temporal Consistency Model module enforces motion-aware coherence across consecutive frames, ensuring stable and temporally consistent segmentation. A Long-Short Memory Recall module further enhances cross-scene reasoning through hierarchical memory that balances long-term semantic recall and short-term adaptation. We also construct a new outdoor benchmark for both 3D and 4D instruction segmentation with temporally aligned, motion-centric text annotations. Experiments demonstrate that MORE-STEM achieves state-of-the-art performance across multiple 3D and 4D understanding tasks.

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:bc00d8850bbc884ff73c61cc441de6aeCVF · CVPR 2026

OS-Fed: One Snapshot Is All You Need

Qian, Xuwei, Zhang, Jinghui, Tan, Yuchuan, Huang, Wenbo, Wu, Zhen, Zhou, Shen, Gao, LiSha, Ding, Ding 等 9 位作者

原文摘要

Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning. Unfortunately, directly compressing model updates often leads to sub-optimal convergence due to information loss, while increasing local computation can cause model divergence. Hence, this paper proposes a drastically different approach that adheres to the maxim that "a picture is worth a thousand words". We observe that the entire gradient information from local training can be effectively reconstructed from a compact, image-like representation. Based on this observation, we propose a novel approach, OS-Fed, which performs One-Shot Federated Learning by transmitting only a single, compact snapshot (comprising an image and a set of learnable labels) per round. To realize this approach, OS-Fed presents new snapshot synthesis techniques to (1) target the accumulated update of a trajectory segment to tackle gradient noise, (2) design a multi-grid snapshot that decouples conflicting gradient directions, and (3) incorporate error compensation to maintain training stability under extreme compression. Extensive experiments on CV and NLP benchmarks show that OS-Fed reduces communication costs by 1.5-16xcompared to state-of-the-art algorithms , resulting in 18-45% faster convergence.

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:bc11431e4b09b0968895ac6eaef61d48CVF · CVPR 2026

Gaussian-Mixture Latent Flow for Stochastic 3D Human Motion Prediction

Ma, Yue, Li, Frederick W. B., Liang, Xiaohui

原文摘要

Stochastic human motion prediction aims to forecast future motion distributions. Although recent studies have achieved strong performance in terms of accuracy and diversity, they often overlook plausibility (e.g., resulting in physically unrealistic predictions) and uncertainty quantification, which is essential for real-world applications and downstream tasks. To address these issues, we propose a latent flow-based model equipped with a data-driven Gaussian mixture prior that more effectively disentangles diverse human behaviors than conventional single-modal priors. This prior is derived from patterns in the training data without requiring additional annotations. Furthermore, the fully invertible nature of our model enables natural uncertainty quantification through tractable likelihood computation. Experiments on the Human3.6M and AMASS datasets demonstrate that our approach achieves state-of-the-art performance in both accuracy and plausibility, while also providing reliable uncertainty estimates.

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:bc11fe3caeebcf5346224ff48c3f21c3CVF · CVPR 2026

Cross-View Splatter: Feed-Forward View Synthesis with Georeferenced Images

Turkulainen, Matias, Krishnan, Akshay, Aleotti, Filippo, Sayed, Mohamed, Garcia-Hernando, Guillermo, Kannala, Juho, Solin, Arno, Brostow, Gabriel 等 9 位作者

原文摘要

We present Cross-View Splatter, a feed-forward method that predicts pixel-aligned Gaussian splats for outdoor scenes captured at ground level and by satellite. Faithful reconstructions require good camera coverage, but ground imagery is time-consuming and hard to capture at scale for large outdoor scenes. Fortunately, satellite imagery can provide a global geometric prior that is easy to access via public APIs. Cross-View Splatter fuses orthorectified satellite views with GPS-tagged ground photos to predict Gaussian splats in a unified 3D coordinate frame. By aligning ground and bird's-eye feature representations, our model improves scene coverage and novel-view synthesis, compared to ground imagery alone. We train on curated georeferenced datasets and paired satellite-terrain data, mined from open mapping services. We evaluate our method on a new benchmark for novel-view synthesis with georeferenced imagery, allowing comparison to prior state-of-the-art methods. Our code and data preparation will be available at https://nianticspatial.github.io/cross-view-splatter/.

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:bc12cefba2741d0a9560c7c8871af0f8CVF · CVPR 2026

Refer-Agent: A Collaborative Multi-Agent System with Reasoning and Reflection for Referring Video Object Segmentation

Jiang, Haichao, Liang, Tianming, Zheng, Wei-Shi, Hu, Jian-Fang

原文摘要

Referring Video Object Segmentation (RVOS) aims to segment objects in videos based on textual queries. Current methods mainly rely on large-scale supervised fine-tuning (SFT) of Multi-modal Large Language Models (MLLMs). However, this paradigm suffers from heavy data dependence and limited scalability against the rapid evolution of MLLMs. Although recent zero-shot approaches offer a flexible alternative, their performance remains significantly behind SFT-based methods, due to the straightforward workflow designs. To address these limitations, we propose Refer-Agent, a collaborative multi-agent system with alternating reasoning-reflection mechanisms. This system decomposes RVOS into step-by-step reasoning process. During reasoning, we introduce a Coarse-to-Fine frame selection strategy to ensure the frame diversity and textual relevance, along with a Dynamic Focus Layout that adaptively adjusts the agent's visual focus.Furthermore, we propose a Chain-of-Reflection mechanism, which employs a Questioner-Responder pair to generate a self-reflection chain, enabling the system to verify intermediate results and generates feedback for next-round reasoning refinement. Extensive experiments on five challenging benchmarks demonstrate that Refer-Agent significantly outperforms state-of-the-art methods, including both SFT-based models and zero-shot approaches.Moreover, Refer-Agent is flexible and enables fast integration of new MLLMs without any additional fine-tuning costs. Code will be released.

cs.CV · cs.CL · cs.MA

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:bc154acb702bc0a9236fea2821592a6dCVF · CVPR 2026

Stay in your Lane: Role Specific Queries with Overlap Suppression Loss for Dense Video Captioning

Baek, Seung Hyup, Lee, Jimin, Lee, Hyeongkeun, Cho, Jae Won

原文摘要

Dense Video Captioning (DVC) is a challenging multimodal task that involves temporally localizing multiple events within a video and describing them with natural language. While query-based frameworks enable the simultaneous, end-to-end processing of localization and captioning, their reliance on shared queries often leads to significant multi-task interference between the two tasks, as well as temporal redundancy in localization. In this paper, we propose utilizing role-specific queries that separate localization and captioning into independent components, allowing each to exclusively learn its role. We then employ contrastive alignment to enforce semantic consistency between the corresponding outputs, ensuring coherent behavior across the separated queries. Furthermore, we design a novel suppression mechanism in which mutual temporal overlaps across queries are penalized to tackle temporal redundancy, supervising the model to learn distinct, non-overlapping event regions for more precise localization. Additionally, we introduce a lightweight module that captures core event concepts to further enhance semantic richness in captions through concept-level representations. We demonstrate the effectiveness of our method through extensive experiments on major DVC benchmarks YouCook2 and ActivityNet Captions.

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:bc2de789fe2d5fd2929f1060a261e87bCVF · CVPR 2026

DREAM: Document Recognition with Explicit Adaptive Memory

Zhao, Tianqi, Wu, Di, Peng, Liangrui, Huang, Yifan, Zhao, Kemeng, Li, Shuo, Li, Zhiyu, Wang, Yizhu 等 10 位作者

原文摘要

Large multimodal models (LMMs) have shown promising performance for various document recognition tasks. However, LMMs adopt implicit modeling, and the parameters lack interpretability. Inspired by recent advances in human memory and learning research, we propose an explicit multiscale prototype memory that augments document recognition models, explicitly modeling recurrent layout and stylistic patterns across different spatial resolutions. A Memory Retrieval Mechanism enables local regions to sparsely attend to a few prototypes (e.g., image borders, tilted text); the retrieved compositional factors are concatenated with visual features and passed to the decoder, providing explicit region-wise structural context. Prototype memory consolidation updates and stabilizes prototypes via attention-weighted exponential moving average (EMA) strategy, while sparsity and anti-collapse regularization promote selective activation. We further adopt hierarchical memory for multi-resolution encoding. The proposed DREAM module is a plug-and-play component, allowing seamless integration into various encoder-decoder architectures. We validate on two tasks including document recognition on public datasets and the self-built DreamDoc dataset, and handwriting recognition on the SCUT-HCCDoc and SCUT-EPT datasets. Experimental results show that the proposed method is effective.

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:bc36e6fe652fbef00e8ffb3c04d37244CVF · CVPR 2026

OS-Oracle: A Comprehensive Framework for Cross-Platform GUI Critic Models

Wu, Zhenyu, Xie, Jingjing, Li, Zehao, Yang, Bowen, Sun, Qiushi, Liu, Zhaoyang, Liu, Zhoumianze, Qiao, Yu 等 11 位作者

原文摘要

The deployment of autonomous agents in Graphical User Interface (GUI) environments confronts significant challenges, notably error accumulation in long-horizon tasks and the severe consequences of irreversible operations. While critic models that provide real-time action assessment offer a promising solution, their effectiveness is hindered by the lack of diverse, high-quality GUI feedback data and public critic benchmarks for computer use.To bridge these gaps,we introduce OS-Oracle that makes three core contributions:(1) a scalable data pipeline for synthesizing cross-platform GUI critic data;(2) a two-stage training paradigm combining supervised fine-tuning (SFT) and consistency-preserving group relative policy optimization (CP-GRPO); (3) OS-Critic Bench, a holistic benchmark for evaluating critic model performance across Mobile, Web, and Desktop platforms.Leveraging this framework, we curate a high-quality dataset containing 310k critic samples. The resulting critic model, OS-Oracle-7B, achieves impressive performance,and further reduces error rates, which improves the capability of GUI agents in dynamic environments. All codes, data and checkpoints will be made public.

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:bc37347e7bd0aa570461344a89ceeee5CVF · CVPR 2026

ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence On Mobile Devices

Kong, Dezhi, Feng, Zhengzhao, Liang, Qiliang, Wang, Hao, Sun, Haofei, Yang, Changpeng, Li, Yang, Zhou, Peng 等 15 位作者

原文摘要

Multimodal large language models (MLLMs) have made significant progress in mobile agent development, yet their capabilities are predominantly confined to a reactive paradigm, where they merely execute explicit user commands. The emerging paradigm of proactive intelligence, where agents autonomously anticipate needs and initiate actions, represents the next frontier for mobile agents. However, its development is critically bottlenecked by the lack of benchmarks that can address real-world complexity and enable objective, executable evaluation. To overcome these challenges, we introduce ProactiveMobile, a comprehensive benchmark designed to systematically advance research in this domain. ProactiveMobile formalizes the proactive task as inferring latent user intent across four dimensions of on-device contextual signals and generating an executable function sequence from a comprehensive function pool of 63 APIs. The benchmark features over 3,660 instances of 14 scenarios that embrace real-world complexity through multi-answer annotations. To ensure quality, a team of 30 experts conducts a final audit of the benchmark, verifying factual accuracy, logical consistency, and action feasibility, and correcting non-compliant entries. Extensive experiments demonstrate that our fine-tuned Qwen2.5-VL-7B-Instruct achieves a success rate of 20.82%, outperforming o1 (17.02%) and GPT-5 (11.37%). This result indicates that proactivity is a critical competency widely lacking in current MLLMs, yet it is learnable, emphasizing the importance of the proposed benchmark for proactivity evaluation. Data and code are available at https://github.com/xiaomi-research/proactive-mobile.

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:bc4b32bf3f8a74e1835539005d3f813dCVF · CVPR 2026

NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos

Yang, Yuxue, Fan, Lue, Shi, Ziqi, Peng, Junran, Wang, Feng, Zhang, Zhaoxiang

原文摘要

In this paper, we propose NeoVerse, a versatile 4D world model that is capable of 4D reconstruction, novel-trajectory video generation, and rich downstream applications. We first identify a common limitation of scalability in current 4D world modeling methods, caused either by expensive and specialized multi-view 4D data or by cumbersome training pre-processing. In contrast, our NeoVerse is built upon a core philosophy that makes the full pipeline scalable to diverse in-the-wild monocular videos. Specifically, NeoVerse features pose-free feed-forward 4D reconstruction, online monocular degradation pattern simulation, and other well-aligned techniques. These designs empower NeoVerse with versatility and generalization to various domains. Meanwhile, NeoVerse achieves state-of-the-art performance in standard reconstruction and generation benchmarks.

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:bc4cade451e4a84189d7dd8629729a7cCVF · CVPR 2026

PerpetualWonder: Long-horizon Action-conditioned 4D Scene Generation

Zhan, Jiahao, Li, Zizhang, Yu, Hong-Xing, Wu, Jiajun

原文摘要

We introduce PerpetualWonder, a hybrid generative simulator that enables long-horizon, action-conditioned 4D scene generation from a single image. Current works fail at this task because their physical state is decoupled from their visual representation, which prevents generative refinements to update the underlying physics for subsequent interactions. PerpetualWonder solves this by introducing the first true closed-loop system. It features a novel unified representation that creates a bidirectional link between the physical state and visual primitives, allowing generative refinements to correct both the dynamics and appearance. It also introduces a robust update mechanism that gathers supervision from multiple viewpoints to resolve optimization ambiguity. Experiments demonstrate that from a single image, PerpetualWonder can successfully simulate complex, multi-step interactions from long-horizon actions, maintaining physical plausibility and visual consistency.

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:bc52593b659478f55f0f54e006f92815CVF · CVPR 2026

Suppressing Non-Semantic Noise in Masked Image Modeling Representations

Hjelkrem-Tan, Martine, Aasan, Marius, Chakraborty, Rwiddhi, Arteaga, Gabriel Y., Choi, Changkyu, Rivera, Adín Ramírez

原文摘要

Masked Image Modeling (MIM) has become a ubiquitous self-supervised vision paradigm. In this work, we show that MIM objectives cause the learned representations to retain non-semantic information, which ultimately hurts performance during inference. We introduce a model-agnostic score for semantic invariance using Principal Component Analysis (PCA) on real and synthetic non-semantic images. Based on this score, we propose a simple method, Semantically Orthogonal Artifact Projection (SOAP), to directly suppress non-semantic information in patch representations, leading to consistent improvements in zero-shot performance across various MIM-based models. SOAP is a post-hoc suppression method, requires zero training, and can be attached to any model as a single linear head.

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:bc68c86da081c12cad512ecc02ccf393CVF · CVPR 2026

Scaling Dense Event-Stream Pretraining from Visual Foundation Models

Chen, Zhiwen, Hou, Junhui, Zhu, Zhiyu, Wu, Jinjian, Shi, Guangming

原文摘要

Learning versatile, fine-grained representations from irregular event streams is pivotal yet nontrivial, primarily due to the heavy annotation that hinders scalability in dataset size, semantic richness, and application scope. To mitigate this dilemma, we launch a novel self-supervised pretraining method that distills visual foundation models (VFMs) to push the boundaries of event representation at scale. Specifically, we curate an extensive synchronized image-event collection to amplify cross-modal alignment. Nevertheless, due to inherent mismatches in sparsity and granularity between image-event domains, existing distillation paradigms are prone to semantic collapse in event representations, particularly at high resolutions. To bridge this gap, we propose to extend the alignment objective to semantic structures provided off-the-shelf by VFMs, indicating a broader receptive field and stronger supervision. The key ingredient of our method is a structure-aware distillation loss that grounds higher-quality image-event correspondences for alignment, optimizing dense event representations. Extensive experiments demonstrate that our approach takes a great leap in downstream benchmarks, significantly surpassing traditional methods and existing pretraining techniques. This breakthrough manifests in enhanced generalization, superior data efficiency and elevated transferability. The source code will be available.

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:bc7517d29e919d18aca83116ce5543ccCVF · CVPR 2026

ViT$^3$: Unlocking Test-Time Training in Vision

Han, Dongchen, Li, Yining, Li, Tianyu, Cao, Zixuan, Wang, Ziming, Song, Jun, Cheng, Yu, Zheng, Bo 等 9 位作者

原文摘要

Test-Time Training (TTT) has recently emerged as a promising direction for efficient sequence modeling. TTT reformulates attention operation as an online learning problem, constructing a compact inner model from key-value pairs at test time. This reformulation opens a rich and flexible design space while achieving linear computational complexity. However, crafting a powerful visual TTT design remains challenging: fundamental choices for the inner module and inner training lack comprehensive understanding and practical guidelines. To bridge this critical gap, in this paper, we present a systematic empirical study of TTT designs for visual sequence modeling. From a series of experiments and analyses, we distill six practical insights that establish design principles for effective visual TTT and illuminate paths for future improvement. These findings culminate in the Vision Test-Time Training (ViT^3) model, a pure TTT architecture that achieves linear complexity and parallelizable computation. We evaluate ViT^3 across diverse visual tasks, including image classification, image generation, object detection, and semantic segmentation. Results show that ViT^3 consistently matches or outperforms advanced linear-complexity models (e.g., Mamba and linear attention variants) and effectively narrows the gap to highly optimized vision Transformers. We hope this study and the ViT^3 baseline can facilitate future work on visual TTT models. Code: github.com/LeapLabTHU/ViTTT.

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:bc848be20d4a39697e1c6184fa32298eCVF · CVPR 2026

Beyond What's Shared: Recovering Lost Unique Information from Intermediate Layers to Boost Multimodal Geo-Foundation Models

Lee, JangHyeon, Dias, Philipe Ambrozio, Chiang, Yao-Yi, Lunga, Dalton

原文摘要

Learning general-purpose representations of geographic locations has become essential to geospatial tasks such as population estimation and environmental monitoring. To obtain such representations, multimodal geo-foundation models often use contrastive learning (CL) to align satellite imagery with geo-coordinates, implicitly assuming that cross-modal (shared) information suffices for downstream tasks. However, given the breadth of tasks, task-relevant information may lie beyond the shared space, so retaining modality-specific (unique) features can improve task performance. Prior methods retain unique information through extra training objectives or external models, increasing training complexity. Motivated by the conventional wisdom that earlier layers capture general input features while later layers become task-specific, we hypothesize that intermediate layers in CL models retain more modality-specific structure than the alignment-optimized final layer. Through a trifecta layerwise analysis of modality gap, representation similarity, and mutual information, we validate this trend and find that fusing intermediate (more unique) and final (more shared) representations yields consistent gains on diverse geospatial tasks. Our findings reveal underutilized information diversity in CL models and show that simple layerwise fusion is an efficient path to richer geo-embeddings.

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:bc8d4d752a062c7e61bde9e800a5b9c3CVF · CVPR 2026

ERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specialization

Cheng, Anzhe, Duan, Shukai, Li, Shixuan, Yin, Chenzhong, Cheng, Mingxi, Ping, Heng, Chattopadhyay, Tamoghna, Thomopoulos, Sophia I. 等 11 位作者

原文摘要

Mixture-of-Experts (MoE) models expand capacity via sparse expert activation, but routing logits can misalign with expert structure (unstable routing, underutilization) and load imbalance can create stragglers. Auxiliary load-balancing losses reduce disparity but often weaken specialization and downstream accuracy. We propose ERMoE, a sparse MoE transformer that reparameterizes each expert in a learned orthonormal eigenbasis and routes with an Eigenbasis Score (cosine similarity between token features and an expert basis) instead of learned gating logits. By tying assignments to each expert's representation space, ERMoE stabilizes utilization, improves interpretability, and removes explicit balancing losses and their gradient interference. ERMoE reaches state-of-the-art results on ImageNet and image-text retrieval (COCO, Flickr30K) with flatter expert loads. A 3D MRI variant (ERMoE-ba) improves brain age prediction by over 7% and yields anatomically interpretable expert specializations.

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:bc8eeba872d98254175847f3a2af0a9cCVF · CVPR 2026

Dynamic Important Example Mining for Reinforcement Finetuning

Tan, Haoru, Wu, Sitong, Chen, Yanfeng, Zhao, Shizhen, Sun, Yang-Tian, Liu, Tianjia, Chang, Chirui, Zhang, Shaofeng 等 12 位作者

原文摘要

Reinforcement fine-tuning (RFT) is increasingly used to strengthen the reasoning abilities of large models, yet its effectiveness is bound by how training data are selected and used. Most data-centric RFT methods rely on static or heuristic sample selection, implicitly assuming a sample's value is fixed over training. This overlooks the non-stationary dynamics of policy learning and can lead to suboptimal updates. We propose Dynamic Important Example Mining (DIEM), a principled and fully automated framework that makes data utilization adaptive throughout RFT. DIEM integrates two components into each optimization step: (i) a gradient-alignment importance estimator that efficiently approximates each sample's marginal contribution to policy improvement; and (ii) a constrained batch reweighting scheme that maximizes aggregate utility while preserving the update's gradient magnitude to stabilize optimization. Across several reasoning benchmarks, DIEM consistently outperforms strong static and dynamic baselines. The code will be released via https://github.com/hrtan/DIEM.

cs.CV

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

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AutoRegressive Generation with B-rep Holistic Token Sequence Representation

Li, Jiahao, Bai, Yunpeng, Dai, Yongkang, Guo, Hao, Gan, Hongping, Shi, Yilei

原文摘要

Previous representation and generation approaches for the B-rep relied on graph-based representations that disentangle geometric and topological features through decoupled computational pipelines, thereby precluding the application of sequence-based generative frameworks, such as transformer architectures that have demonstrated remarkable performance. In this paper, we propose BrepARG, the first attempt to encode B-rep's geometry and topology into a holistic token sequence representation, enabling sequence-based B-rep generation with an autoregressive architecture. Specifically, BrepARG encodes B-rep into 3 types of tokens: geometry and position tokens representing geometric features, and face index tokens representing topology. Then the holistic token sequence is constructed hierarchically, starting with constructing the geometry blocks (i.e., faces and edges) using the above tokens, followed by geometry block sequencing. Finally, we assemble the holistic sequence representation for the entire B-rep. We also construct a transformer-based autoregressive model that learns the distribution over holistic token sequences via next-token prediction, using a multi-layer decoder-only architecture with causal masking. Experiments demonstrate that BrepARG achieves state-of-the-art (SOTA) performance. BrepARG validates the feasibility of representing B-rep as holistic token sequences, opening new directions for B-rep generation.

cs.CV

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

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StructXLIP: Enhancing Vision-language Models with Multimodal Structural Cues

Ruan, Zanxi, Gao, Songqun, Kong, Qiuyu, Wang, Yiming, Cristani, Marco

原文摘要

Edge-based representations are fundamental cues for visual understanding, a principle rooted in early vision research and still central today. We extend this principle to vision-language alignment, showing that isolating and aligning structural cues across modalities can greatly benefit fine-tuning on long, detail-rich captions, with a specific focus on improving cross modal retrieval. We introduce StructXLIP, a fine-tuning alignment paradigm that extracts edge maps (e.g., Canny), treating them as proxies for the visual structure of an image, and filters the corresponding captions to emphasize structural cues, making them "structure-centric". Fine-tuning augments the standard alignment loss with three structure-centric losses: (i) aligning edge maps with structural text, (ii) matching local edge regions to textual chunks, and (iii) connecting edge maps to color images to prevent representation drift. From a theoretical standpoint, while standard CLIP maximizes the mutual information between visual and textual embeddings, StructXLIP additionally maximizes the mutual information between multimodal structural representations. This auxiliary optimization is intrinsically harder, guiding the model toward more robust and semantically stable minima, enhancing vision-language alignment. Beyond outperforming current competitors on cross-modal retrieval on both general and specialized domains, our method serves as a general boosting recipe that can be integrated into future approaches in a plug-and-play manner. Code and pretrained models are available at: https://github.com/intelligolabs/StructXLIP.

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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Tea-Adapter: Teacher Adapter for Efficient Conditional Generation

Zhang, Yinhan, Ma, Yue, Yi, Fangqiu, Qi, Chenyang, Zhang, Chi, Feng, Kunyu, Wang, Zeyu

原文摘要

We propose Tea-Adapter, a plug-and-play adapter designed to efficiently integrate conditional knowledge from a smaller teacher model into a larger student video diffusion model. Existing controllable video DiT methods face critical challenges: full fine-tuning of billion-parameter models is extremely expensive. At the same time, ControlNets introduce substantial parameter overhead and exhibit limited flexibility for novel multi-condition compositions. To overcome these issues, Tea-Adapter introduces a novel reverse distillation method that enables large video diffusion models to inherit precise control capabilities from smaller, efficiently tuned teacher diffusion models, eliminating the need for full fine-tuning. Moreover, recognizing the intrinsic relationships between different conditions, we replace the cascaded ControlNet design with a Mixture of Condition Experts (MCE) layer. This structure dynamically routes diverse conditional inputs within a unified architecture, supporting both single-condition control and multiple condition combinations without additional training cost. To achieve cross-scale knowledge transfer, we further develop a Feature Propagation Module to ensure efficient and temporally consistent feature propagation across video frames. Experiments demonstrate that Tea-Adapter enables high-fidelity, multi-condition video synthesis, making advanced, controllable video generation feasible on low-resource hardware.

cs.CV

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

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Generative Modeling of Weights: Generalization or Memorization?

Zeng, Boya, Yin, Yida, Xu, Zhiqiu, Liu, Zhuang

原文摘要

Generative models, with their success in image and video generation, have recently been explored for synthesizing effective neural network weights. These approaches take trained neural network checkpoints as training data, and aim to generate high-performing neural network weights during inference. In this work, we examine four representative, well-known methods in this emerging area on their ability to generate novel model weights, i.e., weights that are different from the checkpoints seen during training. Contrary to claims in prior work, we find that these methods synthesize weights largely by memorization: they produce either replicas, or at best simple interpolations, of the training checkpoints. Current methods fail to outperform simple baselines, such as adding noise to the weights or taking a simple weight ensemble, in obtaining different and simultaneously high-performing models. Our further results suggest that the memorization potentially resulted from limited data, overparameterized models, and the underuse of structural priors specific to weight data. Our findings highlight the need for more careful design and evaluation of generative models in new domains.

cs.CV

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

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PhotoFramer: Multi-modal Image Composition Instruction

You, Zhiyuan, Wang, Ke, Zhang, He, Cai, Xin, Gu, Jinjin, Xue, Tianfan, Dong, Chao, Zhang, Zhoutong

原文摘要

Composition matters during the photo-taking process, yet many casual users struggle to frame well-composed images. To provide composition guidance, we introduce PhotoFramer, a multi-modal composition instruction framework. Given a poorly composed image, PhotoFramer first describes how to improve the composition in natural language and then generates a well-composed example image. To train such a model, we curate a large-scale dataset. Inspired by how humans take photos, we organize composition guidance into a hierarchy of sub-tasks: shift, zoom-in, and view-change tasks. Shift and zoom-in data are sampled from existing cropping datasets, while view-change data are obtained via a two-stage pipeline. First, we sample pairs with varying viewpoints from multi-view datasets, and train a degradation model to transform well-composed photos into poorly composed ones. Second, we apply this degradation model to expert-taken photos to synthesize poor images to form training pairs. Using this dataset, we finetune a model that jointly processes and generates both text and images, enabling actionable textual guidance with illustrative examples. Extensive experiments demonstrate that textual instructions effectively steer image composition, and coupling them with exemplars yields consistent improvements over exemplar-only baselines. PhotoFramer offers a practical step toward composition assistants that make expert photographic priors accessible to everyday users.

cs.CV

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

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CoIn3D: Revisiting Configuration-Invariant Multi-Camera 3D Object Detection

Kuang, Zhaonian, Ding, Rui, Wang, Haotian, Zheng, Xinhu, Yang, Meng, Hua, Gang

原文摘要

Multi-camera 3D object detection (MC3D) has attracted increasing attention with the growing deployment of multi-sensor physical agents, such as robots and autonomous vehicles. However, MC3D models still struggle to generalize to unseen platforms with new multi-camera configurations. Current solutions simply employ a meta-camera for unified representation but lack comprehensive consideration. In this paper, we revisit this issue and identify that the devil lies in spatial prior discrepancies across source and target configurations, including different intrinsics, extrinsics, and array layouts. To address this, we propose CoIn3D, a generalizable MC3D framework that enables strong transferability from source configurations to unseen target ones. CoIn3D explicitly incorporates all identified spatial priors into both feature embedding and image observation through spatial-aware feature modulation (SFM) and camera-aware data augmentation (CDA), respectively. SFM enriches feature space by integrating four spatial representations, such as focal length, ground depth, ground gradient, and Plucker coordinate. CDA improves observation diversity under various configurations via a training-free dynamic novel-view image synthesis scheme. Extensive experiments demonstrate that CoIn3D achieves strong cross-configuration performance on landmark datasets such as NuScenes, Waymo, and Lyft, under three dominant MC3D paradigms represented by BEVDepth, BEVFormer, and PETR.

cs.CV

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

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P-Flow: Prompting Visual Effects Generation

Zhao, Rui, Shou, Mike Zheng

原文摘要

Recent advancements in video generation models have significantly improved their ability to follow text prompts. However, the customization of dynamic visual effects, defined as temporally evolving and appearance-driven visual phenomena like object crushing or explosion, remains underexplored. Prior works on motion customization or control mainly focus on low-level motions of the subject or camera, which can be guided using explicit control signals such as motion trajectories. In contrast, dynamic visual effects involve higher-level semantics that are more naturally suited for control via text prompts. However, it is hard and time-consuming for humans to craft a single prompt that accurately specifies these effects, as they require complex temporal reasoning and iterative refinement over time. To address this challenge, we propose P-Flow, a novel training-free framework for customizing dynamic visual effects in video generation without modifying the underlying model. By leveraging the semantic and temporal reasoning capabilities of vision-language models, P-Flow performs test-time prompt optimization, refining prompts based on the discrepancy between the visual effects of the reference video and the generated output. Through iterative refinement, the prompts evolve to better induce the desired dynamic effect in novel scenes. Experiments demonstrate that P-Flow achieves high-fidelity and diverse visual effect customization and outperforms other models on both text-to-video and image-to-video generation tasks. Code is available at https://github.com/showlab/P-Flow.

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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G$^2$VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning

Hu, Wenbo, Lin, Jingli, Long, Yilin, Ran, Yunlong, Jiang, Lihan, Wang, Yifan, Zhu, Chenming, Xu, Runsen 等 10 位作者

原文摘要

Vision-Language Models (VLMs) still lack robustness in spatial intelligence, demonstrating poor performance on spatial understanding and reasoning tasks. We attribute this gap to the absence of a visual geometry learning process capable of reconstructing 3D space from 2D images. We present G^2VLM, a geometry grounded vision-language model that bridges two fundamental aspects of spatial intelligence: spatial 3D reconstruction and spatial understanding. G^2VLM natively leverages learned 3D visual geometry features to directly predict 3D attributes and enhance spatial reasoning tasks via in-context learning and interleaved reasoning. Our unified design is highly scalable for spatial understanding: it trains on abundant multi-view image and video data, while simultaneously leveraging the benefits of 3D visual priors that are typically only derived from hard-to-collect annotations.Experimental results demonstrate G^2VLM is proficient in both tasks, achieving comparable results to state-of-the-art feed-forward 3D reconstruction models and achieving better or competitive results across spatial understanding and reasoning tasks.By unifying a semantically strong VLM with low-level 3D vision tasks, we hope G^2VLM can serve as a strong baseline for the community and unlock more future applications, such as 3D scene editing.

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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WeMMU: Enhanced Bridging of Vision-Language Models and Diffusion Models via Noisy Query Tokens

Yang, Jian, Yin, Dacheng, He, Xiaoxuan, Li, Yong, Rao, Fengyun, Lyu, Jing, Zhai, Wei, Cao, Yang 等 9 位作者

原文摘要

Recent progress in multimodal large language models (MLLMs) has highlighted the challenge of efficiently bridging pre-trained Vision-Language Models (VLMs) with Diffusion Models. While methods using a fixed number of learnable query tokens offer computational efficiency, they suffer from task generalization collapse, failing to adapt to new tasks that are distant from their pre-training tasks. To overcome this, we propose Noisy Query Tokens, which learn a distributed representation space between the VLM and Diffusion Model via end-to-end optimization, enhancing continual learning. Additionally, we introduce a VAE branch with linear projection to recover fine-grained image details. Experimental results confirm our approach mitigates generalization collapse and enables stable continual learning across diverse tasks.

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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Quantized Residuals to Continuous Prompts for Few-Shot Class Incremental Learning in Vision-Language Models

Sinha, Abhishek Kumar, Dube, Nitant, Biswas, Soma

原文摘要

Few-shot Class-Incremental Learning (FSCIL) requires learning new classes from very limited data while preventing catastrophic forgetting. Existing methods rely mainly on visual features and are prone to overfitting, while recent vision-language models (VLMs) offer better transferability but suppress fine-grained information due to contrastive feature decorrelation. Moreover, current FSCIL approaches often use static or fully optimizable prompts, making them either rigid or susceptible to semantic drift in incremental sessions. We introduce QR-Prompt, a residual-driven framework that leverages the visual-textual feature residual of VLMs to recover discriminative fine-grained cues missing from the contrastive space. To ensure stability, we propose Discriminative Subspace Quantization (DSQ), which builds a discrete memory of residual subspaces. To enable plasticity, a Hierarchical Prompt Encoder (HPE) and Prompt Composer (PC) transform these discrete codes into continuous, class-adaptive prompts for novel classes. We derive bounds relating DSQ codebook size to generalization and classification margin, and achieve consistent improvements over state-of-the-art FSCIL methods on CUB200, CIFAR100, and miniImageNet. Our results show that residual-based quantization combined with hierarchical prompt composition yields stable and expressive VLM adaptation for FSCIL.

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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Rel-Zero: Harnessing Patch-Pair Invariance for Robust Zero-Watermarking Against AI Editing

Chen, Pengzhen, Liu, Yanwei, Gu, Xiaoyan, Chen, Xiaojun, Liu, Wu, Wang, Weiping

原文摘要

Recent advancements in diffusion-based image editing pose a significant threat to the authenticity of digital visual content. Traditional embedding-based watermarking methods often introduce perceptible perturbations to maintain robustness, inevitably compromising visual fidelity. Meanwhile, existing zero-watermarking approaches, typically relying on global image features, struggle to withstand sophisticated manipulations. In this work, we uncover a key observation: while individual image patches undergo substantial alterations during AI-based editing, the relational distance between patch pairs remains relatively invariant. Leveraging this property, we propose Relational Zero-Watermarking (Rel-Zero), a novel framework that requires no modification to the original image but derives a unique zero-watermark from these editing-invariant patch relations. By grounding the watermark in intrinsic structural consistency rather than absolute appearance, Rel-Zero provides a non-invasive yet resilient mechanism for content authentication. Extensive experiments demonstrate that Rel-Zero achieves substantially improved robustness across diverse editing models and manipulations compared to prior zero-watermarking approaches.

cs.CV

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

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