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首次观测 · 记录 #1CVF · CVPR 2026:cvf:07a44e02ca5d66418369f685d97134d8CVF · CVPR 2026

OmniFood8K: Single-Image Nutrition Estimation via Hierarchical Frequency-Aligned Fusion

Yu, Dongjian, Min, Weiqing, Jiang, Qian, Lin, Xing, Jin, Xin, Jiang, Shuqiang

原文摘要

Accurate estimation of food nutrition plays a vital role in promoting healthy dietary habits and personalized diet management. Most existing food datasets primarily focus on Western cuisines and lack sufficient coverage of Chinese dishes, which restricts accurate nutritional estimation for Chinese meals. Moreover, many state-of-the-art nutrition prediction methods rely on depth sensors, restricting their applicability in daily scenarios. To address these limitations, we introduce OmniFood8K, a comprehensive multimodal dataset comprising 8,036 food samples, each with detailed nutritional annotations and multi-view images. In addition, to enhance models' capability in nutritional prediction, we construct NutritionSynth-115K, a large-scale synthetic dataset that introduces compositional variations while preserving precise nutritional labels. Moreover, we propose an end-to-end framework for nutritional prediction from a single RGB image. First, we predict a depth map from a single RGB image and design the Scale-Shift Residual Adapter (SSRA) to refine it for global scale consistency and local structural preservation. Second, we propose the Frequency-Aligned Fusion Module (FAFM) to hierarchically align and fuse RGB and depth features in the frequency domain. Finally, we design a Mask-based Prediction Head (MPH) to emphasize key ingredient regions via dynamic channel selection for more accurate prediction. Extensive experiments on multiple datasets demonstrate the superiority of our method over existing approaches. Project homepage: https://yudongjian.github.io/OmniFood8K-food/

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:1d69019701872c36158a8b789751ae22CVF · CVPR 2026

Unlocking Strong Supervision: A Data-Centric Study of General-Purpose Audio Pre-Training Methods

Zhou, Xuanru, Shao, Yiwen, Tseng, Wei-Cheng, Yu, Dong

原文摘要

Current audio pre-training seeks to learn unified representations for broad audio understanding tasks, but it remains fragmented and is fundamentally bottlenecked by its reliance on weak, noisy, and scale-limited labels. Drawing lessons from vision's foundational pre-training blueprint, we argue that the audio field must first establish its own large-scale, strong supervision framework. We introduce a new data-centric pipeline that leverages a high-fidelity captioner to create SOTA-quality captions and the first Unified Tag System (UTS) that bridges speech, music, and environmental sounds. We then conduct a systematic comparative study of different pre-training objectives on these strong source data. Our experiments suggest that data quality and coverage are the primary drivers of performance, while the choice of objective dictates downstream task specialization.

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:3c7ef38da2ca54e2a23edeab687f4f5eCVF · CVPR 2026

MotionEdit: Benchmarking and Learning Motion-Centric Image Editing

Wan, Yixin, Ke, Lei, Yu, Wenhao, Chang, Kai-Wei, Yu, Dong

原文摘要

We introduce **MotionEdit**, a novel dataset for motion-centric image editing--the task of modifying subject actions and interactions while preserving identity, structure, and physical plausibility.Unlike existing image editing datasets that focus on static appearance changes or contain only sparse, low-quality motion edits, MotionEdit provides high-fidelity image pairs depicting realistic motion transformations extracted and verified from continuous videos. This new task is not only scientifically challenging but also practically significant, powering downstream applications such as frame-controlled video synthesis and animation.To evaluate model performance on the novel task, we introduce **MotionEdit-Bench**, a benchmark that challenges models on motion-centric edits and measures model performance with generative, discriminative, and preference-based metrics.Benchmark results reveal that motion editing remains highly challenging for existing state-of-the-art diffusion-based editing models.To address this gap, we propose **MotionNFT** (Motion-guided Negative-aware FineTuning), a post-training framework that computes motion alignment rewards based on how well the motion flow between input and model-edited images matches the ground-truth motion, guiding models toward accurate motion transformations.Extensive experiments on FLUX.1 Kontext and Qwen-Image-Edit show that MotionNFT consistently improves editing quality and motion fidelity of both base models on the motion editing task without sacrificing general editing ability, demonstrating its effectiveness.

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:7b3e2a0b73359857e60a05df6ab1b04eCVF · CVPR 2026

Stable and Efficient Single-Rollout RL for Multimodal Reasoning

Liu, Rui, Yu, Dian, Ke, Lei, Liu, Haolin, Zhou, Yujun, Liang, Zhenwen, Mi, Haitao, Tokekar, Pratap 等 9 位作者

原文摘要

Reinforcement Learning with Verifiable Rewards (RLVR) has become a key paradigm to improve the reasoning capabilities of Multimodal Large Language Models (MLLMs). However, prevalent group-based algorithms such as GRPO require multi-rollout sampling for each prompt. While more efficient single-rollout variants have recently been explored in text-only settings, we find that they suffer from severe instability in multimodal contexts, often leading to training collapse. To address this sample efficiency-stability trade-off, we introduce MSSR (Multimodal Stabilized Single-Rollout), a group-free RLVR framework that achieves both stable optimization and effective multimodal reasoning performance. MSSR achieves this via an entropy-based advantage-shaping mechanism that adaptively regularizes advantage magnitudes, preventing collapse and maintaining training stability. While such mechanisms have been used in group-based RLVR, we show that in the multimodal single-rollout setting they are not merely beneficial but essential for stability. In in-distribution evaluations, MSSR demonstrates superior rollout sample efficiency, achieving similar validation accuracy with half the training steps. When trained for the same number of steps, MSSR's performance surpasses the group-based baseline and shows consistent generalization improvements across five diverse reasoning-intensive benchmarks. Together, these results demonstrate that MSSR enables stable, sample-efficient, and effective RLVR for complex multimodal reasoning 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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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:b903210da48c68ff56ac723b95efd0faCVF · CVPR 2026

Best Segmentation Buddies for Image-Shape Correspondence

Lang, Itai, Lyu, Dongwei, Decatur, Dale, Hanocka, Rana

原文摘要

Finding correspondences is a fundamental and extensively researched problem in computer vision and graphics. In this work, we examine the underexplored task of estimating segmentation-to-segmentation correspondence between images in the wild and untextured 3D shapes. This task is highly challenging due to substantial differences in appearance, geometry, and viewpoint. Our approach bridges the cross-modality gap by linking pixels in the image segment to vertices in the corresponding semantic part of the 3D shape. To achieve this, we first distill deep visual features from a 2D vision model onto the 3D shape surface, allowing for the computation of feature similarity between image pixels and shape vertices. Then, we identify Best Segmentation Buddies, vertices whose most similar image pixel lies within the image segmentation region, enabling the reliable discovery of vertices in semantically corresponding shape parts. Finally, we leverage distilled 3D features from the 2D image segmentation model to segment the shape directly in 3D, bootstrapping the correspondence process. We demonstrate the generality and robustness of our approach across a wide range of image-shape pairs, showcasing accurate and semantically meaningful correspondences. Our project page is at https://threedle.github.io/bsb/.

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:ee60eb62d6400366459c5e124c58621fCVF · CVPR 2026

HySeg: Learning Generative Priors for Structure-Aware Remote Sensing Segmentation

Qiu, Jie, Li, Xin, Yang, Fan, Wang, Yan, Yu, Dong, Wang, Changying, Dai, Linwei, Chen, Yongxiang 等 10 位作者

原文摘要

High-resolution remote sensing imagery exhibits complex spatial regularities where topology, continuity, and region adjacency govern semantic organization. However, existing remote sensing image semantic segmentation (RSISS) networks, being predominantly discriminative, estimate strong posteriors from data while lacking generative priors that encode such structural dependencies. This imbalance leads to fragmented boundaries, texture overfitting, and poor cross-dataset generalization. We address this challenge by reformulating RSISS as posterior inference grounded in generative structural priors, introducing HySeg , a hybrid generative-discriminative segmentation paradigm that learns structure-consistent priors through generative modeling and guides posterior inference for remote sensing segmentation. At its core, the MeanStruct module, a MeanFlow-based generative prior learner, models semantic topology as a continuous stochastic field, while the Prior-to-Affinity Projection (P2A) dynamically transforms this field into topology-aware, class-conditional affinities that guide posterior inference in the Dynamic Affinity-driven Segmentation (DAS) head. Our approach is model-agnostic and seamlessly integrates with diverse backbones, consistently improving structural coherence and generalization. Across four challenging RSISS benchmarks, HySeg achieves state-of-the-art performance and advances remote sensing segmentation from appearance-based perception to structural reasoning. Our code is available at https://github.com/HeryJie/HySeg.

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 ↗