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首次观测 · 记录 #1CVF · CVPR 2026:cvf:29100d722fea6f5b18c1c7adf01a210bCVF · CVPR 2026

PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation

Liu, Yuanzhe, Zhu, Jingyuan, Mo, Yuchen, Li, Gen, Cao, Xu, Jin, Jin, Shen, Yifan, Li, Zhengyuan 等 12 位作者

原文摘要

Recent advancements in vision-language-action (VLA) models have shown promise in robotic manipulation, yet they continue to struggle with long-horizon, multi-step tasks. Existing methods lack internal reasoning mechanisms that can identify task-relevant interaction cues or track progress within a subtask, leading to critical execution errors such as repeated actions, missed steps, and premature termination. To address these challenges, we introduce PALM, a VLA framework that structures policy learning around interaction-centric affordance reasoning and subtask progress cues. PALM distills complementary affordance representations that capture object relevance, contact geometry, spatial placements, and motion dynamics, and serve as task-relevant anchors for visuomotor control. To further stabilize long-horizon execution, PALM predicts continuous within-subtask progress, enabling seamless subtask transitions. Across extensive simulation and real-world experiments, PALM consistently outperforms baselines, achieving a 91.8% success rate on LIBERO-LONG, a 12.5% improvement in average length on CALVIN ABC->D, and a 2ximprovement over real-world baselines across three long-horizon generalization settings.

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:43f8f951e5b7cbf174724ed1ee301e87CVF · CVPR 2026

Physics-Guided Multistep Deformation Reversal for Ancient Bamboo Slip Restoration

Tang, Qianqian, Zhu, Jinchi, Zhou, Xiaolu, Xu, Yongchao

原文摘要

Bamboo slips are essential media for recording ancient East Asian civilizations, but excavated slips often suffer severe deformation due to dehydration and stress effects, creating substantial challenges for restoration. Traditional manual restoration is time-consuming and risks damage, while existing generative models struggle with the complex non-linear deformations in bamboo materials.We propose a novel framework for inverse restoration of deformed bamboo slips that provides a progressive physical deformation modeling with stepwise inverse displacement prediction. Our approach establishes a computable mathematical model of deformation based on wood fiber microstructure and stress-diffusion coupling effects, enabling the forward process to simulate physically plausible deformation trajectories as a deterministic, physics-driven progressive evolution. The inverse process transforms from predicting abstract noise to learning physically meaningful inverse displacement fields that progressively restore deformations.Experimental results show substantial gains in restoration fidelity while preserving delicate textual features, enabling the reliable correction of complex non-linear deformations that defeat traditional techniques. By integrating physical insights into bamboo material behavior with progressive restoration modeling, this work establishes a new paradigm for digital archaeological restoration--one that holds significant potential to transform how deformed cultural relics are reconstructed and studied. Code is available at https://github.com/VillanelleQQ/PGDR-BambooSlips

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:79e35c3cac1ae36ce14e0517e673d57aCVF · CVPR 2026

A Difference-in-Difference Approach to Detecting AI-Generated Images

Qi, Xinyi, Ye, Kai, Shi, Chengchun, Yang, Ying, Zhu, Jin, Zhou, Hongyi

原文摘要

Diffusion models are able to produce AI-generated images that are almost indistinguishable from real ones, raising concerns about their potential misuse and posing substantial challenges for detecting them. Many existing detectors rely on reconstruction error -- the difference between the input image and its reconstructed version -- as the basis for distinguishing real from fake images. However, these detectors become less effective as modern AI-generated images become increasingly similar to real ones. To address this challenge, we propose a novel difference-in-difference method. Instead of directly using the reconstruction error (a first-order difference), we compute the difference in reconstruction error -- a second-order difference -- for variance reduction and improving detection accuracy. Extensive experiments demonstrate that our method achieves strong generalization performance, enabling reliable detection of AI-generated images in the era of generative AI. Code is available at https://github.com/Qixinyi1122-lucky/DID.

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:8bb97b6e93a329e004735eb4acee68baCVF · CVPR 2026

MSRL: Scaling Generative Multimodal Reward Modeling via Multi-Stage Reinforcement Learning

Wang, Chenglong, Huo, Yifu, Gan, Yang, He, Qiaozhi, Meng, Qi, Li, Bei, Wang, Yan, Liu, Junfu 等 11 位作者

原文摘要

Recent advances in multimodal reward modeling have been largely driven by a paradigm shift from discriminative to generative approaches. Building on this progress, recent studies have further employed reinforcement learning with verifiable rewards (RLVR) to enhance multimodal reward models (MRMs). Despite their success, RLVR-based training typically depends on labeled multimodal preference data, which are costly and labor-intensive to obtain, making it difficult to scale the training of MRMs. To overcome this limitation, we propose a Multi-Stage Reinforcement Learning (MSRL) approach, which can achieve scalable reinforcement learning for MRMs with limited multimodal data. MSRL redefines the conventional RLVR-based training paradigm by first learning a generalizable reward reasoning capability from large-scale textual preference data and then progressively transferring this capability to multimodal tasks through caption-based and fully multimodal reinforcement learning stages. Furthermore, we introduce a cross-modal knowledge distillation approach to improve preference generalization within MSRL. Extensive experiments demonstrate that MSRL effectively scales the RLVR-based training of generative MRMs and substantially improves their performance across both visual understanding and visual generation tasks (e.g., 68.5%-74.8% on VLReward Bench, 69.2%-75.4% on GenAI-Bench), without requiring additional multimodal preference annotations.

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 ↗