跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+0新增论文
+0其中 AI 相关
+1已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-08 17:39 · 全站最近入库：2026-11-08 06:02各信源进度 →

统计口径与数据新鲜度

统计 2026-11-07 18:18 — 2026-11-08 18:18（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-08 17:39最近发现新增版本：2026-11-08 06:02库内论文最新更新时间：2026-11-06 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

17,100 已收录论文14,661 AI 相关4,057 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 8 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-08 17:37（北京时间）
首次观测 · 记录 #1CVF · CVPR 2026:cvf:0b02895f0f4a35b76b93537e99c7e200CVF · CVPR 2026

Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds

Yang, Bin, Abdelsamad, Mohamed, Zhang, Miao, Condurache, Alexandru Paul

原文摘要

Recent advances in self-supervised learning (SSL) for point clouds have substantially improved 3D scene understanding without human annotations. Existing approaches emphasize semantic awareness by enforcing feature consistency across augmented views or by masked scene modeling. However, the resulting representations transfer poorly to instance localization, and often require full finetuning for strong performance. Instance awareness is a fundamental component of 3D perception, thus bridging this gap is crucial for progressing toward true 3D foundation models that support all downstream tasks on 3D data. In this work, we introduce PointINS, an instance-oriented self-supervised framework that enriches point cloud representations through geometry-aware learning.PointINS employs an orthogonal offset branch to jointly learn high-level semantic understanding and geometric reasoning, yielding instance awareness. We identify two consistent properties essential for robust instance localization and formulate them as complementary regularization strategies, Offset Distribution Regularization (ODR), which aligns predicted offsets with empirically observed geometric priors, and Spatial Clustering Regularization (SCR), which enforces local coherence by regularizing offsets with pseudo-instance masks. Through extensive experiments across five datasets, PointINS achieves on average +3.5% mAP improvement for indoor instance segmentation and +4.1% PQ gain for outdoor panoptic segmentation, paving the way for scalable 3D foundation models.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:34275f4fb7a1f8e855bd442767e33421CVF · CVPR 2026

DRM: Diffusion-based Reward Model With Step-wise Guidance

Zhang, Jaxon, Yang, Binxin, Yin, Hubery, Li, Chen, LYU, Jing

原文摘要

Current mainstream methods of aligning diffusion models with human preferences typically employ VLM-based reward models.However, these reward models, pre-trained for semantic alignment, struggle to capture the essential perceptual qualities--such as aesthetics, composition, and visual harmony.In this work, we argue that a model capable of high-fidelity generation must possess a profound understanding of these visual attributes.Based on this insight, we introduce the Diffusion-based Reward Model (DRM), a novel paradigm that use the pre-trained diffusion model as a powerful evaluative backbone.A key advantage of the DRM is its unique ability to assess not only the final image but also the noisy intermediate latents at any stage of the generative process. We leverage this step-wise evaluative capacity in two ways.First, we propose Step-wise GRPO, a reinforcement learning algorithm that provides dense, per-step rewards to resolve the imprecise credit assignment problem in GRPO algorithm, leading to more stable and effective alignment.Second, we introduce Step-wise Sampling, a novel inference strategy that employs the DRM as a dynamic guide to evaluate multiple generation paths at each step, steering the process towards higher-quality outcomes.Extensive experiments confirm that our approach significantly enhances the final quality of generated images.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:3afcad01e4e49cdc6f512870891304b9CVF · CVPR 2026

DreamShot: Personalized Storyboard Synthesis with Video Diffusion Prior

Huang, Junjia, Yang, Binbin, Yan, Pengxiang, Liu, Jiyang, Xia, Bin, Wang, Zhao, Wang, Yitong, Lin, Liang 等 9 位作者

原文摘要

Storyboard synthesis plays a crucial role in visual storytelling, aiming to generate coherent shot sequences that visually narrate cinematic events with consistent characters, scenes, and transitions. However, existing approaches are mostly adapted from text-to-image diffusion models, which struggle to maintain long-range temporal coherence, consistent character identities, and narrative flow across multiple shots. In this paper, we introduce DreamShot, a video generative model based storyboard framework that fully exploits powerful video diffusion priors for controllable multi-shot synthesis. DreamShot supports both Text-to-Shot and Reference-to-Shot generation, as well as story continuation conditioned on previous frames, enabling flexible and context-aware storyboard generation. By leveraging the spatial-temporal consistency inherent in video generative models, DreamShot produces visually and semantically coherent sequences with improved narrative fidelity and character continuity. Furthermore, DreamShot incorporates a multi-reference role conditioning module that accepts multiple character reference images and enforces identity alignment via a Role-Attention Consistency Loss, explicitly constraining attention between reference and generated roles. Extensive experiments demonstrate that DreamShot achieves superior scene coherence, role consistency, and generation efficiency compared to state-of-the-art text-to-image storyboard models, establishing a new direction toward controllable video model-driven visual storytelling.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:4e25d23e85c7d843462b2325624b71eeCVF · CVPR 2026

NOVA: Sparse Control, Dense Synthesis for Pair-Free Video Editing

Pan, Tianlin, Dai, Jiayi, Yuan, Chenpu, Lv, Zhengyao, Yang, Binxin, Yin, Hubery, Li, Chen, Lyu, Jing 等 10 位作者

原文摘要

Recent video editing models have achieved impressive results, but most still require large-scale paired datasets. Collecting such naturally aligned pairs at scale remains highly challenging and constitutes a critical bottleneck, especially for local video editing data. Existing workarounds transfer image editing to video through global motion control for pair-free video editing, but such designs struggle with background and temporal consistency. In this paper, we propose NOVA: Sparse Control & Dense Synthesis, a new framework for video editing. Specifically, the sparse branch provides semantic guidance through user-edited keyframes distributed across the video, and the dense branch continuously incorporates motion and texture information from the original video to maintain high fidelity and coherence. Moreover, we introduce a degradation-simulation training strategy that enables the model to learn motion reconstruction and temporal consistency by training on artificially degraded videos, thus eliminating the need for paired data. Our extensive experiments demonstrate that NOVA outperforms existing approaches in edit fidelity, motion preservation, and temporal coherence.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:6f4dc14928c1f853df1171b77943777cCVF · CVPR 2026

FedBPrompt: Federated Domain Generalization Person Re-Identification via Body Distribution Aware Visual Prompts

Xu, Xin, Li, Weilong, Liu, Wei, Huang, Wenke, Yu, Zhixi, Yang, Bin, Liao, Xiaoying, Jiang, Kui

原文摘要

Federated Domain Generalization for Person Re-Identification (FedDG-ReID) aims to learn domain-invariant representations from decentralized data. Although Vision Transformers (ViTs) are widely adopted, their global attention often fails to distinguish pedestrians from high similarity backgrounds or diverse viewpoints---a challenge further amplified by cross-client distribution shifts in FedDG-ReID. To address this, we propose Federated Body Distribution Aware Visual Prompt (FedBPrompt), which introduces learnable visual prompts to explicitly guide Transformer attention toward pedestrian-centric regions. FedBPrompt employs a Body Distribution Aware Visual Prompts Mechanism (BAPM) that divides prompts into two groups: Holistic Full Body Prompts suppress cross-client background noise, while Body Part Alignment Prompts capture fine-grained details robust to pose and viewpoint variations. To mitigate the high communication cost of large Transformer models, we further design a Prompt-based Fine-Tuning Strategy (PFTS) that freezes the ViT backbone and updates only lightweight prompts, significantly reducing communication overhead while maintaining adaptability. Extensive experiments demonstrate that BAPM effectively enhances feature discrimination and cross-domain generalization, while PFTS achieves notable performance gains within only a few aggregation rounds. Moreover, both BAPM and PFTS can be easily integrated into existing ViT-based FedDG-ReID frameworks, making FedBPrompt a flexible and effective solution for federated person re-identification. The code will be released.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:9389c8679934971bbfa1fb0d87963aa4CVF · CVPR 2026

WHU-MARS: A Multispectral Aerial-Ground Benchmark Towards Any-Scenario Person Re-Identification

Zhao, Yuxuan, Zhou, Zhongao, Yang, Bin, Li, He, Liang, Jian, Chen, Jun, Du, Bo, Ye, Mang

原文摘要

Recent person re-identification (ReID) leverages heterogeneous sensing with multiple modalities and viewpoints to improve robustness across diverse conditions. However, most approaches target predefined scenario pairs (e.g., visible-infrared or aerial-ground) and train separate task-specific models. In contrast, real-world systems often query against galleries that cover all scenarios, making such designs inefficient and complex to deploy. To bridge this gap, we introduce Any-Scenario ReID (AS-ReID): given a query from any (modality, viewpoint) scenario, a single model retrieves the same identity from a heterogeneous gallery spanning all scenarios. Progress toward AS-ReID is limited by two factors: (i) the lack of a real-world-aligned benchmark with broad scenario coverage, and (ii) the challenge of learning representations with strong intra-identity cohesion and inter-identity discrimination under diverse scenarios. To this end, we construct WHU-MARS, a multispectral aerial-ground benchmark with 2,337 identities and 434,620 images captured by RGB, near-infrared, and thermal infrared cameras on both ground and UAV platforms. WHU-MARS spans day-night, multiple seasons, and diverse weather, enabling AS-ReID as well as conventional ReID tasks. We further propose the Unified Alignment and Discrimination (UAD) framework. Progressive Center Alignment (ProCA) aggregates multi-view features into modality centers and then aligns them toward identity centers to reduce scenario bias. Global Prototype Discrimination (GPD) contrasts samples against global identity prototypes to enforce large-margin discrimination. Extensive experiments highlight the challenges of WHU-MARS and demonstrate the effectiveness of UAD on AS-ReID.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:b61936d4a553fcf6bd1ac2fbe1862474CVF · CVPR 2026

Similarity-Consistent Likelihood Diffusion enables Hidden Person Detection from Wall Reflections

Zheng, Zhiwen, Zhou, Hao, Qi, Huiyu, Huang, Zhao, Zhang, Guangyuan, Jiang, Shaowei, Tang, Wenwen, Yang, Bin 等 11 位作者

原文摘要

Non-line-of-sight (NLOS) imaging seeks to recover hidden-scene information from indirect light transport beyond the direct line of sight. Existing NLOS methods can be broadly categorized into active and passive approaches. Active methods rely on controlled illumination and time-resolved sensors, but their dependence on specialized and expensive hardware limits practical deployment. Passive methods instead use steady-state or uncontrolled indirect reflections with conventional sensors, but often suffer from weak signals, unstable observations, and insufficient measurement constraints. In this paper, we present the Similarity-Likelihood Diffusion Network (SLD-Net) for corner-camera hidden-person imaging from multi-exposure wall observations. SLD-Net consists of two stages: DeLi-Inversion estimates an initial reconstruction and a pixel-wise precision map to construct a heteroscedastic pseudo-likelihood, and SiCo-Diffusion incorporates this likelihood into deterministic DDIM sampling and fuses it with the diffusion prior via annealed Bayesian precision updating, producing reproducible and measurement-consistent reconstructions. Experiments on the Reflect-Corridor and Reflect-Room datasets demonstrate consistent improvements over generic, physics-inspired, and NLOS-specific baselines. SLD-Net improves PSNR from 13.84 to 15.58 dB and reduces FID from 264.91 to 73.54 on Reflect-Corridor, and improves PSNR from 11.58 to 12.49 dB and reduces FID from 177.05 to 26.89 on Reflect-Room.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:ca852b19178e54603d7efe0afe61e11cCVF · CVPR 2026

Towards Cross-Modal Preservation, Consistency and Alignment for Privacy-Preserving Visible-Infrared Person Re-Identification

Xie, Yudi, Zhou, Zhongao, Yang, Bin, Chen, Zhenghan, Ye, Mang

原文摘要

Privacy-preserving Person Re-Identification (PP-ReID) addresses the core privacy-utility trade-off in Re-ID by retrieving a person across multiple non-overlapping cameras while applying anonymization techniques to protect sensitive information. However, prior PP-ReID studies are confined to single-modality visible scenarios, as 24-hour surveillance systems require robust cross-modal visible-infrared (VI) capabilities. Extending PP-ReID to the cross-modal VI setting is therefore crucial. Accordingly, we introduce a new task: Privacy-Preserving Visible-Infrared Person Re-Identification (PP-VI-ReID). This task presents two severe challenges: 1) Crude anonymization strategies destroy identity-critical information and disrupt cross-modal alignment 2) The anonymization process creates inconsistent distortions across modalities. It disrupts color-based textures in visible images while obscuring thermal contours in infrared images. This inconsistency with modality gap forms a Mixed Gap. To overcome these challenges, we propose a framework, the Precise Privacy-preserving and Alignment Network (PPA) with two components: 1) A Keypoint-Preserving Regularization (KPR) module leverages human pose as a prior to guide structure-aware anonymization, preserving essential body features. 2) A Differential Consistency-guided Modality Alignment (DCMA) treats anonymization perturbations not as varying noise, but as a stable, learnable offset, facilitating robust alignment between raw and anonymized features across modalities. Experiments on SYSU-MM01 and RegDB validate our framework, establishing a strong baseline.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗