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

+1032新增论文
+792其中 AI 相关
+128已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-12 05:56 · 全站最近入库：2026-11-12 06:37各信源进度 →

统计口径与数据新鲜度

统计 2026-11-11 06:48 — 2026-11-12 06:48（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：2026-11-12 05:56最近发现新增版本：2026-11-12 06:37库内论文最新更新时间：2026-11-11 08:00

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。

21,675 已收录论文18,323 AI 相关6,056 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 7 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-12 05:55（北京时间）
首次观测 · 记录 #1CVF · CVPR 2026:cvf:099c7a96ba8f7bb6c6edc9c2ed14040eCVF · CVPR 2026

LangField4D: Learning Identity-Adaptive and Spatio-Temporal Continuous 4D Language Fields for Dynamic Scenes

Xu, Yichao, Miao, Qiaowei, Quan, Jinsheng, Yang, Wei, Li, Zhihui, Luo, Yawei

原文摘要

Constructing a 4D language field that supports open-vocabulary queries is essential for semantic perception and interaction in dynamic environments. Existing 4D Gaussian-based approaches face two major challenges. First, the assumption of a static identity per Gaussian leads to semantic inconsistency, as motion fields warp Gaussians across object boundaries over time, causing oscillating identity assignments. Second, current methods typically model dynamic semantics as a set of discrete, predefined state prototypes, which fail to capture dynamic continuity and delineate fine-grained temporal boundaries. To address these issues, we propose LangField4D, a novel 4D Gaussian framework that jointly models spatio-temporal identity and semantics in a unified and continuous representation. We introduce an Identity-Adaptive Gaussian Grouping module that assigns each Gaussian a learnable adaptation feature to dynamically capture its object affiliation, ensuring consistent semantic tracking across time. Building upon this affiliation structure, we further design a Continuous Spatio-Temporal Semantic Learning mechanism based on a Tetraplane representation, which encodes both time-invariant and time-varying semantics within a continuous latent space. Extensive experiments on dynamic scene benchmarks demonstrate that we achieve state-of-the-art performance on both time-agnostic and time-sensitive open-vocabulary query tasks.

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:158e25f560867273d7f509a5e0d6e603CVF · CVPR 2026

Exploring Adaptive Masked Reconstruction for Self-Supervised Skeleton-Based Action Recognition

Sun, Shengkai, Cheng, Zhiyong, Zhang, Zefan, Dong, Jianfeng, Li, Zhihui, Wang, Meng

原文摘要

Recently, masked skeleton reconstruction models have emerged as strong action representation learners, driving significant progress in self-supervised skeleton-based action recognition. However, existing state-of-the-art methods must predict an exceedingly large number of spatiotemporal patches, significantly prolonging training time. Besides, by treating all spatiotemporal regions equally during reconstruction, these models are distracted from learning the critical motion patterns that underlie action semantics. To address these challenges, we propose Adaptive Masked Reconstruction (AMR), a faster and stronger pre-training framework. We first decouple the decoder from the encoder, enabling flexible prediction of larger spatiotemporal patches and dramatically reducing reconstruction complexity. Given that larger patches contain more complex information, which is challenging to predict and consequently degrades performance, we accordingly introduce an adaptive guidance module. This module identifies regions of high motion informativeness, guiding the model to focus on the most discriminative parts of each patch and alleviating reconstruction difficulty. Experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD datasets demonstrate that AMR not only accelerates pre-training substantially but also improves downstream recognition accuracy, surpassing current state-of-the-art approaches.

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:629414e8c666de4f06dbe0c12c402f2fCVF · CVPR 2026

ParticleGS: Learning Neural Gaussian Particle Dynamics from Videos for Prior-free Physical Motion Extrapolation

Quan, Jinsheng, Miao, Qiaowei, Xu, Yichao, Lin, Zizhuo, Li, Ying, Yang, Wei, Li, Zhihui, Luo, Yawei

原文摘要

The ability to extrapolate dynamic 3D scenes beyond the observed timeframe is fundamental to advancing physical world understanding and predictive modeling. Existing dynamic 3D reconstruction methods have achieved high-fidelity rendering of temporal interpolation, but typically lack physical consistency in predicting the future. To overcome this issue, we propose ParticleGS, a physics-based framework that reformulates dynamic 3D scenes as physically grounded systems. ParticleGS comprises three key components: 1) an encoder that decomposes the scene into static properties and initial dynamic physical fields; 2) an evolver based on Neural Ordinary Differential Equations (Neural ODEs) that learns continuous-time dynamics for motion extrapolation; and 3) a decoder that reconstructs 3D Gaussians from evolved particle states for rendering. Through this design, ParticleGS integrates physical reasoning into dynamic 3D representations, enabling accurate and consistent prediction of the future. Experiments show that ParticleGS achieves state-of-the-art performance in extrapolation while maintaining rendering quality.

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:902919e714014919702cb1b73abd5b32CVF · CVPR 2026

RiskProp: Collision-Anchored Self-Supervised Risk Propagation For Early Accident Anticipation

Zou, Yiyang, Zhao, Tianhao, Xiao, Peilun, Jin, Hongyu, Qi, Longyu, Li, Yuxuan, Liang, Liyin, Qian, Yifeng 等 12 位作者

原文摘要

Accident anticipation aims to predict impending collisions from dashcam videos and trigger early alerts. Existing methods rely on binary supervision with manually annotated "anomaly onset" frames, which are subjective and inconsistent, leading to inaccurate risk estimation. In contrast, we propose RiskProp, a novel collision-anchored self-supervised risk propagation paradigm for early accident anticipation, which removes the need for anomaly onset annotations and leverages only the reliably annotated collision frame. RiskProp models temporal risk evolution through two observation-driven losses. First, since future frames contain more definitive evidence of an impending accident, we introduce a future-frame regularization loss that uses the model's next-frame prediction as a soft target to supervise the current frame, enabling backward propagation of risk signals. Second, inspired by the empirical trend of rising risk before accidents, we design an adaptive monotonic constraint to encourage a non-decreasing progression over time. Experiments on CAP-DATA and Nexar demonstrate that RiskProp achieves state-of-the-art performance and produces smoother, more discriminative risk curves, improving both early anticipation and interpretability.

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:acb91c0aeb24b0724a7e88f8042d17a3CVF · CVPR 2026

SANER: Switchable Adapter with Non-parametric Enhanced Routing for Person De-Reidentification

Liu, Yimin, Pu, Nan, Yang, Fengxiang, Li, Wenjing, Li, Zhihui, Zhong, Zhun

原文摘要

Person De-Reidentification (De-ReID) is an emerging and safety-critical task that aims to selectively forget specific individuals in surveillance systems while preserving the recognition capability for others. Existing methods typically learn both forgetting and retaining objectives within a unified feature space, which leads to conflicting optimization goals and may cause unexpected performance degradation on novel or retained identities. We provide a new perspective to handle De-ReID through feature space decoupling. Although it is a promising solution, discriminating which feature space should be used for the given novel query remain unsolved. To alleviate these challenges, we propose SANER, advancing De-ReID with a Switchable Adapter (SA) and a test-time Non-parametric Enhanced Routing (NER) algorithm. SA decouples the pre-trained feature space into two task-specific subspaces with a forgetting adapter and a retaining adapter. The former suppresses identity-specific semantics for de-identification, while the latter preserves discriminative cues for accurate re-ID. In addition, SA is further enhanced with NER to adaptively analyze optimal feature space routing for the given query at test-time by comparing the query with pre-computed prototypes in the original feature space. Extensive experiments on multiple De-ReID benchmarks demonstrate the effectiveness of SANER, achieving new state-of-the-art De-ReID performance.

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:d0782a770f5597d7f408ac51cd3a50ceCVF · CVPR 2026

BiOTPrompt: Bidirectional Optimal Transport Guided Prompting for Disease Evolution-aware Radiology Report Generation

Liu, Tengfei, Fan, Yijian, Wang, Boyue, Hu, Yongli, Li, Mingjie, Li, Jinghua, Gao, Junbin, Chang, Xiaojun 等 10 位作者

原文摘要

Radiology report generation (RRG) aims to automatically describe medical images via free-text reports. In clinical practice, comparing current and prior chest X-rays is essential for assessing disease progression, motivating the development of longitudinal RRG methods. However, most existing approaches often struggle to capture fine-grained temporal changes, as they often rely on unidirectional alignments or static reasoning pipelines, overlooking the bidirectional and asymmetric nature of disease evolution. To tackle these challenges, we propose BiOTPrompt, a novel framework for disease evolution-aware radiology report generation, which introduces a Bidirectional Optimal Transport (BiOT) mechanism to explicitly model progression dynamics between historical and current chest X-rays. By analyzing the asymmetry between bidirectional transport plans, BiOTPrompt can identify newly emerged and resolved regions, which are then used to construct dynamic prompts that guide large language models (LLMs) in generating clinically relevant diagnostic reports. Furthermore, we incorporate a vision-language consistency constraint to ensure alignment between visual evidence and textual descriptions, mitigating hallucinations and enhancing factual correctness. Extensive experiments on the Longitudinal-MIMIC dataset demonstrate that BiOTPrompt achieves state-of-the-art performance in both language metrics and clinical relevance, setting a new standard for longitudinal radiology report generation.

cs.CV · cs.CL

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:ec683c5769b0767737c96a16885a19a9CVF · CVPR 2026

Efficient Training for Human Video Generation with Entropy-Guided Prioritized Progressive Learning

Li, Changlin, Zhang, Jiawei, Liu, Shuhao, Lin, Sihao, Shi, Zeyi, Li, Zhihui, Chang, Xiaojun

原文摘要

Human video generation has advanced rapidly with the development of diffusion models, but the high computational cost and substantial memory consumption associated with training these models on high-resolution, multi-frame data pose significant challenges. In this paper, we propose Entropy-Guided Prioritized Progressive Learning (Ent-Prog), an efficient training framework tailored for diffusion models on human video generation. First, we introduce Conditional Entropy Inflation (CEI) to assess the importance of different model components on the target conditional generation task, enabling prioritized training of the most critical components. Second, we introduce an adaptive progressive schedule that adaptively increases computational complexity during training by measuring the convergence efficiency. Ent-Prog reduces both training time and GPU memory consumption while maintaining model performance. Extensive experiments across three datasets, demonstrate the effectiveness of Ent-Prog, achieving up to 2.2x training speedup and 2.4x GPU memory reduction without compromising generative performance.

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 ↗