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
+3已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-14 16:12 · 全站最近入库：2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

统计 2026-11-13 16:54 — 2026-11-14 16:54（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：2026-11-14 16:12最近发现新增版本：2026-11-14 15:09库内论文最新更新时间：2026-11-13 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。

23,348 已收录论文19,580 AI 相关6,729 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 3 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-14 16:11（北京时间）
首次观测 · 记录 #1CVF · CVPR 2026:cvf:14868e69f6facf767dae0e3f639e8780CVF · CVPR 2026

A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World

Cheng, Jikang, Yan, Renye, Yan, Zhiyuan, Gan, Yaozhong, Zhang, Xueyi, Wang, Zhongyuan, Peng, Wei, Liang, Ling

原文摘要

Existing methods for deepfake detection aim to develop generalizable detectors. Although "generalizable" could be the ultimate target once and for all, with limited training forgeries and domains, it appears idealistic to expect generalization that covers entirely unseen variations, especially given the diversity, advancement, and vast volume of real-world deepfakes. Therefore, introducing large-scale multi-domain data for training can be feasible and important for real-world applications.However, within such a multi-domain scenario, the differences between multiple domains, rather than the subtle real/fake distinctions, dominate the feature space. As a result, despite detectors being able to relatively separate real and fake within each domain (i.e., high AUC), they struggle with single-image real/fake judgments in domain-unspecified conditions (i.e., low ACC).In this paper, we first define a new research paradigm named Multi-In-Domain Face Forgery Detection (MID-FFD), which includes sufficient volumes of real-fake domains for training. Then, the detector should provide definitive real-fake judgments to the domain-unspecified inputs, which simulate the frame-by-frame independent detection scenario in the real world. Meanwhile, to address the domain-dominant issue, we propose a two-stage, model-agnostic framework termed DevDet (\underline Dev eloper for \underline Det ector) to amplify real/fake differences and make them dominant in the feature space. DevDet consists of a Face Forgery Developer (FFDev) and a Dose-Adaptive detector Fine-Tuning strategy (DAFT). Experiments demonstrate our superiority in effectively predicting real-fake under the MID-FFD scenario while maintaining original generalization ability to unseen data.

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:88a1968832fe02a5d7bb5bf73cbc1915CVF · CVPR 2026

PAUL: Uncertainty-Guided Partition and Augmentation for Robust Cross-View Geo-Localization under Noisy Correspondence

Li, Zheng, Zhang, Xueyi, Guo, Yanming, Xie, Yuxiang, Ding, Zhaoyun, Cai, Siqi, Li, Haizhou, Lao, Mingrui

原文摘要

Cross-view geo-localization is a critical task for UAV navigation, event detection, and aerial surveying, which establish correspondence between drone-captured and satellite imagery. Most existing approaches embed cross-view data into a joint feature space to maximize similarity between paired images. However, these methods typically assume perfect alignment of image pairs in training data, an assumption that rarely holds in practical scenarios. In real-world conditions, factors such as urban canyon effects, electromagnetic interference, and adverse weather frequently induce GPS drift, resulting in systematic **alignment shifts** where only partial correspondences exist between image pairs. Despite its prevalence, this source of noisy correspondence has received limited attention in current research.To our best knowledge, this work presents the first systematic investigation of the **Noisy Correspondence in Cross-View Geo-Localization (NC-CVGL)** problem, specifically addressing the practical scenario where a significant portion of training pairs exhibit spatial misalignment due to GPS inaccuracies. To this end, we propose **PAUL** (**P**artition and **A**ugmentation by **U**ncertainty **L**earning), a framework that achieves noise-robust learning through three coordinated mechanisms: **Co-partition** separates noisy from clean samples using data uncertainty and loss patterns; **Co-augmentation** enhances high-confidence regions via local assessment; and **Co-training** refines learning through mutual supervision between dual networks.Unlike conventional noise-handling methods that filter or relabel noisy samples, PAUL effectively utilizes noisy data through this triple collaborative mechanism. Comprehensive experiments validate the effectiveness of individual components in PAUL, which consistently achieves superior performance over other competitive noisy-correspondence-driven methods in various noise ratios.

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:edaa5c4d95292ec80a430bf5524ab56cCVF · CVPR 2026

Unleashing Vision-Language Semantics for Deepfake Video Detection

Zhu, Jiawen, Miao, Yunqi, Zhang, Xueyi, Deng, Jiankang, Pang, Guansong

原文摘要

Recent Deepfake Video Detection (DFD) studies have demonstrated that pre-trained Vision-Language Models (VLMs) such as CLIP exhibit strong generalization capabilities in detecting artifacts across different identities. However, existing approaches focus on leveraging visual features only, overlooking their most distinctive strength -- the rich vision-language semantics embedded in the latent space. We propose VLAForge, a novel DFD framework that unleashes the potential of such cross-modal semantics to enhance model's discriminability in deepfake detection. This work i) enhances the visual perception of VLM through a ForgePerceiver, which acts as an independent learner to capture diverse, subtle forgery cues both granularly and holistically, while preserving the pretrained Vision-Language Alignment (VLA) knowledge, and ii) provides a complementary discriminative cue -- Identity-Aware VLA score, derived by coupling cross-modal semantics with the forgery cues learned by ForgePerceiver. Notably, the VLA score is augmented by an identity prior-informed text prompting to capture authenticity cues tailored to each identity, thereby enabling more discriminative cross-modal semantics. Comprehensive experiments on video DFD benchmarks, including classical face-swapping forgeries and recent full-face generation forgeries, demonstrate that our VLAForge substantially outperforms state-of-the-art methods at both frame and video levels. Code is available at https://github.com/mala-lab/VLAForge.

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 ↗