作者: Factual 時(shí)間: 2025-3-21 22:59 作者: MEEK 時(shí)間: 2025-3-22 02:26
FrequencyLowCut Pooling - Plug and Play Against Catastrophic Overfitting,to any CNN architecture: FrequencyLowCut pooling. Our experiments show, that in combination with simple and Fast Gradient Sign Method (FGSM) adversarial training, our hyper-parameter free operator substantially improves model robustness and avoids catastrophic overfitting. Our code is available at .作者: 禁令 時(shí)間: 2025-3-22 08:13
TAFIM: Targeted Adversarial Attacks Against Facial Image Manipulations,alized model only needs a single forward pass, thus running orders of magnitude faster and allowing for easy integration in image processing stacks, even on resource-constrained devices like smartphones (Project Page: .).作者: Heart-Rate 時(shí)間: 2025-3-22 09:01 作者: 酷熱 時(shí)間: 2025-3-22 16:49
,An Information Theoretic Approach for?Attention-Driven Face Forgery Detection,g-and-play block, termed self-information attention (SIA) module, which can be integrated with most of the top-performance deep models to boost their detection performance. The SIA module can explicitly help the model locate the informative regions and recalibrate channel-wise feature responses, whi作者: 酷熱 時(shí)間: 2025-3-22 20:46 作者: 修飾 時(shí)間: 2025-3-23 00:45 作者: metropolitan 時(shí)間: 2025-3-23 02:32
,Adaptive Cross-domain Learning for?Generalizable Person Re-identification,omplementary branches, a dynamic branch for extracting domain-adaptive features and a static branch for extracting the domain-invariant features. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art performances on the popular benchmarks. Under Protocol-2, our metho作者: erythema 時(shí)間: 2025-3-23 08:07 作者: nerve-sparing 時(shí)間: 2025-3-23 10:55 作者: Bravado 時(shí)間: 2025-3-23 14:17 作者: 小淡水魚 時(shí)間: 2025-3-23 18:55 作者: opinionated 時(shí)間: 2025-3-24 01:02 作者: Addictive 時(shí)間: 2025-3-24 05:02 作者: 薄膜 時(shí)間: 2025-3-24 09:58 作者: excrete 時(shí)間: 2025-3-24 12:43
Energy, a Case Study: (II) Demand,alized model only needs a single forward pass, thus running orders of magnitude faster and allowing for easy integration in image processing stacks, even on resource-constrained devices like smartphones (Project Page: .).作者: 記憶 時(shí)間: 2025-3-24 15:26 作者: 泄露 時(shí)間: 2025-3-24 21:48
Nicolas Jonard,Murat Y?ld?zo?lug-and-play block, termed self-information attention (SIA) module, which can be integrated with most of the top-performance deep models to boost their detection performance. The SIA module can explicitly help the model locate the informative regions and recalibrate channel-wise feature responses, whi作者: 半圓鑿 時(shí)間: 2025-3-25 02:07
https://doi.org/10.1007/1-4020-2515-7t knowing the physical properties of the totem. Unlike prior learning-based approaches, our method does not require training on datasets of specific manipulations, and instead uses physical properties of the scene and camera to solve the forensics problem.作者: abolish 時(shí)間: 2025-3-25 04:38 作者: Baffle 時(shí)間: 2025-3-25 10:30
Economics of North Sea Oil Taxationomplementary branches, a dynamic branch for extracting domain-adaptive features and a static branch for extracting the domain-invariant features. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art performances on the popular benchmarks. Under Protocol-2, our metho作者: grovel 時(shí)間: 2025-3-25 15:17 作者: Esophagitis 時(shí)間: 2025-3-25 16:23
The Economics of Nuclear Energyt domain-specific features and adapting to individual domains or instances. We further utilize a multi-task learning strategy to train the model, ensuring it can adaptively produce discriminative feature representations for an arbitrary domain. Our results show a great domain generalization capabili作者: 高深莫測(cè) 時(shí)間: 2025-3-25 21:43
The Balance of Supply and Demandd task-sensitive instance-level alignments, to minimize the domain discrepancy. 2) We take full advantage of the unlabeled data with a dynamic clustering strategy, and employ pseudo bounding boxes to support ReID and detection training on the target domain. With the above designs, our framework achi作者: Intuitive 時(shí)間: 2025-3-26 03:32 作者: 共和國(guó) 時(shí)間: 2025-3-26 07:43
Conference proceedings 2022ning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..作者: BARK 時(shí)間: 2025-3-26 10:03 作者: Diluge 時(shí)間: 2025-3-26 15:34 作者: Champion 時(shí)間: 2025-3-26 18:35 作者: vitreous-humor 時(shí)間: 2025-3-26 21:19 作者: 最小 時(shí)間: 2025-3-27 03:44
Can Neighborhood Protect Diversity experiments demonstrate that our framework can not only ignore the interference of content information, but also guide the detector to mine suspicious artifact traces and achieve competitive performance.作者: AGATE 時(shí)間: 2025-3-27 05:26
Nicolas Jonard,Murat Y?ld?zo?luariant to the semantic content of the image and also robust to perturbations. We show our approach improves significantly from existing GAN fingerprinting works on both semantic generalization and robustness. Data and code are available at ..作者: 玷污 時(shí)間: 2025-3-27 12:09
Partha Dasgupta,Karl-G?ran M?lertemporal layer learns to capture the information for reconstruction. In this way, the model naturally preserves the disentangled semantics into binary codes. Validated by comprehensive experiments, our method consistently outperforms the state-of-the-arts on three video benchmarks.作者: Bridle 時(shí)間: 2025-3-27 17:30 作者: 一夫一妻制 時(shí)間: 2025-3-27 18:36 作者: Lipohypertrophy 時(shí)間: 2025-3-28 00:25 作者: dagger 時(shí)間: 2025-3-28 05:25 作者: jovial 時(shí)間: 2025-3-28 09:25 作者: macular-edema 時(shí)間: 2025-3-28 12:24 作者: effrontery 時(shí)間: 2025-3-28 14:43 作者: FOLLY 時(shí)間: 2025-3-28 19:55
,Learning Semantic Correspondence with?Sparse Annotations,stantiate our paradigm with two variants of learning strategies: a single offline teacher setting, and mutual online teachers setting. Our approach achieves notable improvements on three challenging benchmarks for semantic correspondence and establishes the new state-of-the-art. Project page:?..作者: nutrition 時(shí)間: 2025-3-29 01:57 作者: Maximize 時(shí)間: 2025-3-29 05:49 作者: inventory 時(shí)間: 2025-3-29 09:40
FrequencyLowCut Pooling - Plug and Play Against Catastrophic Overfitting,an image and signal processing point of view, this success might be a bit surprising as the inherent spatial pyramid design of most CNNs is apparently violating basic signal processing laws, i.e.?. in their down-sampling operations. However, since poor sampling appeared not to affect model accuracy,作者: 枯萎將要 時(shí)間: 2025-3-29 13:20
TAFIM: Targeted Adversarial Attacks Against Facial Image Manipulations,approach that produces image-specific perturbations which are embedded in the original images. The key idea is that these protected images prevent face manipulation by causing the manipulation model to produce a predefined manipulation target (uniformly colored output image in our case) instead of t作者: 龍卷風(fēng) 時(shí)間: 2025-3-29 18:10
,FingerprintNet: Synthesized Fingerprints for?Generated Image Detection, false news. To prevent such cases, vigorous research is conducted on distinguishing the generated images from the real ones, but challenges still remain with detecting the unseen generated images outside of the training settings. To overcome this problem, we analyze the distinctive characteristic o作者: abreast 時(shí)間: 2025-3-29 20:02 作者: Harridan 時(shí)間: 2025-3-30 00:25 作者: Blasphemy 時(shí)間: 2025-3-30 06:52
,Exploring Disentangled Content Information for?Face Forgery Detection,le performance during testing. We observe that the detector is prone to focus more on content information than artifact traces, suggesting that the detector is sensitive to the intrinsic bias of the dataset, which leads to severe overfitting. Motivated by this key observation, we design an easily em作者: Alpha-Cells 時(shí)間: 2025-3-30 08:40 作者: 大包裹 時(shí)間: 2025-3-30 13:35
,Totems: Physical Objects for?Verifying Visual Integrity,h taken of that scene. Totems bend and redirect light rays, thus providing multiple, albeit distorted, views of the scene within a single image. A defender can use these distorted totem pixels to detect if an image has been manipulated. Our approach unscrambles the light rays passing through the tot作者: 小臼 時(shí)間: 2025-3-30 19:04
,Dual-Stream Knowledge-Preserving Hashing for?Unsupervised Video Retrieval,n frame-level temporal context changes without focusing on video-level global semantics that are more useful for retrieval. Hence, we address this problem by decomposing video information into reconstruction-dependent and semantic-dependent information, which disentangles the semantic extraction fro作者: BRINK 時(shí)間: 2025-3-30 22:58 作者: Negligible 時(shí)間: 2025-3-31 03:01
,Adaptive Cross-domain Learning for?Generalizable Person Re-identification,nseen target domains. Most existing methods are challenged for dealing with the shared and specific characteristics among different domains, which is called the domain conflict problem. To address this problem, we present an Adaptive Cross-domain Learning (ACL) framework equipped with a CrOss-Domain作者: AER 時(shí)間: 2025-3-31 06:53
Multi-query Video Retrieval,Despite recent progress, imperfect annotations in existing video retrieval datasets have posed significant challenges on model evaluation and development. In this paper, we tackle this issue by focusing on the less-studied setting of multi-query video retrieval, where multiple descriptions are provi作者: 感情 時(shí)間: 2025-3-31 10:22 作者: Exuberance 時(shí)間: 2025-3-31 13:30 作者: 明智的人 時(shí)間: 2025-3-31 20:52 作者: 闖入 時(shí)間: 2025-3-31 22:39
Domain Adaptive Person Search,nificant advances under fully and weakly supervised settings. However, existing methods ignore the generalization ability of the person search models. In this paper, we take a further step and present Domain Adaptive Person Search (DAPS), which aims to generalize the model from a labeled source doma作者: Dungeon 時(shí)間: 2025-4-1 04:43 作者: congenial 時(shí)間: 2025-4-1 08:45 作者: N防腐劑 時(shí)間: 2025-4-1 11:28 作者: drusen 時(shí)間: 2025-4-1 17:21 作者: 盡責(zé) 時(shí)間: 2025-4-1 19:21
Monica Bonacina,Anna Cretì,Antonio Sileo visible watermarks can be removed by DNNs without damaging their host images. Such watermark-removal techniques pose a great threat to the ownership of images. Inspired by the vulnerability of DNNs on adversarial perturbations, we propose a novel defence mechanism by adversarial machine learning fo作者: glacial 時(shí)間: 2025-4-2 00:22
