作者: 小溪 時間: 2025-3-21 21:12 作者: STANT 時間: 2025-3-22 00:41 作者: 夸張 時間: 2025-3-22 06:55 作者: 晚來的提名 時間: 2025-3-22 11:58 作者: 附錄 時間: 2025-3-22 14:08 作者: 附錄 時間: 2025-3-22 21:06
Ambient Communications and Computer Systemssequences. Motivated by the human cognitive process for long-form video understanding, we emphasize interactive reasoning and planning over the ability to process lengthy visual inputs. We introduce a novel agent-based system, ., that employs a large language model as a central agent?to iteratively 作者: Goblet-Cells 時間: 2025-3-22 23:29
Ambient Communications and Computer Systemsta typically do not align perfectly with each other, which might lead to video-language representations that do not accurately reflect cross-modal semantics. Moreover, previous data also possess an uneven distribution of concepts, thereby hampering the downstream performance across unpopular subject作者: Enervate 時間: 2025-3-23 01:49 作者: 旁觀者 時間: 2025-3-23 06:08
https://doi.org/10.1007/978-981-13-5934-7tedly querying the network. In this work, we develop a novel network architecture that leverages sparse-coding layers to obtain superior robustness to this class of attacks. Three decades of computer science research has studied sparse coding in the context of image denoising, object recognition, an作者: 束縛 時間: 2025-3-23 09:45 作者: Fsh238 時間: 2025-3-23 16:37
Almost Self-centered Index of Some Graphstrained on data from the same distribution. However, in practical applications, the student network may be required to perform in a new scenario (., the target domain), which usually exhibits significant differences from the known scenario of the teacher network (., the source domain). The tradition作者: EWER 時間: 2025-3-23 19:26 作者: backdrop 時間: 2025-3-23 23:50
Lecture Notes in Computer Scienceuction styles like documentaries, dramas, feature films, or a specific YouTube channel’s video-making technique. Our algorithm recommends optimal visual transitions to help achieve this flexibility using a more bottom-up approach. We first employ a transformer-based encoder-decoder network to learn 作者: Colonoscopy 時間: 2025-3-24 04:03 作者: genuine 時間: 2025-3-24 06:44 作者: 清晰 時間: 2025-3-24 14:05 作者: Little 時間: 2025-3-24 17:06
https://doi.org/10.1007/978-3-030-03062-9g results in various downstream applications. Given its potential as a medium for visual signals, exploring the development of a neural blending method that utilizes INRs is a natural progression. Neural blending involves merging two INRs to create a new INR that encapsulates information from both o作者: FATAL 時間: 2025-3-24 21:56
https://doi.org/10.1007/978-3-030-03062-9operties of spherical images pose a major challenge for models and metrics designed for ordinary 2D images. Here, we show that direct application of Fréchet Inception Distance (FID) is insufficient for quantifying geometric fidelity in spherical images. We introduce two quantitative metrics accounti作者: 追蹤 時間: 2025-3-25 02:29 作者: aneurysm 時間: 2025-3-25 03:48
Jennifer Renoux,Uwe K?ckemann,Amy Loutfiatastrophic forgetting and task confusion. While CSSL problem has been studied to address the catastrophic forgetting challenge, little work has been done to address the task confusion aspect. In this work, we show through extensive experiments that self-supervised learning (SSL) can make CSSL more 作者: Evolve 時間: 2025-3-25 10:14
Félix Iglesias Vázquez,Wolfgang Kastnered data. Due to the limitations in diversity and challenges in handling lighting conditions, traditional rendering-based methods are increasingly being supplanted by diffusion-based methods. However, a significant challenge in diffusion-based methods is ensuring that the generated sensor data preser作者: Negotiate 時間: 2025-3-25 15:20
0302-9743 ce on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; r作者: GRAZE 時間: 2025-3-25 17:21 作者: 沒有貧窮 時間: 2025-3-25 20:25 作者: 魔鬼在游行 時間: 2025-3-26 01:54 作者: 善變 時間: 2025-3-26 07:55 作者: 蛛絲 時間: 2025-3-26 10:44 作者: 提煉 時間: 2025-3-26 16:33 作者: 擁護(hù)者 時間: 2025-3-26 17:39
0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-72988-1978-3-031-72989-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: anachronistic 時間: 2025-3-26 22:55 作者: 十字架 時間: 2025-3-27 04:07
Manish Asthana,Kapil Dev Gupta,Arvind Kumarssing potential spurious correlations in datasets, annotating concepts for images, and refining the annotations for improved robustness. We evaluate the proposed method on multiple datasets, and the results demonstrate its effectiveness in reducing model reliance on spurious correlations while preserving its interpretability.作者: 牲畜欄 時間: 2025-3-27 07:06
,: Long-Form Video Understanding with?Large Language Model as?Agent,es used on average. These results demonstrate superior effectiveness and efficiency of our method over the current state-of-the-art methods, highlighting the potential of agent-based approaches in advancing long-form video understanding.作者: overreach 時間: 2025-3-27 10:08 作者: DAUNT 時間: 2025-3-27 17:12
Sunil B. Bhoi,Jayesh M. Dhodiyaion learning of the natural world—and introduce Nature Multi-View (NMV), a dataset of natural world imagery including >3 million ground-level and aerial image pairs for over 6,000 plant taxa across the ecologically diverse state of California. The NMV dataset and accompanying material are available at ..作者: FOLD 時間: 2025-3-27 18:30
Conference proceedings 2025uter Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforceme作者: PACK 時間: 2025-3-28 00:33
,Ex2Eg-MAE: A Framework for?Adaptation of?Exocentric Video Masked Autoencoders for?Egocentric Socialntly excels across diverse social role understanding tasks. It achieves state-of-the-art results in Ego4D’s . challenge (+0.7% mAP, +3.2% Accuracy). For the . challenge, it achieves competitive performance with the state-of-the-art (–0.7% mAP, +1.5% Accuracy) without supervised training on external 作者: 繁重 時間: 2025-3-28 02:45
,SAVE: Protagonist Diversification with?,tructure ,gnostic ,ideo ,diting,xtual embedding to properly represent the motion in a source video. We also regulate the motion word to attend to proper motion-related areas by introducing a novel pseudo optical flow, efficiently computed from the pre-calculated attention maps. Finally, we decouple the motion from the appearance o作者: Hyperlipidemia 時間: 2025-3-28 09:39
,Meta-optimized Angular Margin Contrastive Framework for?Video-Language Representation Learning, training guided by a small amount of unbiased meta-data and augmented by video-text data generated by large vision-language model, we improve video-language representations and achieve superior performances on commonly used video question answering and text-video retrieval datasets.作者: Herpetologist 時間: 2025-3-28 13:57 作者: meritorious 時間: 2025-3-28 17:19 作者: 小口啜飲 時間: 2025-3-28 21:57 作者: OUTRE 時間: 2025-3-29 01:01 作者: 廣口瓶 時間: 2025-3-29 06:46 作者: Angiogenesis 時間: 2025-3-29 09:36 作者: insecticide 時間: 2025-3-29 15:04 作者: 詞根詞綴法 時間: 2025-3-29 18:53 作者: Robust 時間: 2025-3-29 20:56
,CroMo-Mixup: Augmenting Cross-Model Representations for?Continual Self-Supervised Learning,) Cross-Model feature Mixup, which learns similarities between embeddings obtained from current and old models of the mixed sample and the original images, facilitating cross-task class contrast learning and old knowledge retrieval. We evaluate the effectiveness of CroMo-Mixup to improve both Task-I作者: bisphosphonate 時間: 2025-3-30 00:44 作者: 本能 時間: 2025-3-30 06:31 作者: 偉大 時間: 2025-3-30 09:16 作者: Infraction 時間: 2025-3-30 12:38
Towards Resilient Services in the Homextual embedding to properly represent the motion in a source video. We also regulate the motion word to attend to proper motion-related areas by introducing a novel pseudo optical flow, efficiently computed from the pre-calculated attention maps. Finally, we decouple the motion from the appearance o作者: indenture 時間: 2025-3-30 18:15
Ambient Communications and Computer Systems training guided by a small amount of unbiased meta-data and augmented by video-text data generated by large vision-language model, we improve video-language representations and achieve superior performances on commonly used video question answering and text-video retrieval datasets.作者: Fibrin 時間: 2025-3-30 21:11 作者: 改變 時間: 2025-3-31 03:38
https://doi.org/10.1007/978-981-13-5934-7on accuracy. Specifically, compared to networks trained with a variety of state-of-the-art defenses, our sparse-coding architectures maintain comparable or higher classification accuracy while degrading state-of-the-art training data reconstructions by factors of 1.1 to 18.3 across a variety of reco作者: APNEA 時間: 2025-3-31 05:00 作者: BABY 時間: 2025-3-31 10:59 作者: 胡言亂語 時間: 2025-3-31 16:49
Sensor Fusion for Augmented Realityches. Evaluation across diverse indoor RGB-D datasets demonstrates LRSLAM’s superior performance in terms of parameter efficiency, processing time, and accuracy, retaining reconstruction and localization quality. Our code will be publicly available upon publication.作者: SUE 時間: 2025-3-31 21:35
Jurjen Caarls,Pieter Jonker,Stelian Persacific predictors to improve the universality of the shared encoder’s representations. Through experiments on multiple multi-task learning benchmark datasets, we demonstrate that DGR effectively improves the quality of the shared representations, leading to better multi-task prediction performances. 作者: 鬧劇 時間: 2025-3-31 22:20 作者: Complement 時間: 2025-4-1 02:08 作者: 兩棲動物 時間: 2025-4-1 07:55
Jennifer Renoux,Uwe K?ckemann,Amy Loutfi) Cross-Model feature Mixup, which learns similarities between embeddings obtained from current and old models of the mixed sample and the original images, facilitating cross-task class contrast learning and old knowledge retrieval. We evaluate the effectiveness of CroMo-Mixup to improve both Task-I作者: Crepitus 時間: 2025-4-1 12:42 作者: 不理會 時間: 2025-4-1 17:00 作者: 巨頭 時間: 2025-4-1 22:33
,SAVE: Protagonist Diversification with?,tructure ,gnostic ,ideo ,diting, Accordingly, tasks such as modifying the object or changing the style in a video have been possible. However, previous works usually work well on trivial and consistent shapes, and easily collapse on a difficult target that has a largely different body shape from the original one. In this paper, we作者: embolus 時間: 2025-4-1 23:46
,: Long-Form Video Understanding with?Large Language Model as?Agent,sequences. Motivated by the human cognitive process for long-form video understanding, we emphasize interactive reasoning and planning over the ability to process lengthy visual inputs. We introduce a novel agent-based system, ., that employs a large language model as a central agent?to iteratively 作者: Panacea 時間: 2025-4-2 06:13
,Meta-optimized Angular Margin Contrastive Framework for?Video-Language Representation Learning,ta typically do not align perfectly with each other, which might lead to video-language representations that do not accurately reflect cross-modal semantics. Moreover, previous data also possess an uneven distribution of concepts, thereby hampering the downstream performance across unpopular subject作者: 不能逃避 時間: 2025-4-2 09:18
Source-Free Domain-Invariant Performance Prediction,data distributions. Most existing performance prediction methods heavily rely on the source data in their estimation process, limiting their applicability in a more realistic setting where only the trained model is accessible. The few methods that do not require source data exhibit considerably infe作者: 痛苦一生 時間: 2025-4-2 14:06
,Improving Robustness to?Model Inversion Attacks via?Sparse Coding Architectures,tedly querying the network. In this work, we develop a novel network architecture that leverages sparse-coding layers to obtain superior robustness to this class of attacks. Three decades of computer science research has studied sparse coding in the context of image denoising, object recognition, an作者: chastise 時間: 2025-4-2 15:48
,Constructing Concept-Based Models to?Mitigate Spurious Correlations with?Minimal Human Effort,n provide a principled way of disclosing and guiding model behaviors through human-understandable concepts, albeit at a high cost of human efforts in data annotation. In this paper, we leverage a synergy of multiple foundation models to construct CBMs with nearly no human effort. We discover undesir
