作者: 符合規(guī)定 時間: 2025-3-21 23:53
https://doi.org/10.1007/1-4020-3156-4 features with minimum labelling efforts, showing that cross modeling on such features using a transformer architecture leads to strong performance. In addition, we demonstrate the broad application of NSVA by addressing two additional tasks, namely fine-grained sports action recognition and salient作者: athlete’s-foot 時間: 2025-3-22 01:37
When Does a Human Being Become a Person?,ound domain-invariant visual representations and improve the model generalization. Furthermore, in the large-scale DomainBed benchmark, our proposed method achieves state-of-the-art results and ranks 1st in average performance for five multi-domain datasets. The dataset and codes are available at 作者: FUSE 時間: 2025-3-22 08:32 作者: –scent 時間: 2025-3-22 10:41 作者: Antimicrobial 時間: 2025-3-22 13:13 作者: Antimicrobial 時間: 2025-3-22 19:06
From Quietism to Evangelicalismby utilizing audiovisual data but relying exclusively on audio annotations. We achieve this by modelling the direct relationship between the audio signal and the possible sound sources (speakers), as well as introducing a contrastive loss.作者: CHIDE 時間: 2025-3-23 00:20
Education in the Indonesian Army,ene to mitigate the ambiguity of prediction. Finally, we introduce an adaptive relevance fusion module for learning the shared representations among multiple contexts. Extensive experiments show that our approach outperforms the state-of-the-art methods on both EMOTIC and GroupWalk datasets. We also作者: 貧困 時間: 2025-3-23 04:06 作者: Hypopnea 時間: 2025-3-23 07:38 作者: 是突襲 時間: 2025-3-23 10:06 作者: 可以任性 時間: 2025-3-23 17:46 作者: Corporeal 時間: 2025-3-23 18:39
Ferdinand Eder,Franz Kroath,Josef Thonhausermework to capture the mapping from radio signals to respiration while excluding the GM components in a self-supervised manner. We test the proposed model based on the newly collected and released datasets under real-world conditions. This study is the first realization of the nRRM task for moving/oc作者: Lobotomy 時間: 2025-3-24 00:25
https://doi.org/10.1007/978-3-031-37645-0easoning by bringing audio as a core component of this multimodal problem. Using ., we evaluate multiple state-of-the-art models on our new challenging task. While some models show promising results (. accuracy), they all fall short of human performance (. accuracy). We conclude the paper by demonst作者: 逃避現(xiàn)實 時間: 2025-3-24 06:12
Explorations of Educational Purpose-a-kind online video quality prediction framework for live streaming, using a multi-modal learning framework with separate pathways to compute visual and audio quality predictions. Our all-in-one model is able to provide accurate quality predictions at the patch, frame, clip, and audiovisual levels.作者: BRUNT 時間: 2025-3-24 09:56
,Most and?Least Retrievable Images in?Visual-Language Query Systems,s advertisement. They are evaluated by extensive experiments based on the modern visual-language models on multiple benchmarks, including Paris, ImageNet, Flickr30k, and MSCOCO. The experimental results show the effectiveness and robustness of the proposed schemes for constructing MRI and LRI.作者: Supplement 時間: 2025-3-24 14:25 作者: champaign 時間: 2025-3-24 16:10
,Grounding Visual Representations with?Texts for?Domain Generalization,ound domain-invariant visual representations and improve the model generalization. Furthermore, in the large-scale DomainBed benchmark, our proposed method achieves state-of-the-art results and ranks 1st in average performance for five multi-domain datasets. The dataset and codes are available at 作者: maculated 時間: 2025-3-24 19:18
,Bridging the?Visual Semantic Gap in?VLN via?Semantically Richer Instructions,lude textual instructions that are intended to inform an expert navigator, such as a human, but not a beginner visual navigational agent, such as a randomly initialized DL model. Specifically, to bridge the visual semantic gap of current VLN datasets, we take advantage of metadata available for the 作者: 豐滿中國 時間: 2025-3-25 01:50 作者: originality 時間: 2025-3-25 04:34 作者: 埋葬 時間: 2025-3-25 09:38
End-to-End Active Speaker Detection,by utilizing audiovisual data but relying exclusively on audio annotations. We achieve this by modelling the direct relationship between the audio signal and the possible sound sources (speakers), as well as introducing a contrastive loss.作者: 出生 時間: 2025-3-25 15:43 作者: macular-edema 時間: 2025-3-25 16:26
Adaptive Fine-Grained Sketch-Based Image Retrieval,implify the MAML training in the inner loop to make it more stable and tractable. (2) The margin in our contrastive loss is also meta-learned with the rest of the model. (3) Three additional regularisation losses are introduced in the outer loop, to make the meta-learned FG-SBIR model more effective作者: incite 時間: 2025-3-25 23:46
,Quantized GAN for?Complex Music Generation from?Dance Videos,, we assess the generative qualities of our proposal against alternatives. The attained quantitative results, which measure the music consistency, beats correspondence, and music diversity, demonstrate the effectiveness of our proposed method. Last but not least, we curate a challenging dance-music 作者: 藝術(shù) 時間: 2025-3-26 00:50
,Uncertainty-Aware Multi-modal Learning via?Cross-Modal Random Network Prediction,training process. From a technical point of view, CRNP is the first approach to explore random network prediction to estimate uncertainty and to combine multi-modal data. Experiments on two 3D multi-modal medical image segmentation tasks and three 2D multi-modal computer vision classification tasks 作者: 緯度 時間: 2025-3-26 07:05
,Localizing Visual Sounds the?Easy Way,or improved precision. Our simple and effective framework achieves state-of-the-art performance on two popular benchmarks, Flickr SoundNet and VGG-Sound Source. In particular, we improve the CIoU on Flickr SoundNet from 76.80% to 83.94%, and on VGG-Sound Source from 34.60% to 38.85%. Code and pretra作者: 容易做 時間: 2025-3-26 08:54
,Remote Respiration Monitoring of?Moving Person Using Radio Signals,mework to capture the mapping from radio signals to respiration while excluding the GM components in a self-supervised manner. We test the proposed model based on the newly collected and released datasets under real-world conditions. This study is the first realization of the nRRM task for moving/oc作者: 認(rèn)識 時間: 2025-3-26 13:35 作者: lobster 時間: 2025-3-26 18:09
Telepresence Video Quality Assessment,-a-kind online video quality prediction framework for live streaming, using a multi-modal learning framework with separate pathways to compute visual and audio quality predictions. Our all-in-one model is able to provide accurate quality predictions at the patch, frame, clip, and audiovisual levels.作者: FER 時間: 2025-3-26 23:38 作者: Influx 時間: 2025-3-27 02:30 作者: Obligatory 時間: 2025-3-27 09:02 作者: larder 時間: 2025-3-27 11:41
VoViT: Low Latency Graph-Based Audio-Visual Voice Separation Transformer,t source. In a second stage, the predominant voice is enhanced with an audio-only network. We present different ablation studies and comparison to state-of-the-art methods. Finally, we explore the transferability of models trained for speech separation in the task of singing voice separation. The demos, code, and weights are available in ..作者: 蜈蚣 時間: 2025-3-27 15:34 作者: Loathe 時間: 2025-3-27 19:50
0302-9743 ruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-19835-9978-3-031-19836-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: RALES 時間: 2025-3-28 00:07 作者: Shuttle 時間: 2025-3-28 04:37 作者: 值得尊敬 時間: 2025-3-28 06:31
0302-9743 puter Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..?.The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforc作者: 積習(xí)已深 時間: 2025-3-28 10:58
https://doi.org/10.1007/978-94-010-2819-6o guide VQGAN [.] produces higher visual quality outputs than prior, less flexible approaches like minDALL-E [.], GLIDE [.] and Open-Edit [.], despite not being trained for the tasks presented. Our code is available in a ..作者: 侵略 時間: 2025-3-28 15:14 作者: MOTTO 時間: 2025-3-28 20:39 作者: 假 時間: 2025-3-28 22:54
,Most and?Least Retrievable Images in?Visual-Language Query Systems,s. An MRI is associated with and thus can be retrieved by many unrelated texts, while an LRI is disassociated from and thus not retrievable by related texts. Both of them have important practical applications and implications. Due to their one-to-many nature, it is fundamentally challenging to const作者: Ornithologist 時間: 2025-3-29 05:54 作者: Promotion 時間: 2025-3-29 10:30 作者: hegemony 時間: 2025-3-29 15:28
,Bridging the?Visual Semantic Gap in?VLN via?Semantically Richer Instructions,information. While this is a trivial task for most humans, it is still an open problem for AI models. In this work, we hypothesize that poor use of the visual information available is at the core of the low performance of current models. To support this hypothesis, we provide experimental evidence s作者: 簡略 時間: 2025-3-29 16:49
,: Adapting Pretrained Text-to-Image Transformers for?Story Continuation,en text. However, these models are ill-suited for specialized tasks like story visualization, which requires an agent to produce a sequence of images given a corresponding sequence of captions, forming a narrative. Moreover, we find that the story visualization task fails to accommodate generalizati作者: 含鐵 時間: 2025-3-29 21:59 作者: Allege 時間: 2025-3-30 02:43 作者: MAL 時間: 2025-3-30 05:27
End-to-End Active Speaker Detection,on. In this paper, we propose an end-to-end ASD workflow where feature learning and contextual predictions are jointly learned. Our end-to-end trainable network simultaneously learns multi-modal embeddings and aggregates spatio-temporal context. This results in more suitable feature representations 作者: 類似思想 時間: 2025-3-30 12:16
,Emotion Recognition for?Multiple Context Awareness,certainty in expressing emotions and fail to model multiple context representations complementarily. To alleviate these issues, we present a context-aware emotion recognition framework that combines four complementary contexts. The first context is multimodal emotion recognition based on facial expr作者: 輕率的你 時間: 2025-3-30 15:47 作者: constellation 時間: 2025-3-30 17:33 作者: 外向者 時間: 2025-3-30 20:49 作者: 束以馬具 時間: 2025-3-31 04:38
,Localizing Visual Sounds the?Easy Way,ining. Previous works often seek high audio-visual similarities for likely positive (sounding) regions and low similarities for likely negative regions. However, accurately distinguishing between sounding and non-sounding regions is challenging without manual annotations. In this work, we propose a 作者: 蚊子 時間: 2025-3-31 05:10
,Learning Visual Styles from?Audio-Visual Associations,nt a method for learning visual styles from unlabeled audio-visual data. Our model learns to manipulate the texture of a scene to match a sound, a problem we term .. Given a dataset of paired audio-visual data, we learn to modify input images such that, after manipulation, they are more likely to co作者: 頭盔 時間: 2025-3-31 11:30
,Remote Respiration Monitoring of?Moving Person Using Radio Signals,rious remote applications (e.g., telehealth or emergency detection). The existing nRRM approaches mainly analyze fine details from videos to extract minute respiration signals; however, they have practical limitations in that the head or body of a subject must be quasi-stationary. In this study, we 作者: Isometric 時間: 2025-3-31 17:19 作者: Charitable 時間: 2025-3-31 18:45
,: A Dataset for?Physical Audiovisual CommonSense Reasoning,the physical world. Fundamental to this reasoning is .: understanding the physical properties and affordances of available objects, how they can be manipulated, and how they interact with other objects. Physical commonsense reasoning is fundamentally a multi-sensory task, since physical properties a作者: 熱心 時間: 2025-4-1 00:03 作者: 不可磨滅 時間: 2025-4-1 05:22
Telepresence Video Quality Assessment,orced millions of people to work and learn from home. Global Internet traffic of video conferencing has dramatically increased Because of this, efficient and accurate video quality tools are needed to monitor and perceptually optimize telepresence traffic streamed via Zoom, Webex, Meet, ... However,作者: 記憶 時間: 2025-4-1 09:44 作者: conjunctivitis 時間: 2025-4-1 11:59 作者: 使習(xí)慣于 時間: 2025-4-1 14:26 作者: 無能力之人 時間: 2025-4-1 22:30 作者: GIBE 時間: 2025-4-2 02:35
https://doi.org/10.1007/1-4020-3156-4he-art approaches fall quite short of capturing how human experts analyze sports scenes. There are several major reasons: (1) The used dataset is collected from non-official providers, which naturally creates a gap between models trained on those datasets and real-world applications; (2) previously 作者: 捏造 時間: 2025-4-2 04:33
When Does a Human Being Become a Person?,advocate for leveraging natural language supervision for the domain generalization task. We introduce two modules to ground visual representations with texts containing typical reasoning of humans: (1) . and (2) .. The former learns the image-text joint embedding space where we can ground high-level
