作者: Externalize 時(shí)間: 2025-3-21 23:15 作者: 輕浮思想 時(shí)間: 2025-3-22 02:27 作者: Corroborate 時(shí)間: 2025-3-22 06:07 作者: CARE 時(shí)間: 2025-3-22 12:27 作者: definition 時(shí)間: 2025-3-22 13:50 作者: definition 時(shí)間: 2025-3-22 20:59 作者: Expressly 時(shí)間: 2025-3-22 23:23
Bruno Sch?dler,Rolf Weingartner2) . to reduce the maximum negative score for each probe. Across diverse biometric tasks, including face recognition, gait recognition, and person re-identification, our experiments demonstrate the effectiveness of the proposed loss functions, significantly enhancing open-set performance while posit作者: 衰老 時(shí)間: 2025-3-23 05:13 作者: browbeat 時(shí)間: 2025-3-23 08:23 作者: BOAST 時(shí)間: 2025-3-23 11:25
,Generating Physically Realistic and?Directable Human Motions from?Multi-modal Inputs,ibits the key capabilities of . to out-of-sync input commands, . elements from multiple motion sequences, and . unspecified parts of motions from sparse multimodal input. We demonstrate these key capabilities for an MHC learned over a dataset of 87 diverse skills and showcase different multi-modal u作者: 吹牛需要藝術(shù) 時(shí)間: 2025-3-23 17:03 作者: FORGO 時(shí)間: 2025-3-23 19:38
,PathMMU: A Massive Multimodal Expert-Level Benchmark for?Understanding and?Reasoning in?Pathology,evel performance benchmark for PathMMU. We conduct extensive evaluations, including zero-shot assessments of 14 open-sourced and 4 closed-sourced LMMs and their robustness to image corruption. We also fine-tune representative LMMs to assess their adaptability to PathMMU. The empirical findings indic作者: parsimony 時(shí)間: 2025-3-24 01:36
,RealGen: Retrieval Augmented Generation for?Controllable Traffic Scenarios,amples in a gradient-free way, which may originate from templates or tagged scenarios. This in-context learning framework endows versatile generative capabilities, including the ability to edit scenarios, compose various behaviors, and produce critical scenarios. Evaluations show that RealGen offers作者: gerrymander 時(shí)間: 2025-3-24 03:55
,ADen: Adaptive Density Representations for?Sparse-View Camera Pose Estimation,re space of rotation uniformly?by brute-force. This leads to an inevitable trade-off between?high sample density, which improves model precision, and sample efficiency that determines the runtime. In this paper, we propose ADen to unify the two frameworks by employing a generator?and a discriminator作者: 債務(wù) 時(shí)間: 2025-3-24 10:19 作者: pacifist 時(shí)間: 2025-3-24 12:35
,ViLA: Efficient Video-Language Alignment for?Video Question Answering,he state-of-the-art methods on the video question-answering benchmarks: . on STAR Interaction, . on STAR average with . speed up, ours 2-frames out-perform SeViLA 4-frames on the VLEP dataset with . speed-up. Code will be available at 作者: ARCHE 時(shí)間: 2025-3-24 16:46 作者: 打擊 時(shí)間: 2025-3-24 22:52 作者: FATAL 時(shí)間: 2025-3-25 02:24 作者: osteocytes 時(shí)間: 2025-3-25 06:30
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: palliative-care 時(shí)間: 2025-3-25 07:49 作者: instructive 時(shí)間: 2025-3-25 13:26
RaumFragen: Stadt – Region – Landschaftis trained utilizing unrolled windows as a recurrent network, maintaining tracks?for long periods of time even when points are occluded or leave?the field of view. Quantitatively, CoTracker substantially outperforms prior trackers on standard point-tracking benchmarks. Code and?model weights are available at ..作者: 脫離 時(shí)間: 2025-3-25 16:15
https://doi.org/10.1007/978-3-658-37681-9ware token selection to accurately inquire about temporal cues. We instantiate ELM on the reformulated multi-faced benchmark, and it surpasses previous state-of-the-art approaches in all aspects. All code, data, and models are accessible at ..作者: 悅耳 時(shí)間: 2025-3-25 22:00 作者: 煩擾 時(shí)間: 2025-3-26 02:18 作者: Amenable 時(shí)間: 2025-3-26 06:35
HET VERPLATTEN VAN DE ORGANISATIE,ate posterior samples of clean images, removing the water effects. Even though our prior did not see any underwater images during training, our method outperforms state-of-the-art baselines for image restoration on very challenging scenes. Our code, models and data are available on the project’s website.作者: 系列 時(shí)間: 2025-3-26 11:50
,CoTracker: It Is Better to?Track Together,is trained utilizing unrolled windows as a recurrent network, maintaining tracks?for long periods of time even when points are occluded or leave?the field of view. Quantitatively, CoTracker substantially outperforms prior trackers on standard point-tracking benchmarks. Code and?model weights are available at ..作者: Cholagogue 時(shí)間: 2025-3-26 15:13 作者: 排名真古怪 時(shí)間: 2025-3-26 17:12 作者: Aviary 時(shí)間: 2025-3-26 22:04
,Which Model Generated This Image? A Model-Agnostic Approach for?Origin Attribution,iments corresponding to various generative models verify the effectiveness of our OCC-CLIP framework. Furthermore, an experiment based on?the recently released DALL.E-3 API verifies the real-world applicability of our solution. Our source code is available?at ..作者: SPASM 時(shí)間: 2025-3-27 01:52 作者: 十字架 時(shí)間: 2025-3-27 05:22
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: debase 時(shí)間: 2025-3-27 12:35 作者: 大洪水 時(shí)間: 2025-3-27 16:50 作者: disrupt 時(shí)間: 2025-3-27 21:33 作者: fibula 時(shí)間: 2025-3-28 00:51
Christine Altenbuchner,Manuela Larcher. Nvidia’s PYOCO, and . vs. Meta’s Make-A-Video. Our model outperforms commercial solutions such as RunwayML’s Gen2 and Pika Labs. Finally, our factorizing approach naturally lends itself to animating images based on a user’s text prompt, where our generations are preferred . over prior work.作者: 古老 時(shí)間: 2025-3-28 03:42 作者: 合同 時(shí)間: 2025-3-28 09:55
,Improving Adversarial Transferability via?Model Alignment,metric analysis of the resulting changes in the loss landscape. Extensive experiments on the ImageNet dataset, using a variety of model architectures, demonstrate that perturbations generated from aligned source models exhibit significantly higher transferability than those from the original source model. Our source code is available at ..作者: Simulate 時(shí)間: 2025-3-28 11:14 作者: 懶惰人民 時(shí)間: 2025-3-28 17:03
,Factorizing Text-to-Video Generation by?Explicit Image Conditioning,. Nvidia’s PYOCO, and . vs. Meta’s Make-A-Video. Our model outperforms commercial solutions such as RunwayML’s Gen2 and Pika Labs. Finally, our factorizing approach naturally lends itself to animating images based on a user’s text prompt, where our generations are preferred . over prior work.作者: 提名的名單 時(shí)間: 2025-3-28 22:49
,MobileDiffusion: Instant Text-to-Image Generation on?Mobile Devices, the?base model. Empirical studies, conducted both quantitatively?and qualitatively, demonstrate the effectiveness of our proposed technologies. With them, MobileDiffusion achieves instant text-to-image generation on mobile devices, establishing a new?state of the art.作者: NEX 時(shí)間: 2025-3-29 01:00
,Generating Physically Realistic and?Directable Human Motions from?Multi-modal Inputs,on. For example, the input may come from a VR controller providing arm motion and body velocity, partial key-point animation, computer vision applied to videos, or even higher-level motion goals. This requires a versatile low-level humanoid controller that can handle such sparse, under-specified gui作者: PLIC 時(shí)間: 2025-3-29 03:22
,CoTracker: It Is Better to?Track Together,oaches that track points independently, CoTracker tracks them jointly, accounting for?their dependencies. We show that joint tracking significantly improves tracking accuracy and robustness, and allows CoTracker to?track occluded points and points outside of the camera view. We?also introduce severa作者: 名字 時(shí)間: 2025-3-29 10:44 作者: 他日關(guān)稅重重 時(shí)間: 2025-3-29 13:05 作者: Ingest 時(shí)間: 2025-3-29 15:41
,Improving Adversarial Transferability via?Model Alignment,alignment technique aimed at improving a given source model’s ability in generating transferable adversarial perturbations. During the alignment process, the parameters of the source model are fine-tuned to minimize an alignment loss. This loss measures the divergence in the predictions between the 作者: 來(lái)這真柔軟 時(shí)間: 2025-3-29 23:29 作者: Exuberance 時(shí)間: 2025-3-30 00:51
,ADen: Adaptive Density Representations for?Sparse-View Camera Pose Estimation,structions. Classic methods often depend on feature correspondence, such as keypoints, which require the input images to have large overlap and?small viewpoint changes. Such requirements present considerable challenges in scenarios with sparse views. Recent data-driven approaches aim to directly out作者: 欲望 時(shí)間: 2025-3-30 04:49
,Embodied Understanding of?Driving Scenarios,standing is typically founded upon Vision-Language Models (VLMs). Nevertheless, existing VLMs are restricted to the 2D domain, devoid of spatial awareness and long-horizon extrapolation proficiencies. We revisit the key aspects of autonomous driving and formulate appropriate rubrics. Hereby, we intr作者: olfction 時(shí)間: 2025-3-30 11:41
,Learning to?Drive via?Asymmetric Self-Play,data alone. The majority of driving data is uninteresting, and deliberately collecting?new long-tail scenarios is expensive and unsafe. We propose asymmetric self-play to scale beyond real?data with additional ., and . synthetic scenarios. Our approach pairs a teacher that learns to generate scenari作者: Control-Group 時(shí)間: 2025-3-30 14:31
,OpenIns3D: Snap and?Lookup for?3D Open-Vocabulary Instance Segmentation,k-Snap-Lookup” scheme. The “Mask” module learns class-agnostic mask proposals in 3D point clouds, the “Snap” module generates synthetic scene-level images at multiple scales and leverages 2D vision-language models to extract interesting objects, and the “Lookup” module searches through the outcomes 作者: 誘導(dǎo) 時(shí)間: 2025-3-30 19:17 作者: FLAIL 時(shí)間: 2025-3-31 00:46 作者: Condense 時(shí)間: 2025-3-31 02:07 作者: Offensive 時(shí)間: 2025-3-31 08:03
Open-Set Biometrics: Beyond Good Closed-Set Models,cations involve open-set biometrics, where probe subjects may or may not be present in the gallery. This poses distinct challenges in effectively distinguishing individuals in the gallery while minimizing false detections. While it is commonly believed that powerful biometric models can excel in bot作者: Limerick 時(shí)間: 2025-3-31 12:14 作者: 比喻好 時(shí)間: 2025-3-31 16:48
,Which Model Generated This Image? A Model-Agnostic Approach for?Origin Attribution,to identify the origin model?that generates them. In this work, we study the origin attribution?of generated images in a practical setting where only a few images generated by a source model are available and the source?model cannot be accessed. The goal is to check if a given image?is generated by 作者: 我們的面粉 時(shí)間: 2025-3-31 19:48 作者: Rinne-Test 時(shí)間: 2025-3-31 23:49 作者: Ascendancy 時(shí)間: 2025-4-1 02:33 作者: 金哥占卜者 時(shí)間: 2025-4-1 06:04
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242330.jpg作者: Overstate 時(shí)間: 2025-4-1 12:02
https://doi.org/10.1007/978-3-642-18967-8on. For example, the input may come from a VR controller providing arm motion and body velocity, partial key-point animation, computer vision applied to videos, or even higher-level motion goals. This requires a versatile low-level humanoid controller that can handle such sparse, under-specified gui作者: CULP 時(shí)間: 2025-4-1 17:14 作者: 高度表 時(shí)間: 2025-4-1 22:33
