作者: 浮雕 時間: 2025-3-21 21:16 作者: 外形 時間: 2025-3-22 01:20
https://doi.org/10.1007/978-3-031-72664-4artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer 作者: 圓錐體 時間: 2025-3-22 05:12 作者: Obstruction 時間: 2025-3-22 09:36 作者: 強(qiáng)制令 時間: 2025-3-22 15:47
Ambulanzmanual P?diatrie von A-Zhe same feature to achieve recognition invariance. However, visual recognition involves not only identifying . an object is but also understanding . it is presented. For example, seeing a car from the side versus head-on is crucial for deciding whether to stay put or jump out of the way. While unsup作者: 強(qiáng)制令 時間: 2025-3-22 18:41
https://doi.org/10.1007/978-3-642-24683-8s to the success of CLIP and its variants. Several works have also used CLIP features for dense prediction tasks and have shown the emergence of open-set abilities. However, the contrastive objective used by these models only focuses on image-text alignment and does not incentivise image feature lea作者: 多節(jié) 時間: 2025-3-22 23:07
Ambulanzmanual P?diatrie von A-Zwever, most previous approaches have struggled to accurately predict motions that are both realistic and coherent with past motion due to a lack of guidance on the latent distribution. In this paper, we introduce Semantic Latent Directions (SLD) as a solution to this challenge, aiming to constrain t作者: Constituent 時間: 2025-3-23 01:31 作者: 離開真充足 時間: 2025-3-23 09:03
Ambulanzmanual P?diatrie von A-Zr the generation process through user queries) and . (.. visually grounding their predictions), which we deem essential for future adoption in clinical practice. While there have been efforts to tackle these issues, they are either limited in their interactivity by not supporting textual queries or 作者: 寬敞 時間: 2025-3-23 09:55
Ambulanzmanual P?diatrie von A-Znd navigation within an environment. While modern AVE methods have demonstrated impressive performance, they are constrained to fixed-scale glimpses from rigid grids. In contrast, existing mobile platforms equipped with optical zoom capabilities can capture glimpses of arbitrary positions and scales作者: BOOST 時間: 2025-3-23 15:31 作者: crumble 時間: 2025-3-23 20:26
https://doi.org/10.1007/978-3-642-24683-8y, we aim to leverage the long sequence modeling capability of a State-Space Model called Mamba to extend its applicability to visual data generation. Firstly, we identify a critical oversight in most current Mamba-based vision methods, namely the lack of consideration for spatial continuity in the 作者: BRIDE 時間: 2025-3-24 00:08
Ambulanzmanual P?diatrie von A-Zusion model that dynamically adapts to scene graphs. Existing methods struggle to handle scene graphs due to varying numbers of nodes, multiple edge combinations, and manipulator-induced node-edge operations. EchoScene overcomes this by associating each node with a denoising process and enables coll作者: 胎兒 時間: 2025-3-24 02:42 作者: 緊張過度 時間: 2025-3-24 07:17
Ambulanzmanual P?diatrie von A-Zn Localization (OnTAL), extend this approach to instance-level predictions. However, existing methods mainly focus on short-term context, neglecting historical information. To address this, we introduce the History-Augmented Anchor Transformer (HAT) Framework for OnTAL. By integrating historical con作者: fiscal 時間: 2025-3-24 11:04
https://doi.org/10.1007/978-3-642-24683-8 deep net to learn higher-level representations. Contrary to this motivation, we hypothesize that the discarded activations are useful and can be incorporated on the fly to improve models’ prediction. To validate our hypothesis, we propose a search and aggregate method to find useful activation maps作者: foliage 時間: 2025-3-24 15:23 作者: 改變 時間: 2025-3-24 22:27
Ambulanzmanual P?diatrie von A-Z Recent efforts tackle this challenge by adopting an analysis-by-synthesis paradigm to learn 3D reconstruction with only 2D annotations. However, existing methods face limitations in both shape reconstruction and texture generation. This paper introduces an innovative Analysis-by-Synthesis Transform作者: 相一致 時間: 2025-3-25 01:17
Ambulanzmanual P?diatrie von A-Zs after training. This leads to the problem of . (MU), aiming to eliminate the influence of chosen data points on model performance, while still maintaining the model’s utility post-unlearning. Despite various MU methods for data influence erasure, evaluations have largely focused on . data forgetti作者: 評論性 時間: 2025-3-25 03:37
Ambulanzmanual P?diatrie von A-Zplored. Existing approaches demonstrate proficiency in transferring colors and textures but often struggle with replicating the geometry of the scenes. In our work, we leverage an explicit Gaussian Scale (GS) representation and directly match the distributions of Gaussians between style and content 作者: 機(jī)制 時間: 2025-3-25 11:16 作者: sinoatrial-node 時間: 2025-3-25 15:21 作者: 猛然一拉 時間: 2025-3-25 16:52 作者: debunk 時間: 2025-3-25 23:18 作者: grandiose 時間: 2025-3-26 01:43
Ambulanzmanual P?diatrie von A-Z 9 chest X-ray tasks, including localized image interpretation and report generation, showcase its competitiveness with SOTA models while additional analysis demonstrates ChEX’s interactive capabilities. Code: ..作者: 擁護(hù)者 時間: 2025-3-26 04:22 作者: agnostic 時間: 2025-3-26 09:10 作者: 最后一個 時間: 2025-3-26 14:04 作者: FECT 時間: 2025-3-26 19:15
,ChEX: Interactive Localization and?Region Description in?Chest X-Rays, 9 chest X-ray tasks, including localized image interpretation and report generation, showcase its competitiveness with SOTA models while additional analysis demonstrates ChEX’s interactive capabilities. Code: ..作者: Trypsin 時間: 2025-3-26 21:02 作者: 珠寶 時間: 2025-3-27 04:28 作者: 胖人手藝好 時間: 2025-3-27 06:02
,HAT: History-Augmented Anchor Transformer for?Online Temporal Action Localization,UMOS and MUSES). Results show that our model outperforms state-of-the-art approaches significantly on PREGO datasets and achieves comparable or slightly superior performance on non-PREGO datasets, underscoring the importance of leveraging long-term history, especially in procedural and egocentric action scenarios. Code is available at: ..作者: 圍裙 時間: 2025-3-27 09:33 作者: interpose 時間: 2025-3-27 16:31
https://doi.org/10.1007/978-3-642-24683-8rent architectures on ImageNet, CityScapes, and ADE20K show that our method consistently improves model test-time performance. Additionally, it complements existing test-time augmentation techniques to provide further performance gains.作者: FAZE 時間: 2025-3-27 19:46
,Leveraging Temporal Contextualization for?Video Action Recognition,odule processes context tokens to generate informative prompts in the text modality. Extensive experiments in zero-shot, few-shot, base-to-novel, and fully-supervised action recognition validate the effectiveness of our model. Ablation studies for TC and VP support our design choices. Our project page with the source code is available at ..作者: refraction 時間: 2025-3-28 01:38
,Deep Nets with?Subsampling Layers Unwittingly Discard Useful Activations at?Test-Time,rent architectures on ImageNet, CityScapes, and ADE20K show that our method consistently improves model test-time performance. Additionally, it complements existing test-time augmentation techniques to provide further performance gains.作者: 無能性 時間: 2025-3-28 04:13
Conference proceedings 2025orcement learning; 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: 手銬 時間: 2025-3-28 08:10
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. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinf作者: Deference 時間: 2025-3-28 13:51 作者: 圓錐體 時間: 2025-3-28 18:07 作者: Thyroid-Gland 時間: 2025-3-28 22:11
,SILC: Improving Vision Language Pretraining with?Self-distillation,ion, while also providing improvements on image-level tasks such as classification and retrieval. SILC models sets a new state of the art for zero-shot classification, few shot classification, image and text retrieval, zero-shot segmentation, and open vocabulary segmentation. We further show that SI作者: anthesis 時間: 2025-3-28 23:51
,Learning Semantic Latent Directions for?Accurate and?Controllable Human Motion Prediction,ting the coefficients of the latent directions during the inference phase. Expanding on SLD, we introduce a set of motion queries to enhance the diversity of predictions. By aligning these motion queries with the SLD space, SLD is further promoted to more accurate and coherent motion predictions. Th作者: 出汗 時間: 2025-3-29 06:58
,CLAP: Isolating Content from?Style Through Contrastive Learning with?Augmented Prompts, to isolate latent content from style features. This enables CLIP-like model’s encoders to concentrate on latent content information, refining the learned representations by pre-trained CLIP-like models. Our extensive experiments across diverse datasets demonstrate significant improvements in zero-s作者: Chronic 時間: 2025-3-29 11:17 作者: GULLY 時間: 2025-3-29 14:59 作者: JADED 時間: 2025-3-29 16:59
SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries,sible maneuvers while all agents in the scene exhibit reactive and realistic behaviors. Furthermore, we propose novel guidance objectives and a partial diffusion process that enables users to control key aspects of the scenarios, such as the collision type and aggressiveness of the adversarial agent作者: Desert 時間: 2025-3-29 21:53
,Analysis-by-Synthesis Transformer for?Single-View 3D Reconstruction, eliminates the incorrect inductive bias. Experimental results on CUB-200-2011 and ShapeNet datasets demonstrate superior performance in shape reconstruction and texture generation compared to previous methods. The code is available at ..作者: 子女 時間: 2025-3-30 03:18
,Challenging Forgets: Unveiling the?Worst-Case Forget Sets in?Machine Unlearning,r optimization level to emulate worst-case scenarios, while simultaneously engaging in standard training and unlearning at the lower level, achieving a balance between data influence erasure and model utility. Our proposal offers a worst-case evaluation of MU’s resilience and effectiveness. Through 作者: Resistance 時間: 2025-3-30 06:13 作者: HATCH 時間: 2025-3-30 09:10
,SCLIP: Rethinking Self-Attention for?Dense Vision-Language Inference, modification to CLIP significantly enhances its capability in dense prediction, improving the original CLIP’s 14.1% average zero-shot mIoU over eight semantic segmentation benchmarks to 38.2%, and outperforming the existing SoTA’s 33.9% by a large margin. Code is available at ..作者: 怕失去錢 時間: 2025-3-30 15:32
,Flying with?Photons: Rendering Novel Views of?Propagating Light,n. Additionally, we demonstrate removing viewpoint-dependent propagation delays using a time warping procedure, rendering of relativistic effects, and video synthesis of direct and global components of light transport.作者: 套索 時間: 2025-3-30 16:39
https://doi.org/10.1007/978-3-642-41893-81) stealthy and model-agnostic watermarks; 2) minimal impact on the target task; 3) irrefutable evidence of misuse; and 4) improved applicability in practical scenarios. We validate these benefits through extensive experiments and extend our method to fine-grained classification and image segmentati作者: Distribution 時間: 2025-3-30 21:52
Ambulanzmanual P?diatrie von A-Zory regularization loss for learning features from unlabeled image triplets. Our experiments demonstrate that this approach helps develop a visual representation that encodes object identity and organizes objects by their poses, retaining semantic classification accuracy while achieving emergent glo作者: 戰(zhàn)勝 時間: 2025-3-31 03:19 作者: Infraction 時間: 2025-3-31 08:12 作者: Nutrient 時間: 2025-3-31 12:12
https://doi.org/10.1007/978-3-642-24683-8 to isolate latent content from style features. This enables CLIP-like model’s encoders to concentrate on latent content information, refining the learned representations by pre-trained CLIP-like models. Our extensive experiments across diverse datasets demonstrate significant improvements in zero-s作者: Apogee 時間: 2025-3-31 16:57
Ambulanzmanual P?diatrie von A-Zene graph, facilitating the generation of globally coherent scenes. The resulting scenes can be manipulated during inference by editing the input scene graph and sampling the noise in the diffusion model. Extensive experiments validate our approach, which maintains scene controllability and surpasse作者: temperate 時間: 2025-3-31 17:38
