作者: 茁壯成長 時間: 2025-3-21 20:47 作者: 的染料 時間: 2025-3-22 01:07 作者: 腐蝕 時間: 2025-3-22 06:39
,Learning to?Adapt SAM for?Segmenting Cross-Domain Point Clouds,of point clouds. Especially for LiDAR point clouds, the domain discrepancy becomes obvious across varying capture scenes, fluctuating weather conditions, and the diverse array of LiDAR devices in use. Inspired by the remarkable generalization capabilities exhibited by the vision foundation model, SA作者: 沒有準(zhǔn)備 時間: 2025-3-22 10:10 作者: 新義 時間: 2025-3-22 13:52
,ViewFormer: Exploring Spatiotemporal Modeling for?Multi-view 3D Occupancy Perception via?View-Guideround by quantifying the physical space into a grid map. The widely adopted projection-first deformable attention, efficient in transforming image features into 3D representations, encounters challenges in aggregating multi-view features due to sensor deployment constraints. To address this issue, w作者: 新義 時間: 2025-3-22 20:52 作者: COMA 時間: 2025-3-23 00:30 作者: 擴(kuò)張 時間: 2025-3-23 05:20 作者: Introduction 時間: 2025-3-23 07:54
,Contrastive Learning with?Counterfactual Explanations for?Radiology Report Generation,e automatic report generation models to learn entangled and spurious representations resulting in misdiagnostic reports. To tackle these, we propose a novel .unter.actual .xplanations-based framework (CoFE) for radiology report generation. Counterfactual explanations serve as a potent tool for under作者: CODA 時間: 2025-3-23 13:46 作者: Palter 時間: 2025-3-23 14:25 作者: 制定法律 時間: 2025-3-23 19:36 作者: 直覺好 時間: 2025-3-23 22:23
,Content-Aware Radiance Fields: Aligning Model Complexity with?Scene Intricacy Through Learned Bitwint 3D content by that training models for each individual scene. This unique characteristic of scene representation and per-scene training distinguishes radiance field models from other neural models, because complex scenes necessitate models with higher representational capacity and vice versa. In 作者: Dignant 時間: 2025-3-24 02:32 作者: 遵循的規(guī)范 時間: 2025-3-24 08:39 作者: Complement 時間: 2025-3-24 12:53
Event Camera Data Dense Pre-training,mera data. Our approach utilizes solely event data for training..Transferring achievements from dense RGB pre-training directly to event camera data yields subpar performance. This is attributed to the spatial sparsity inherent in an event image (converted from event data), where many pixels do not 作者: essential-fats 時間: 2025-3-24 16:48
,Distractors-Immune Representation Learning with?Cross-Modal Contrastive Regularization for?Change Cd viewpoint changes). Under these distractors, unchanged objects often appear pseudo changes about location and scale, and certain objects might overlap others, resulting in perturbational and discrimination-degraded features between two images. However, most existing methods directly capture the di作者: Ascribe 時間: 2025-3-24 21:54
Rethinking Image-to-Video Adaptation: An Object-Centric Perspective,mage-to-video adaptation paradigms use lightweight adapters for temporal modeling on top of the spatial module. However, these attempts are subject to limitations in efficiency and interpretability. In this paper, we propose a novel and efficient image-to-video adaptation strategy from the object-ce作者: sparse 時間: 2025-3-25 02:51 作者: 增強(qiáng) 時間: 2025-3-25 03:25 作者: DAMN 時間: 2025-3-25 09:34 作者: 不利 時間: 2025-3-25 13:28
Laura Kelly,Victoria Foster,Anne Hayesodel per task and use the REINFORCE?[.] algorithm to patch into a subset of them with a new query image. The resulting Task Vectors guide the model towards performing the task better than the original model. (For code and models see?.).作者: 祖?zhèn)髫敭a(chǎn) 時間: 2025-3-25 19:11
Finding Visual Task Vectors,odel per task and use the REINFORCE?[.] algorithm to patch into a subset of them with a new query image. The resulting Task Vectors guide the model towards performing the task better than the original model. (For code and models see?.).作者: LINES 時間: 2025-3-26 00:04
0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-72774-0978-3-031-72775-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 草率男 時間: 2025-3-26 01:21 作者: GILD 時間: 2025-3-26 06:23
The Attractiveness of Alternative Medicineouds to facilitate knowledge transfer and propose an innovative hybrid feature augmentation methodology, which enhances the alignment between the 3D feature space and SAM’s feature space, operating at both the scene and instance levels. Our method is evaluated on many widely-recognized datasets and achieves state-of-the-art performance.作者: 減去 時間: 2025-3-26 09:38 作者: probate 時間: 2025-3-26 14:21
Rethinking Peace and Conflict Studiest features..For training our framework, we curate a synthetic event camera dataset featuring diverse scene and motion patterns. Transfer learning performance on downstream dense prediction tasks illustrates the superiority of our method over state-of-the-art approaches.作者: 莊嚴(yán) 時間: 2025-3-26 17:03
,LLaVA-Grounding: Grounded Visual Chat with?Large Multimodal Models,upport GVC and various types of visual prompts by connecting segmentation models with language models. Experimental results demonstrate that our model outperforms other LMMs on Grounding-Bench. Furthermore, our model achieves competitive performance on classic grounding benchmarks like RefCOCO/+/g and Flickr30K Entities.作者: 兵團(tuán) 時間: 2025-3-26 22:42
,Learning to?Adapt SAM for?Segmenting Cross-Domain Point Clouds,ouds to facilitate knowledge transfer and propose an innovative hybrid feature augmentation methodology, which enhances the alignment between the 3D feature space and SAM’s feature space, operating at both the scene and instance levels. Our method is evaluated on many widely-recognized datasets and achieves state-of-the-art performance.作者: Restenosis 時間: 2025-3-27 02:59 作者: constellation 時間: 2025-3-27 09:04 作者: agonist 時間: 2025-3-27 10:42
,ShapeLLM: Universal 3D Object Understanding for?Embodied Interaction, data and tested on our newly human-curated benchmark, 3D MM-Vet. .?and .?achieve state-of-the-art performance in 3D geometry understanding and language-unified 3D interaction tasks, such as embodied visual grounding.作者: ATRIA 時間: 2025-3-27 17:16 作者: forecast 時間: 2025-3-27 20:35
https://doi.org/10.1057/9781137462565stion, these methods show advancement in leveraging Large Language Models (LLMs) for complex problem-solving. Despite their potential, existing VP methods generate all code in a single function, which does not fully utilize LLM’s reasoning capacity and the modular adaptability of code. This results 作者: VEIL 時間: 2025-3-27 22:31 作者: 不如屎殼郎 時間: 2025-3-28 05:21
Ethical Problems in Alternative Medicinecross diverse range of downstream tasks and domains. With the emergence of such powerful models, it has become crucial to effectively leverage their capabilities in tackling challenging vision tasks. On the other hand, only a few works have focused on devising adversarial examples that transfer well作者: Deference 時間: 2025-3-28 06:41 作者: Nmda-Receptor 時間: 2025-3-28 12:51
R. H. Schneider,J. W. Salerno,S. I. Nidich enhancement network that is capable of predicting clean and full measurements from noisy partial observations. We leverage a denoising autoencoder scheme to acquire rich and noise-robust representations in the measurement space. Through this pipeline, our enhancement network is trained to accuratel作者: figurine 時間: 2025-3-28 15:53
Unconventional Western Medicineround by quantifying the physical space into a grid map. The widely adopted projection-first deformable attention, efficient in transforming image features into 3D representations, encounters challenges in aggregating multi-view features due to sensor deployment constraints. To address this issue, w作者: –scent 時間: 2025-3-28 22:45
https://doi.org/10.1007/978-3-642-60037-1ng-form videos. Given the diverse nature of generic boundaries, spanning different video appearances, objects, and actions, this task remains challenging. Existing methods usually detect various boundaries by the same protocol, regardless of their distinctive characteristics and detection difficulti作者: 陳舊 時間: 2025-3-29 00:23
https://doi.org/10.1007/978-3-642-60037-1urrent works are usually carried out separately on small datasets thus lacking generalization ability. Through rigorous evaluation of diverse benchmarks, we demonstrate the shortcomings of existing ad-hoc methods in achieving cross-domain reasoning and their tendency to data bias fitting. In this pa作者: 上腭 時間: 2025-3-29 03:29 作者: 是貪求 時間: 2025-3-29 10:15
Traditionelle chinesische Medizine automatic report generation models to learn entangled and spurious representations resulting in misdiagnostic reports. To tackle these, we propose a novel .unter.actual .xplanations-based framework (CoFE) for radiology report generation. Counterfactual explanations serve as a potent tool for under作者: 熱心 時間: 2025-3-29 15:11 作者: 積極詞匯 時間: 2025-3-29 18:59 作者: 詞匯記憶方法 時間: 2025-3-29 21:44
https://doi.org/10.1057/9781137476821ding with 3D point clouds and languages. .?is built upon an improved 3D encoder by extending .?[.] to .?that benefits from multi-view image distillation for enhanced geometry understanding. By utilizing .?as the 3D point cloud input encoder for LLMs, .?is trained on constructed instruction-following作者: Pericarditis 時間: 2025-3-30 02:06 作者: Inordinate 時間: 2025-3-30 04:03
Laura Kelly,Victoria Foster,Anne Hayese analyze the activations of MAE-VQGAN, a recent Visual Prompting model?[.], and find ., activations that encode task-specific information. Equipped with this insight, we demonstrate that it is possible to identify the Task Vectors and use them to guide the network towards performing different tasks作者: 仔細(xì)閱讀 時間: 2025-3-30 11:11 作者: Coronation 時間: 2025-3-30 13:39
Rethinking Peace and Conflict Studiesmera data. Our approach utilizes solely event data for training..Transferring achievements from dense RGB pre-training directly to event camera data yields subpar performance. This is attributed to the spatial sparsity inherent in an event image (converted from event data), where many pixels do not 作者: FRAX-tool 時間: 2025-3-30 16:51 作者: JEER 時間: 2025-3-30 21:48 作者: 欺騙手段 時間: 2025-3-31 02:00
Computer Vision – ECCV 2024978-3-031-72775-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Anonymous 時間: 2025-3-31 08:51
https://doi.org/10.1007/978-3-031-72775-7artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer 作者: 障礙 時間: 2025-3-31 10:19 作者: Initiative 時間: 2025-3-31 14:10
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242341.jpg作者: 豪華 時間: 2025-3-31 17:48 作者: 完整 時間: 2025-3-31 22:13 作者: 腐敗 時間: 2025-4-1 04:20
,Learning to?Enhance Aperture Phasor Field for?Non-Line-of-Sight Imaging,nals are detected. The phasor wavefronts at the aperture, which are band-limited signals, are employed as inputs and outputs of the network, guiding our network to learn from the frequency range of interests and discard unnecessary information. The experimental results in more practical acquisition 作者: 不安 時間: 2025-4-1 07:00 作者: 抒情短詩 時間: 2025-4-1 13:36
,Fine-Grained Dynamic Network for?Generic Event Boundary Detection,Besides, a multi-order difference detector is also proposed to ensure generic boundaries can be effectively identified and adaptively processed. Extensive experiments on the challenging Kinetics-GEBD and TAPOS datasets demonstrate that adopting the dynamic strategy significantly benefits GEBD tasks,作者: Seminar 時間: 2025-4-1 17:12 作者: 懶洋洋 時間: 2025-4-1 21:55
,AlignZeg: Mitigating Objective Misalignment for?Zero-Shot Semantic Segmentation,allocate a more generalizable feature space. . During the inference stage, AlignZeg uses a class indicator to find potential unseen class proposals followed by a prediction postprocess to correct the prediction bias. Experiments demonstrate that AlignZeg markedly enhances zero-shot semantic segmenta
