作者: 雄偉 時(shí)間: 2025-3-21 20:20
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242334.jpg作者: HAWK 時(shí)間: 2025-3-22 04:01
https://doi.org/10.1007/978-3-031-72946-1artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer 作者: 正式演說(shuō) 時(shí)間: 2025-3-22 07:01 作者: Ibd810 時(shí)間: 2025-3-22 10:03
Transformations in the Workshop,swer this question with a clear . – VLMs introduce closed-set assumptions via their finite query set, making them vulnerable to open-set conditions. We systematically evaluate VLMs for open-set recognition and find they frequently misclassify objects not contained in their query set, leading to alar作者: 盟軍 時(shí)間: 2025-3-22 13:58 作者: 盟軍 時(shí)間: 2025-3-22 20:44 作者: minimal 時(shí)間: 2025-3-23 01:06 作者: Axon895 時(shí)間: 2025-3-23 04:10 作者: Thyroxine 時(shí)間: 2025-3-23 07:18 作者: 煩躁的女人 時(shí)間: 2025-3-23 09:51 作者: Ptosis 時(shí)間: 2025-3-23 14:28
Gerhard B?cker Dipl.-Vw.,Jutta Schmitz M. A.segmentation and depth estimation. We focus on fine-tuning the Stable Diffusion model, which has demonstrated impressive abilities in modeling image details and high-level semantics. Through our experiments, we have three key insights. Firstly, we demonstrate that for dense prediction tasks, the den作者: 用手捏 時(shí)間: 2025-3-23 18:38
Rentenanpassung und Altersarmutapacity of a foundation model can alleviate the cross-domain generalization problem. The main challenge of incorporating a foundation model into stereo matching pipeline lies in the absence of an effective forward process from single-view coarse-grained tokens to cross-view fine-grained cost represe作者: figment 時(shí)間: 2025-3-23 22:54 作者: HALL 時(shí)間: 2025-3-24 03:25
https://doi.org/10.1007/978-3-531-90416-0 essential for cancer diagnosis, staging and treatment planning. Finding scatteredly distributed, low-contrast clinically relevant LNs in 3D CT is difficult even for experienced physicians under high inter-observer variations. Previous automatic LN detection typically yields limited recall and high 作者: gimmick 時(shí)間: 2025-3-24 09:35
Geschlechter — Lebenslagen — AlternHowever, existing schemes for 3D scene editing still exhibit certain shortcomings, hindering their further interactive design. Such schemes typically adhere to fixed input patterns, limiting users’ flexibility in text input. Moreover, their editing capabilities are constrained by a single or a few 2作者: 兩種語(yǔ)言 時(shí)間: 2025-3-24 11:38
Christine Hartmann,Marcus Hillingeritates a laborious model architecture design. This not only consumes substantial time and effort but also disregards valuable insights from successful existing VSR models. Furthermore, the resource-intensive process of retraining these newly designed models exacerbates the challenge. In this paper, 作者: flaunt 時(shí)間: 2025-3-24 17:36 作者: abracadabra 時(shí)間: 2025-3-24 22:47 作者: troponins 時(shí)間: 2025-3-25 00:30 作者: Ingredient 時(shí)間: 2025-3-25 06:31
Hanna Zieschang,Dietmar Br?unigs. Particularly noteworthy is the challenge posed by catastrophic overfitting (CO) in this field. Although existing FAT approaches have made strides in mitigating CO, the ascent of adversarial robustness occurs with a non-negligible decline in classification accuracy on clean samples. To tackle this作者: Detonate 時(shí)間: 2025-3-25 07:44 作者: 甜食 時(shí)間: 2025-3-25 13:37
https://doi.org/10.1007/978-3-658-37883-7ng solely on text prompts often results in ambiguous frame composition due to spatial uncertainty. The research community thus leverages the . structure signals, ., per-frame depth/edge sequences to enhance controllability, whose collection accordingly increases the burden of inference. In this work作者: Morose 時(shí)間: 2025-3-25 17:03
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; motion estimation...?.作者: Juvenile 時(shí)間: 2025-3-25 19:58
0302-9743 n; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation...?.978-3-031-72945-4978-3-031-72946-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Aesthete 時(shí)間: 2025-3-26 00:24 作者: employor 時(shí)間: 2025-3-26 08:10
Das Altern im Spiegelbild der Stammzellenus activities of multiple users. We exploit WiMANS to benchmark the performance of state-of-the-art WiFi-based human sensing models and video-based models, posing new challenges and opportunities for future work. We believe WiMANS can push the boundaries of current studies and catalyze the research on WiFi-based multi-user sensing.作者: 沉積物 時(shí)間: 2025-3-26 08:55 作者: META 時(shí)間: 2025-3-26 16:04 作者: invert 時(shí)間: 2025-3-26 17:29 作者: GILD 時(shí)間: 2025-3-27 00:21 作者: conscience 時(shí)間: 2025-3-27 05:01
Das Altern im Spiegelbild der Stammzellenn. Additionally, UDI performs competitively in low-shot image classification, improving the scalability of joint-embedding pipelines. Various visualizations and ablation studies are presented to further elucidate the mechanisms behind UDI. Our source code is available at ..作者: Paradox 時(shí)間: 2025-3-27 08:56
Peter K. Plinkert,Mark Praetoriusthe prediction module to calculate the task loss. As a result, the direction constraint from the loss minimization is blocked by the sampled representation. This relaxes the constraint on the inference representation and enables the model to capture the specific information for different modality co作者: lethal 時(shí)間: 2025-3-27 12:22 作者: 從屬 時(shí)間: 2025-3-27 14:03
Altern gestalten - Medizin, Technik, Umwelt state-of-the-art performance with the efficient computation compared to the existing transformer-based semantic segmentation models in three public benchmarks, including ADE20K, Cityscapes and COCO-Stuff. Furthermore, our ISR method reduces the computational cost by up to 61% with minimal mIoU perf作者: 修剪過(guò)的樹(shù)籬 時(shí)間: 2025-3-27 17:57
Ingo Bode,Felix Wilke M. A., Dipl.Soz.ive pipeline, we effortlessly scale our dataset up to 300 million samples named VeCap dataset. Our results show significant advantages in image-text alignment and overall model performance. For example, VeCLIP achieves up to . gain in COCO and Flickr30k retrieval tasks under the 12M setting. For dat作者: inculpate 時(shí)間: 2025-3-28 01:22
Gerhard B?cker Dipl.-Vw.,Jutta Schmitz M. A.ve features for transfer learning to downstream tasks. Thirdly, we find that tuning Stable Diffusion to downstream tasks in a parameter-efficient way is feasible. We first extensively investigate currently popular parameter-efficient tuning methods. Then we search for the best protocol for effective作者: MEEK 時(shí)間: 2025-3-28 03:06 作者: 友好關(guān)系 時(shí)間: 2025-3-28 08:09
Ingo Bode,Felix Wilke M. A., Dipl.Soz.esholding Algorithm. Then, the U-shape SNN decoder reconstructs the video based on the encoded spikes. Experimental results demonstrate that the STLR achieves performance comparable to popular SNNs on IJRR, HQF, and MVSEC datasets while significantly enhancing energy efficiency.作者: transplantation 時(shí)間: 2025-3-28 12:27 作者: charisma 時(shí)間: 2025-3-28 17:59 作者: mosque 時(shí)間: 2025-3-28 21:02
Christine Hartmann,Marcus Hillingerevent stream in a coarse-to-fine manner, while the EAF unit efficiently fuses frames with the event stream through a multi-scale design. Thanks to both units, EATER outperforms the full fine-tuning approach with parameter efficiency, as demonstrated by comprehensive experiments.作者: 陰謀 時(shí)間: 2025-3-29 00:28 作者: CANE 時(shí)間: 2025-3-29 04:06 作者: 協(xié)議 時(shí)間: 2025-3-29 07:46 作者: Curmudgeon 時(shí)間: 2025-3-29 14:06
Hanna Zieschang,Dietmar Br?unige for this observation. Notably, models trained stably with these terms exhibit superior performance compared to prior FAT work. On this basis, we harness CO to achieve ‘a(chǎn)ttack obfuscation’, aiming to bolster model performance. Consequently, the models suffering from CO can attain optimal classifica作者: 跟隨 時(shí)間: 2025-3-29 18:42 作者: 先兆 時(shí)間: 2025-3-29 21:53 作者: Affirm 時(shí)間: 2025-3-30 02:07
,Unsqueeze , Bottleneck to?Learn Rich Representations,n. Additionally, UDI performs competitively in low-shot image classification, improving the scalability of joint-embedding pipelines. Various visualizations and ablation studies are presented to further elucidate the mechanisms behind UDI. Our source code is available at ..作者: Indicative 時(shí)間: 2025-3-30 04:13 作者: 敬禮 時(shí)間: 2025-3-30 12:01 作者: 史前 時(shí)間: 2025-3-30 12:29
,Embedding-Free Transformer with?Inference Spatial Reduction for?Efficient Semantic Segmentation, state-of-the-art performance with the efficient computation compared to the existing transformer-based semantic segmentation models in three public benchmarks, including ADE20K, Cityscapes and COCO-Stuff. Furthermore, our ISR method reduces the computational cost by up to 61% with minimal mIoU perf作者: sterilization 時(shí)間: 2025-3-30 19:10
,VeCLIP: Improving CLIP Training via?Visual-Enriched Captions,ive pipeline, we effortlessly scale our dataset up to 300 million samples named VeCap dataset. Our results show significant advantages in image-text alignment and overall model performance. For example, VeCLIP achieves up to . gain in COCO and Flickr30k retrieval tasks under the 12M setting. For dat作者: 骯臟 時(shí)間: 2025-3-30 23:07 作者: 腐蝕 時(shí)間: 2025-3-31 03:42
,Learning Representations from?Foundation Models for?Domain Generalized Stereo Matching,opose a cosine-constrained concatenation cost (C4) space to construct cost volumes. We integrate FormerStereo with state-of-the-art (SOTA) stereo matching networks and evaluate its effectiveness on multiple benchmark datasets. Experiments show that the FormerStereo framework effectively improves the作者: Arboreal 時(shí)間: 2025-3-31 08:13
,Spike-Temporal Latent Representation for?Energy-Efficient Event-to-Video Reconstruction,esholding Algorithm. Then, the U-shape SNN decoder reconstructs the video based on the encoded spikes. Experimental results demonstrate that the STLR achieves performance comparable to popular SNNs on IJRR, HQF, and MVSEC datasets while significantly enhancing energy efficiency.作者: monologue 時(shí)間: 2025-3-31 10:44 作者: Torrid 時(shí)間: 2025-3-31 17:02
,Chat-Edit-3D: Interactive 3D Scene Editing via?Text Prompts,rmore, we design a scheme utilizing Hash-Atlas to represent 3D scene views, which transfers the editing of 3D scenes onto 2D atlas images. This design achieves complete decoupling between the 2D editing and 3D reconstruction processes, enabling . to flexibly integrate a wide range of existing 2D or 作者: 爵士樂(lè) 時(shí)間: 2025-3-31 21:12 作者: alcoholism 時(shí)間: 2025-4-1 01:15
,Look Hear: Gaze Prediction for?Speech-Directed Human Attention,rs, from 220 participants performing our referral task. In our quantitative and qualitative analyses, ART not only outperforms existing methods in scanpath prediction, but also appears to capture several human attention patterns, such as waiting, scanning, and verification. Code and dataset are avai作者: misanthrope 時(shí)間: 2025-4-1 01:49
,Raising the?Ceiling: Conflict-Free Local Feature Matching with?Dynamic View Switching, strategy. 3) By integrating the semi-sparse paradigm and the coarse-to-fine architecture, RCM preserves the benefits of both high efficiency and global search, mitigating the reliance on keypoint repeatability. As a result, RCM enables more matchable points in the source image to be matched in an e作者: Flawless 時(shí)間: 2025-4-1 07:34
,Q&A Prompts: Discovering Rich Visual Clues through Mining Question-Answer Prompts for?VQA requiringhe training set as inputs and outputs to train a visual question generation (VQG) model. Then, we use an image tagging model to identify various instances and send packaged image-tag pairs into the VQG model to generate relevant questions with the extracted image tags as answers. Finally, we encode 作者: paleolithic 時(shí)間: 2025-4-1 13:23
