作者: conflate 時(shí)間: 2025-3-21 21:06 作者: ungainly 時(shí)間: 2025-3-22 04:05 作者: Antimicrobial 時(shí)間: 2025-3-22 07:24
Measuring Savings of International Reservescompared to existing part datasets (excluding datasets of humans). It can be utilized for many vision tasks including Object Segmentation, Semantic Part Segmentation, Few-shot Learning and Part Discovery. We conduct comprehensive experiments which study these tasks and set up a set of baselines.作者: 單色 時(shí)間: 2025-3-22 09:48 作者: Ancestor 時(shí)間: 2025-3-22 12:58
The Economics of International Securitybe generalized for 3D facial depth/normal estimation. The proposed network consists of two novel modules: Adaptive Sampling Module (ASM) and Adaptive Normal Module (ANM), which are specialized in handling the defocus blur in DP images. Finally, we demonstrate that the proposed method achieves state-作者: Ancestor 時(shí)間: 2025-3-22 20:20 作者: 退潮 時(shí)間: 2025-3-23 00:33 作者: JEER 時(shí)間: 2025-3-23 01:32 作者: 楓樹 時(shí)間: 2025-3-23 06:05 作者: 鬧劇 時(shí)間: 2025-3-23 11:05
https://doi.org/10.1007/978-1-349-02606-7he multiple moving cameras recording setup. We adopt a hybrid labelling pipeline leveraging deep estimation models as well as manual annotations to obtain good quality keypoint sequences at a reduced cost. Our efforts produced the BRACE dataset, which contains over 3?h and 30?min of densely annotate作者: 能夠支付 時(shí)間: 2025-3-23 17:00
,ECCV Caption: Correcting False Negatives by?Collecting Machine-and-Human-verified Image-Caption Asscall@K (R@K). We re-evaluate the existing 25 VL models on existing and proposed benchmarks. Our findings are that the existing benchmarks, such as COCO 1K R@K, COCO 5K R@K, CxC R@1 are highly correlated with each other, while the rankings change when we shift to the ECCV mAP@R. Lastly, we delve into作者: Noctambulant 時(shí)間: 2025-3-23 20:38 作者: Allege 時(shí)間: 2025-3-24 00:45 作者: 持續(xù) 時(shí)間: 2025-3-24 05:03
,PartImageNet: A Large, High-Quality Dataset of?Parts,compared to existing part datasets (excluding datasets of humans). It can be utilized for many vision tasks including Object Segmentation, Semantic Part Segmentation, Few-shot Learning and Part Discovery. We conduct comprehensive experiments which study these tasks and set up a set of baselines.作者: 許可 時(shí)間: 2025-3-24 09:04
,A-OKVQA: A Benchmark for?Visual Question Answering Using World Knowledge,the image. We demonstrate the potential of this new dataset through a detailed analysis of its contents and baseline performance measurements over a variety of state-of-the-art vision–language models.作者: Melatonin 時(shí)間: 2025-3-24 12:02 作者: 詼諧 時(shí)間: 2025-3-24 15:01 作者: CREST 時(shí)間: 2025-3-24 20:01
,FS-COCO: Towards Understanding of?Freehand Sketches of?Common Objects in?Context,s the potential benefit of combining the two modalities. In addition, we extend a popular vector sketch LSTM-based encoder to handle sketches with larger complexity than was supported by previous work. Namely, we propose a hierarchical sketch decoder, which we leverage at a sketch-specific “pretext”作者: 舊式步槍 時(shí)間: 2025-3-24 23:53
,Exploring Fine-Grained Audiovisual Categorization with?the?SSW60 Dataset,ds is better than using exclusively image or audio based methods for the task of video classification. We also present interesting modality transfer experiments, enabled by the unique construction of SSW60 to encompass three different modalities. We hope the SSW60 dataset and accompanying baselines 作者: Inertia 時(shí)間: 2025-3-25 06:14 作者: 青春期 時(shí)間: 2025-3-25 11:07 作者: Ancillary 時(shí)間: 2025-3-25 12:09
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; reinforce作者: eucalyptus 時(shí)間: 2025-3-25 15:49
,A Real World Dataset for?Multi-view 3D Reconstruction,priate real world benchmark for the task and demonstrate that our dataset can fill that gap. The entire annotated dataset along with the source code for the annotation tools and evaluation baselines is available at ..作者: 抵制 時(shí)間: 2025-3-25 23:06
Conference proceedings 2022ing; 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: Accessible 時(shí)間: 2025-3-26 01:11 作者: uncertain 時(shí)間: 2025-3-26 05:47 作者: Blemish 時(shí)間: 2025-3-26 09:32 作者: 裂隙 時(shí)間: 2025-3-26 15:20 作者: 柳樹；枯黃 時(shí)間: 2025-3-26 18:16 作者: circuit 時(shí)間: 2025-3-26 22:25 作者: 開玩笑 時(shí)間: 2025-3-27 03:15 作者: 松軟無力 時(shí)間: 2025-3-27 05:30 作者: 擋泥板 時(shí)間: 2025-3-27 10:55 作者: 幼稚 時(shí)間: 2025-3-27 14:53
Development and Disarmament: The Meaningpending on the vision task. 2) Current approaches to enhance robustness have only marginal effects, and can even reduce robustness. 3) We do not observe significant differences between convolutional and transformer architectures. We believe our dataset provides a rich testbed to study robustness and will help push forward research in this area.作者: GRAVE 時(shí)間: 2025-3-27 19:17 作者: Ptosis 時(shí)間: 2025-3-28 00:56 作者: 佛刊 時(shí)間: 2025-3-28 06:04
,Capturing, Reconstructing, and?Simulating: The UrbanScene3D Dataset,ng boxes, and 3D point cloud/mesh segmentations, etc. The simulator with physical engine and lighting system not only produce variety of data but also enable users to simulate cars or drones in the proposed urban environment for future research. The dataset with aerial path planning and 3D reconstruction benchmark is available at: ..作者: arthroplasty 時(shí)間: 2025-3-28 09:58 作者: headlong 時(shí)間: 2025-3-28 10:56 作者: 罐里有戒指 時(shí)間: 2025-3-28 14:56 作者: 思想靈活 時(shí)間: 2025-3-28 21:02
,The Caltech Fish Counting Dataset: A Benchmark for?Multiple-Object Tracking and?Counting,rain MOT and counting algorithms and evaluate generalization performance at unseen test locations. We perform extensive baseline experiments and identify key challenges and opportunities for advancing the state of the art in generalization in MOT and counting.作者: critic 時(shí)間: 2025-3-29 00:15
,ECCV Caption: Correcting False Negatives by?Collecting Machine-and-Human-verified Image-Caption Assificant limitation. They have many missing correspondences, originating from the data construction process itself. For example, a caption is only matched with one image although the caption can be matched with other similar images and vice versa. To correct the massive false negatives, we construct 作者: 雕鏤 時(shí)間: 2025-3-29 06:48
MOTCOM: The Multi-Object Tracking Dataset Complexity Metric,lity, complicates comparison of datasets, and reduces the conversation on tracker performance to a matter of leader board position. As a remedy, we present the novel MOT dataset complexity metric (MOTCOM), which is a combination of three sub-metrics inspired by key problems in MOT: occlusion, errati作者: 不在灌木叢中 時(shí)間: 2025-3-29 08:58
,How to?Synthesize a?Large-Scale and?Trainable Micro-Expression Dataset?,ress the lack of large-scale datasets in micro-expression (MiE) recognition due to the prohibitive cost of data collection, which renders large-scale training less feasible. To this end, we develop a protocol to automatically synthesize large scale MiE training data that allow us to train improved r作者: 厭煩 時(shí)間: 2025-3-29 11:26 作者: BIAS 時(shí)間: 2025-3-29 19:36
,REALY: Rethinking the?Evaluation of?3D Face Reconstruction, We observe that aligning two shapes with different reference points can largely affect the evaluation results. This poses difficulties for precisely diagnosing and improving a 3D face reconstruction method. In this paper, we propose a novel evaluation approach with a new benchmark REALY, consists o作者: 遺留之物 時(shí)間: 2025-3-29 23:16 作者: 設(shè)想 時(shí)間: 2025-3-30 00:01 作者: 來這真柔軟 時(shí)間: 2025-3-30 04:08 作者: 繁榮中國(guó) 時(shí)間: 2025-3-30 11:53 作者: Overstate 時(shí)間: 2025-3-30 15:36
,OOD-CV: A Benchmark for?Robustness to?Out-of-Distribution Shifts of?Individual Nuisances in?Naturals they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV?, a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions, and enables benchmarking model作者: 自作多情 時(shí)間: 2025-3-30 19:09 作者: cuticle 時(shí)間: 2025-3-31 00:12
,The Anatomy of?Video Editing: A Dataset and?Benchmark Suite for?AI-Assisted Video Editing,t reframing, rotoscoping, color grading, or applying digital makeups. However, most of the solutions have focused on video manipulation and VFX. This work introduces the Anatomy of Video Editing, a dataset, and benchmark, to foster research in AI-assisted video editing. Our benchmark suite focuses o作者: 厭惡 時(shí)間: 2025-3-31 04:43 作者: 尖酸一點(diǎn) 時(shí)間: 2025-3-31 07:23
,PANDORA: A Panoramic Detection Dataset for?Object with?Orientation,tion to better understand the content of the panoramic image. These datasets and detectors use a Bounding Field of View (BFoV) as a bounding box in panoramic images. However, we observe that the object instances in panoramic images often appear with arbitrary orientations. It indicates that BFoV as 作者: maculated 時(shí)間: 2025-3-31 10:43 作者: correspondent 時(shí)間: 2025-3-31 15:36
,Exploring Fine-Grained Audiovisual Categorization with?the?SSW60 Dataset, has made great strides in fine-grained visual categorization on images, the counterparts in audio and video fine-grained categorization are relatively unexplored. To encourage advancements in this space, we have carefully constructed the SSW60 dataset to enable researchers to experiment with classi作者: 牽索 時(shí)間: 2025-3-31 18:03
,The Caltech Fish Counting Dataset: A Benchmark for?Multiple-Object Tracking and?Counting,ar videos as a rich source of data for advancing low signal-to-noise computer vision applications and tackling domain generalization in multiple-object tracking (MOT) and counting. In comparison to existing MOT and counting datasets, which are largely restricted to videos of people and vehicles in c作者: Airtight 時(shí)間: 2025-3-31 23:35
,A Dataset for?Interactive Vision-Language Navigation with?Unknown Command Feasibility,input command is fully feasible in the environment. Yet in practice, a request may not be possible due to language ambiguity or environment changes. To study VLN with unknown command feasibility, we introduce a new dataset Mobile app Tasks with Iterative Feedback (MoTIF), where the goal is to comple作者: 安心地散步 時(shí)間: 2025-4-1 03:49 作者: 揮舞 時(shí)間: 2025-4-1 08:04 作者: endure 時(shí)間: 2025-4-1 12:41 作者: 紀(jì)念 時(shí)間: 2025-4-1 17:48 作者: exceed 時(shí)間: 2025-4-1 18:58
https://doi.org/10.1007/978-981-10-0092-8ress the lack of large-scale datasets in micro-expression (MiE) recognition due to the prohibitive cost of data collection, which renders large-scale training less feasible. To this end, we develop a protocol to automatically synthesize large scale MiE training data that allow us to train improved r
