作者: Senescent 時(shí)間: 2025-3-21 23:58
https://doi.org/10.1007/978-3-319-48323-8rimental results with the OU-MVLP and CASIA-B datasets demonstrate the state-of-the-art performance of the proposed method for both gait identification and verification scenarios, a direct consequence of the explicitly disentangled pose and shape features produced by the proposed end-to-end model-ba作者: Emg827 時(shí)間: 2025-3-22 03:46 作者: faction 時(shí)間: 2025-3-22 07:14 作者: Maximize 時(shí)間: 2025-3-22 12:13 作者: FOLLY 時(shí)間: 2025-3-22 15:45 作者: FOLLY 時(shí)間: 2025-3-22 20:05 作者: 不可接觸 時(shí)間: 2025-3-22 22:02 作者: 小故事 時(shí)間: 2025-3-23 02:57 作者: Pepsin 時(shí)間: 2025-3-23 08:42 作者: Rejuvenate 時(shí)間: 2025-3-23 11:52 作者: 盡管 時(shí)間: 2025-3-23 14:23
https://doi.org/10.1007/978-3-642-67047-3ion). We implemented MC-RISE and evaluate them using two datasets (GTSRB and ImageNet) to demonstrate the effectiveness of our methods in comparison with existing techniques for interpreting image classification results.作者: 顯赫的人 時(shí)間: 2025-3-23 19:36 作者: malign 時(shí)間: 2025-3-24 01:29 作者: 助記 時(shí)間: 2025-3-24 05:37
End-to-End Model-Based Gait Recognitionrimental results with the OU-MVLP and CASIA-B datasets demonstrate the state-of-the-art performance of the proposed method for both gait identification and verification scenarios, a direct consequence of the explicitly disentangled pose and shape features produced by the proposed end-to-end model-ba作者: perpetual 時(shí)間: 2025-3-24 06:42
Horizontal Flipping Assisted Disentangled Feature Learning for Semi-supervised Person Re-identificatfeatures. It is free of labels, and can be applied to both supervised and unsupervised learning branches in our model. Extensive results on four Re-ID datasets demonstrate that by reducing 5/6 labeled data, Our method achieves the best performance on Market-1501 and CUHK03, and comparable accuracy o作者: 起皺紋 時(shí)間: 2025-3-24 12:00
MIX’EM: Unsupervised Image Classification Using a Mixture of Embeddings (ii), semantic categories emerge through the mixture coefficients, making it possible to apply (iii). Subsequently, we run K-means on the representations to acquire semantic classification. We conduct extensive experiments and analyses on STL10, CIFAR10, and CIFAR100-20 datasets, achieving state-of作者: Alveolar-Bone 時(shí)間: 2025-3-24 16:20
Backbone Based Feature Enhancement for Object Detectionncy and accuracy. Without bells and whistles, our BBFE improves different baseline methods (both anchor-based and anchor-free) by a large margin (.2.0 points higher AP) on COCO, surpassing common feature pyramid networks including FPN and PANet.作者: Deceit 時(shí)間: 2025-3-24 23:04
Long-Term Cloth-Changing Person Re-identificationcontribution, we propose a novel Re-ID method specifically designed to address the cloth-changing challenge. Specifically, we consider that under cloth-changes, soft-biometrics such as body shape would be more reliable. We, therefore, introduce a shape embedding module as well as a cloth-elimination作者: Onerous 時(shí)間: 2025-3-25 00:36 作者: 接觸 時(shí)間: 2025-3-25 07:07
Background Learnable Cascade for Zero-Shot Object Detectionriate word vector for background class and use this learned vector in Cascade Semantic R-CNN, this design makes “Background Learnable” and reduces the confusion between background and unseen classes. Our extensive experiments show BLC obtains significantly performance improvements for MS-COCO over s作者: Celiac-Plexus 時(shí)間: 2025-3-25 10:54 作者: 知識(shí)分子 時(shí)間: 2025-3-25 13:55
Synthesizing the Unseen for Zero-Shot Object Detectiontected bounding boxes. We test our approach on three object detection benchmarks, PASCAL VOC, MSCOCO, and ILSVRC detection, under both conventional and generalized settings, showing impressive gains over the state-of-the-art methods. Our codes are available at ..作者: indubitable 時(shí)間: 2025-3-25 19:40
Fully Supervised and Guided Distillation for One-Stage Detectors process. Extensive experiments on Pascal VOC and COCO benchmarks demonstrate the following advantages of our algorithm, including the effectiveness for improving recall and reducing false detections, the robustness on common one-stage detector heads and the superiority compared with state-of-the-ar作者: GILD 時(shí)間: 2025-3-25 22:15
Visualizing Color-Wise Saliency of Black-Box Image Classification Modelsion). We implemented MC-RISE and evaluate them using two datasets (GTSRB and ImageNet) to demonstrate the effectiveness of our methods in comparison with existing techniques for interpreting image classification results.作者: 懶惰人民 時(shí)間: 2025-3-26 03:57 作者: 實(shí)現(xiàn) 時(shí)間: 2025-3-26 06:45
Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the Wildadverse effects of false positives?(FPs) and false negatives?(FNs) from inaccurate pseudo-labels. Two components have positive effects on improving the performance of scene text detectors when adapting from synthetic-to-real scenes. We evaluate the proposed method by transferring from SynthText, VIS作者: amputation 時(shí)間: 2025-3-26 09:12 作者: 固定某物 時(shí)間: 2025-3-26 14:37
Satya Krishna Ramachandran,Sachin Kheterpalr than some independent operators. We perform experiments on multiple benchmarks including image matching, camera localisation, and 3D reconstruction. The results indicate that our method improves the matching performance of various descriptors and that it generalises across methods and tasks.作者: incredulity 時(shí)間: 2025-3-26 18:22 作者: sparse 時(shí)間: 2025-3-26 23:39
COG: COnsistent Data AuGmentation for Object Perceptionow that our method COG’s performance is superior to its competitor on detection and instance segmentation tasks. In addition, the results manifest the robustness of COG when faced with hyper-parameter variations, etc.作者: SPECT 時(shí)間: 2025-3-27 02:25 作者: Verify 時(shí)間: 2025-3-27 07:15 作者: 脊椎動(dòng)物 時(shí)間: 2025-3-27 13:23 作者: 集中營(yíng) 時(shí)間: 2025-3-27 16:01 作者: 遵循的規(guī)范 時(shí)間: 2025-3-27 21:49 作者: 潔凈 時(shí)間: 2025-3-28 00:41
https://doi.org/10.1007/978-3-642-67047-3act from the last convolutional layer and show that kernels identified are symbolic in that they react strongly to sets of similar images that effectively divide output classes into sub-classes with distinct characteristics.作者: Abominate 時(shí)間: 2025-3-28 02:38 作者: 凝視 時(shí)間: 2025-3-28 07:51 作者: faultfinder 時(shí)間: 2025-3-28 11:00
Adaptive Spotting: Deep Reinforcement Object Search in 3D Point Clouds be searched. This network is successfully trained in an end-to-end manner by integrating a contrastive loss and a reinforcement localization reward. Evaluations on ModelNet40 and Stanford 2D-3D-S datasets demonstrate the superiority of the proposed approach over several state-of-the-art baselines.作者: enfeeble 時(shí)間: 2025-3-28 18:17
End-to-End Model-Based Gait Recognition. In this paper, we propose an end-to-end model-based gait recognition method. Specifically, we employ a skinned multi-person linear (SMPL) model for human modeling, and estimate its parameters using a pre-trained human mesh recovery (HMR) network. As the pre-trained HMR is not recognition-oriented,作者: 最高點(diǎn) 時(shí)間: 2025-3-28 21:24 作者: 閑蕩 時(shí)間: 2025-3-29 00:49 作者: Crumple 時(shí)間: 2025-3-29 04:54
Backbone Based Feature Enhancement for Object Detectionection performance. However, almost all the architectures of feature pyramid are manually designed, which requires ad hoc design and prior knowledge. Meanwhile, existing methods focus on exploring more appropriate connections to generate features with strong semantics features from inherent pyramida作者: 懶惰民族 時(shí)間: 2025-3-29 08:49 作者: 機(jī)制 時(shí)間: 2025-3-29 14:53
Any-Shot Object Detection real world scenarios, it is less practical to expect that ‘.’ the novel classes are either unseen or have few-examples. Here, we propose a more realistic setting termed ‘.’, where totally unseen and few-shot categories can simultaneously co-occur during inference. Any-shot detection offers unique c作者: 微生物 時(shí)間: 2025-3-29 18:25
Background Learnable Cascade for Zero-Shot Object Detectionemain several challenges for ZSD, including reducing the ambiguity between background and unseen objects as well as improving the alignment between visual and semantic concept. In this work, we propose a novel framework named Background Learnable Cascade (BLC) to improve ZSD performance. The major c作者: integrated 時(shí)間: 2025-3-29 22:03
Unsupervised Domain Adaptive Object Detection Using Forward-Backward Cyclic Adaptationadversarial training based domain adaptation methods have shown their effectiveness on minimizing domain discrepancy via marginal feature distributions alignment. However, aligning the marginal feature distributions does not guarantee the alignment of class conditional distributions. This limitation作者: contradict 時(shí)間: 2025-3-30 02:59 作者: 預(yù)知 時(shí)間: 2025-3-30 04:51
Synthesizing the Unseen for Zero-Shot Object Detectionresponding semantics during inference. However, since the unseen objects are never visualized during training, the detection model is skewed towards seen content, thereby labeling unseen as background or a seen class. In this work, we propose to . visual features for unseen classes, so that the mode作者: TOXIN 時(shí)間: 2025-3-30 10:23
Fully Supervised and Guided Distillation for One-Stage Detectors regions and false detection regions of student networks to effectively distill the feature representation from teacher networks. To address it, we propose a fully supervised and guided distillation algorithm for one-stage detectors, where an excitation and suppression loss is designed to make a stu作者: opportune 時(shí)間: 2025-3-30 13:03
Visualizing Color-Wise Saliency of Black-Box Image Classification Modelsarning, is often hard to interpret. This problem of interpretability is one of the major obstacles in deploying a trained model in safety-critical systems. Several techniques have been proposed to address this problem; one of which is RISE, which explains a classification result by a heatmap, called作者: exorbitant 時(shí)間: 2025-3-30 17:37 作者: 責(zé)難 時(shí)間: 2025-3-30 21:16
D2D: Keypoint Extraction with Describe to Detect Approachbe, or jointly detect and describe are two typical strategies for extracting local features. In contrast, we propose an approach that inverts this process by first describing and then detecting the keypoint locations. Describe-to-Detect (D2D) leverages successful descriptor models without the need f作者: follicle 時(shí)間: 2025-3-31 03:07 作者: Arresting 時(shí)間: 2025-3-31 07:44
Adaptive Spotting: Deep Reinforcement Object Search in 3D Point Cloudsa. A straightforward approach that exhaustively scans the scene is often prohibitive due to computational inefficiencies. High-quality feature representation also needs to be learned to achieve accurate recognition and localization. Aiming to address these two fundamental problems in a unified frame
