作者: abduction 時間: 2025-3-21 23:26 作者: 戰(zhàn)勝 時間: 2025-3-22 00:59 作者: Oratory 時間: 2025-3-22 06:52
,An Accurate Detection Is Not All You Need to?Combat Label Noise in?Web-Noisy Datasets, or distance-based methods despite being poorly separated from the OOD distribution using unsupervised learning. Because we further observe a low correlation with SOTA metrics, this urges us to propose a hybrid solution that alternates between noise detection using linear separation and a state-of-t作者: 親屬 時間: 2025-3-22 09:39 作者: Ballerina 時間: 2025-3-22 14:21
,Learned HDR Image Compression for?Perceptually Optimal Storage and?Display,ther as side information to aid HDR image reconstruction from the output LDR image. To measure the perceptual quality of output HDR and LDR images, we use two recently proposed image distortion metrics, both validated against human perceptual data of image quality and with reference to the uncompres作者: Ballerina 時間: 2025-3-22 18:42 作者: Estrogen 時間: 2025-3-23 00:42 作者: 沉著 時間: 2025-3-23 03:11 作者: 下垂 時間: 2025-3-23 09:15
,Improving Virtual Try-On with?Garment-Focused Diffusion Models,rived from the CLIP and VAE encodings of the reference garment. Meanwhile, a novel garment-focused adapter is integrated into the UNet of diffusion model, pursuing local fine-grained alignment with the visual appearance of reference garment and human pose. We specifically design an appearance loss o作者: Abutment 時間: 2025-3-23 11:02 作者: Nausea 時間: 2025-3-23 17:51 作者: Coeval 時間: 2025-3-23 20:52 作者: cancer 時間: 2025-3-23 22:16 作者: Foment 時間: 2025-3-24 05:17 作者: 馬賽克 時間: 2025-3-24 06:57 作者: 專心 時間: 2025-3-24 13:38 作者: Assault 時間: 2025-3-24 14:56
,SDPT: Synchronous Dual Prompt Tuning for?Fusion-Based Visual-Language Pre-trained Models,projections allow the unified prototype token to synchronously represent the two modalities and enable SDPT to share the unified semantics of text and image for downstream tasks across different modal prompts. Experimental results demonstrate that SDPT assists fusion-based VLPMs to achieve superior 作者: originality 時間: 2025-3-24 22:18 作者: 攀登 時間: 2025-3-25 01:57 作者: GROVE 時間: 2025-3-25 05:47
Interkulturelle ?ffnung der Pflegeberatung translation, we also demonstrate that two by-products of our approach—3D keypoint augmentation and multi-view understanding—can assist in keypoint-based sign language understanding. Code and models are available at ..作者: CORD 時間: 2025-3-25 09:58
Angela Nikelski,Annette Nauerth or distance-based methods despite being poorly separated from the OOD distribution using unsupervised learning. Because we further observe a low correlation with SOTA metrics, this urges us to propose a hybrid solution that alternates between noise detection using linear separation and a state-of-t作者: 刺耳的聲音 時間: 2025-3-25 15:43
Selbstbestimmt Wohnen mit Demenzperations in our network architecture instead of the long decoding path of the U-Net architecture used in most existing studies. Our model achieves state-of-the-art performance without increasing parameters and further reduces inference time, as demonstrated by extensive results. Codes are available作者: escalate 時間: 2025-3-25 17:23 作者: 禮節(jié) 時間: 2025-3-25 23:26 作者: Neutral-Spine 時間: 2025-3-26 01:14 作者: faction 時間: 2025-3-26 07:47 作者: MIR 時間: 2025-3-26 10:25 作者: 針葉樹 時間: 2025-3-26 14:43
Mechthild Bereswill,Anke Neuberbaselines across multiple datasets. It achieves a 1.9% improvement in mean Average Precision (mAP) over the state-of-the-art StreamPETR method on the NuScenes dataset. It shows significant performance gains on the Argoverse 2 dataset, highlighting its generalization capability. The code is available作者: 鳥籠 時間: 2025-3-26 18:24 作者: 自作多情 時間: 2025-3-26 21:08 作者: 態(tài)學 時間: 2025-3-27 02:45 作者: chapel 時間: 2025-3-27 06:11 作者: 內(nèi)疚 時間: 2025-3-27 12:26
Robert J. DeLorenzo,Larry H. Dashefskyormation as a robust and domain-invariant conductor, and MMIT-Mixup injects the domain-invariant and class-specific knowledge to obtain domain-invariant prototypes. Then, RI-FT optimizes the distance between features and prototypes to enhance the robustness of visual-encoder. We consider several typ作者: Cleave 時間: 2025-3-27 16:52 作者: 牢騷 時間: 2025-3-27 20:36 作者: JEER 時間: 2025-3-28 00:58
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: 減弱不好 時間: 2025-3-28 04:15
0302-9743 n; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-72966-9978-3-031-72967-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 和藹 時間: 2025-3-28 08:09 作者: Sputum 時間: 2025-3-28 10:51
Online Vectorized HD Map Construction Using Geometry,tions independently. GeMap achieves new state-of-the-art performance on the nuScenes and Argoverse 2 datasets. Remarkably, it reaches a 71.8% mAP on the large-scale Argoverse 2 dataset, outperforming MapTRv2 by +4.4% and surpassing the 70% mAP threshold for the first time. Code is available at ..作者: 爆炸 時間: 2025-3-28 17:45
0302-9743 ce on Computer 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 netwo作者: ventilate 時間: 2025-3-28 19:44 作者: 窩轉脊椎動物 時間: 2025-3-29 01:27 作者: Repatriate 時間: 2025-3-29 03:39 作者: Bricklayer 時間: 2025-3-29 07:28 作者: Filibuster 時間: 2025-3-29 13:29
,An Accurate Detection Is Not All You Need to?Combat Label Noise in?Web-Noisy Datasets,upon the recent empirical observation that applying unsupervised contrastive learning to noisy, web-crawled datasets yields a feature representation under which the in-distribution (ID) and out-of-distribution (OOD) samples are linearly separable [.]. We show that direct estimation of the separating作者: 發(fā)電機 時間: 2025-3-29 18:23 作者: ADJ 時間: 2025-3-29 23:16 作者: Digitalis 時間: 2025-3-30 01:34
,Learned HDR Image Compression for?Perceptually Optimal Storage and?Display,er demand for superior image quality. As a result, HDR image compression is crucial to fully realize the benefits of HDR imaging without suffering from large file sizes and inefficient data handling. Conventionally, this is achieved by introducing a residual/gain map as additional metadata to bridge作者: admission 時間: 2025-3-30 05:02 作者: finite 時間: 2025-3-30 08:20
,Non-exemplar Domain Incremental Learning via?Cross-Domain Concept Integration, exemplar-based solutions are not always viable due to data privacy concerns or storage limitations.Therefore, Non-Exemplar Domain Incremental Learning (NEDIL) has emerged as a significant paradigm for resolving DIL challenges.Current NEDIL solutions extend the classifier incrementally for new domai作者: aplomb 時間: 2025-3-30 13:57
,Free-VSC: Free Semantics from?Visual Foundation Models for?Unsupervised Video Semantic Compression,wever, the semantic richness of previous methods remains limited, due to the single semantic learning objective, limited training data, etc. To address this, we propose to boost the UVSC task by absorbing the off-the-shelf rich semantics from VFMs. Specifically, we introduce a VFMs-shared semantic a作者: 真繁榮 時間: 2025-3-30 18:03
,Improving Virtual Try-On with?Garment-Focused Diffusion Models, apply diffusion models for synthesizing an image of a target person wearing a given in-shop garment, i.e., image-based virtual try-on (VTON) task. The difficulty originates from the aspect that the diffusion process should not only produce holistically high-fidelity photorealistic image of the targ作者: Petechiae 時間: 2025-3-30 23:24 作者: Pathogen 時間: 2025-3-31 01:18
,Disentangled Generation and?Aggregation for?Robust Radiance Fields,h a high-quality representation and low computation cost. A key requirement of this method is the precise input of camera poses. However, due to the local update property of the triplane, a similar joint estimation as previous joint pose-NeRF optimization works easily results in local minima. To thi作者: incite 時間: 2025-3-31 05:01
,UNIKD: UNcertainty-Filtered Incremental Knowledge Distillation for?Neural Implicit Representation,quire the images of a scene from different camera views to be available for one-time training. This is expensive especially for scenarios with large-scale scenes and limited data storage. In view of this, we explore the task of incremental learning for NIRs in this work. We design a student-teacher 作者: drusen 時間: 2025-3-31 12:29
,Subspace Prototype Guidance for?Mitigating Class Imbalance in?Point Cloud Semantic Segmentation, segmentation network is influenced by the quantity of samples available for different categories. To mitigate the cognitive bias induced by class imbalance, this paper introduces a novel method, namely subspace prototype guidance (.), to guide the training of segmentation network. Specifically, the作者: CURL 時間: 2025-3-31 14:03 作者: Orchiectomy 時間: 2025-3-31 17:50
,Semantic-Guided Robustness Tuning for?Few-Shot Transfer Across Extreme Domain Shift,in shift between base and novel target classes. Current methods always employ a lightweight backbone and continue to use a linear-probe-like traditional fine-tuning (Trad-FT) paradigm. While for recently emerging large-scale pre-trained model (LPM), which has more parameters with considerable prior 作者: 后來 時間: 2025-4-1 01:19
,Revisit Event Generation Model: Self-supervised Learning of?Event-to-Video Reconstruction with?Implvent-based and frame-based computer vision. Previous approaches have depended on supervised learning on synthetic data, which lacks interpretability and risk over-fitting to the setting of the event simulator. Recently, self-supervised learning (SSL) based methods, which primarily utilize per-frame 作者: Original 時間: 2025-4-1 05:24
,SDPT: Synchronous Dual Prompt Tuning for?Fusion-Based Visual-Language Pre-trained Models,ual-modal fusion-based visual-language pre-trained models (VLPMs), such as GLIP, has encountered issues. Existing prompt tuning methods have not effectively addressed the modal mapping and aligning problem for tokens in different modalities, leading to poor transfer generalization. To address this i作者: 竊喜 時間: 2025-4-1 08:45
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242333.jpg作者: 小溪 時間: 2025-4-1 11:02 作者: 2否定 時間: 2025-4-1 16:32
https://doi.org/10.1007/978-3-642-85555-9ing (FSCIL) faces several challenges, such as overfitting and catastrophic forgetting. Such a challenging problem is often tackled by fixing a feature extractor trained on base classes to reduce the adverse effects of overfitting and forgetting. Under such formulation, our primary focus is represent
