作者: 承認(rèn) 時(shí)間: 2025-3-21 22:40
,Enhanced Sparsification via?Stimulative Training,press the importance of dropped weights, which is regarded as the suppressed sparsification paradigm. However, this paradigm inactivates the dropped parts of networks causing capacity damage before pruning, thereby leading to performance degradation. To alleviate this issue, we first study and revea作者: 欲望 時(shí)間: 2025-3-22 01:26 作者: CHURL 時(shí)間: 2025-3-22 08:02 作者: 一美元 時(shí)間: 2025-3-22 10:29 作者: Triglyceride 時(shí)間: 2025-3-22 16:58 作者: Triglyceride 時(shí)間: 2025-3-22 18:31 作者: prostatitis 時(shí)間: 2025-3-22 23:55
PapMOT: Exploring Adversarial Patch Attack Against Multiple Object Tracking,heir respective identities across successive frames. Despite significant progress made in multiple object tracking (MOT), recent studies have revealed the vulnerability of existing MOT methods to adversarial attacks. Nevertheless, all of these attacks belong to digital attacks that inject pixel-leve作者: Musket 時(shí)間: 2025-3-23 03:46
,HiDiffusion: Unlocking Higher-Resolution Creativity and?Efficiency in?Pretrained Diffusion Models,n models will encounter unreasonable object duplication and exponentially increase the generation time. In this paper, we discover that object duplication arises from feature duplication in the deep blocks of the U-Net. Concurrently, We pinpoint the extended generation times to self-attention redund作者: Pulmonary-Veins 時(shí)間: 2025-3-23 05:35 作者: 粗魯?shù)娜?nbsp; 時(shí)間: 2025-3-23 12:43
,Syn-to-Real Domain Adaptation for?Point Cloud Completion via?Part-Based Approach,s methods have been proposed to overcome this limitation by leveraging synthetic complete point clouds. While access to complete point clouds offers a notable advantage, they often struggle to bridge domain gaps, leading to sub-optimal performance. As a remedy, we propose a novel part-based framewor作者: 突襲 時(shí)間: 2025-3-23 15:18
,Learn to?Preserve and?Diversify: Parameter-Efficient Group with?Orthogonal Regularization for?Domaiseen test data occurs. Recently, foundation models with enormous parameters have been pre-trained with huge datasets, demonstrating strong generalization ability and showing promising direction for solving the DG problem. However, fully Fine-Tuning (FT) the foundation models results in unsatisfactor作者: fatty-acids 時(shí)間: 2025-3-23 20:11
,SCOMatch: Alleviating Overtrusting in?Open-Set Semi-supervised Learning,nd out-of-distribution (OOD) samples from unseen classes, for semi-supervised learning (SSL). Prior OSSL methods initially learned the decision boundary between ID and OOD with labeled ID data, subsequently employing self-training to refine this boundary. These methods, however, suffer from the tend作者: 兩種語言 時(shí)間: 2025-3-24 00:45
,Region-Aware Distribution Contrast: A Novel Approach to?Multi-task Partially Supervised Learning,and surface normal estimation, particularly when dealing with partially annotated data (MTPSL). The complexity arises from the absence of complete task labels for each training image. Given the inter-related nature of these pixel-wise dense tasks, our focus is on mining and capturing cross-task rela作者: 責(zé)怪 時(shí)間: 2025-3-24 05:50
,MasterWeaver: Taming Editability and?Face Identity for?Personalized Text-to-Image Generation, human identities indicated by the reference images. Despite promising identity fidelity has been achieved by several tuning-free methods, they often suffer from overfitting issues. The learned identity tends to entangle with irrelevant information, resulting in unsatisfied text controllability, esp作者: 不感興趣 時(shí)間: 2025-3-24 08:11
,PointRegGPT: Boosting 3D Point Cloud Registration Using Generative Point-Cloud Pairs for?Training,hile rendering-based synthetic data suffers from domain gaps. In this work, we present ., boosting 3D . cloud .istration using .enerative .oint-cloud pairs for .raining. Given a single depth map, we first apply a random camera motion to re-project it into a target depth map. Converting them to point作者: adj憂郁的 時(shí)間: 2025-3-24 10:42 作者: STING 時(shí)間: 2025-3-24 18:11
,Long-CLIP: Unlocking the?Long-Text Capability of?CLIP,by aligning image and text modalities. Despite its widespread adoption, a significant limitation of CLIP lies in the inadequate length of text input. The length of the text token is restricted to 77, and an empirical study shows the actual effective length is even less than 20. This prevents CLIP fr作者: 發(fā)電機(jī) 時(shí)間: 2025-3-24 20:06 作者: 消滅 時(shí)間: 2025-3-25 01:11
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: Haphazard 時(shí)間: 2025-3-25 05:41 作者: Interstellar 時(shí)間: 2025-3-25 11:33 作者: 創(chuàng)新 時(shí)間: 2025-3-25 11:40
H. V?re,R. Lampinen,C. Humphries,P. WilliamsIoU, and DocSim scores. Moreover, Dolfin’s applications extend beyond layout generation, making it suitable for modeling other types of geometric structures, such as line segments. Our experiments present both qualitative and quantitative results to demonstrate the advantages of Dolfin.作者: 機(jī)密 時(shí)間: 2025-3-25 16:51
,On the?Approximation Risk of?Few-Shot Class-Incremental Learning,ross both base and novel classes. Leveraging these insights, we conduct comprehensive experiments to validate our principles, achieving state-of-the-art performance on three FSCIL benchmark datasets. Code is available at ..作者: ONYM 時(shí)間: 2025-3-25 22:40 作者: Ccu106 時(shí)間: 2025-3-26 02:44
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: 起草 時(shí)間: 2025-3-26 05:59
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: 喊叫 時(shí)間: 2025-3-26 12:06 作者: 清楚 時(shí)間: 2025-3-26 16:39
Chronic Cutaneous Lupus Erythematosuspress the importance of dropped weights, which is regarded as the suppressed sparsification paradigm. However, this paradigm inactivates the dropped parts of networks causing capacity damage before pruning, thereby leading to performance degradation. To alleviate this issue, we first study and revea作者: 遠(yuǎn)地點(diǎn) 時(shí)間: 2025-3-26 19:47 作者: Infuriate 時(shí)間: 2025-3-26 21:01 作者: 懶惰人民 時(shí)間: 2025-3-27 04:03
https://doi.org/10.1007/978-3-319-72134-7 modeling hand-held objects with arbitrary topology and overcomes the resolution limitations of parametric models, allowing for finer-grained reconstruction. However, directly modeling detailed SDFs from visual clues presents challenges due to depth ambiguity and appearance similarity, especially in作者: 使苦惱 時(shí)間: 2025-3-27 07:58 作者: 紳士 時(shí)間: 2025-3-27 10:30 作者: Pandemic 時(shí)間: 2025-3-27 13:59
Alpha-1 Antitrypsin Deficiency,heir respective identities across successive frames. Despite significant progress made in multiple object tracking (MOT), recent studies have revealed the vulnerability of existing MOT methods to adversarial attacks. Nevertheless, all of these attacks belong to digital attacks that inject pixel-leve作者: 瑣事 時(shí)間: 2025-3-27 19:50
https://doi.org/10.1007/978-1-0716-3605-3n models will encounter unreasonable object duplication and exponentially increase the generation time. In this paper, we discover that object duplication arises from feature duplication in the deep blocks of the U-Net. Concurrently, We pinpoint the extended generation times to self-attention redund作者: Occlusion 時(shí)間: 2025-3-28 00:02 作者: originality 時(shí)間: 2025-3-28 03:30
Merrheim and the New Syndicalisms methods have been proposed to overcome this limitation by leveraging synthetic complete point clouds. While access to complete point clouds offers a notable advantage, they often struggle to bridge domain gaps, leading to sub-optimal performance. As a remedy, we propose a novel part-based framewor作者: LIMN 時(shí)間: 2025-3-28 06:54 作者: muffler 時(shí)間: 2025-3-28 12:42 作者: entrance 時(shí)間: 2025-3-28 16:18 作者: deface 時(shí)間: 2025-3-28 22:07
Spezifische Belastungsmuster des Alpinsports human identities indicated by the reference images. Despite promising identity fidelity has been achieved by several tuning-free methods, they often suffer from overfitting issues. The learned identity tends to entangle with irrelevant information, resulting in unsatisfied text controllability, esp作者: averse 時(shí)間: 2025-3-29 00:28 作者: 燒烤 時(shí)間: 2025-3-29 03:44
Spezifische Belastungsmuster des Alpinsportsan effort. To tackle this problem, many methods adopt weakly supervised 3D object detection that estimates 3D boxes by leveraging 2D boxes and scene/class-specific priors. However, these approaches generally depend on sophisticated manual priors, which is hard to generalize to novel categories and s作者: osteoclasts 時(shí)間: 2025-3-29 09:09 作者: insipid 時(shí)間: 2025-3-29 12:15
H. V?re,R. Lampinen,C. Humphries,P. Williamsdeling capability and transparency over the existing approaches. Dolfin employs a Transformer-based diffusion process to model layout generation. In addition to an efficient bi-directional (non-causal joint) sequence representation, we also design an autoregressive diffusion model (Dolfin-AR) that i作者: BRAND 時(shí)間: 2025-3-29 19:04 作者: GIDDY 時(shí)間: 2025-3-29 23:21 作者: MOTTO 時(shí)間: 2025-3-30 00:04 作者: Indolent 時(shí)間: 2025-3-30 04:42
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242329.jpg作者: forthy 時(shí)間: 2025-3-30 08:22
,Diffusion Models as?Optimizers for?Efficient Planning in?Offline RL,hieve more efficient planning without sacrificing capability. To evaluate the effectiveness and efficiency of the Trajectory Diffuser, we conduct experiments on the D4RL benchmarks. The results demonstrate that our method achieves .-. faster inference speed compared to previous sequence modeling met作者: PATHY 時(shí)間: 2025-3-30 14:04
,Enhanced Sparsification via?Stimulative Training,illation-guided exploration strategy. To reduce the huge capacity gap of distillation, we propose a subnet mutating expansion technique. Extensive experiments on various benchmarks indicate the effectiveness of STP. Specifically, without fine-tuning, our method consistently achieves superior perform作者: lesion 時(shí)間: 2025-3-30 18:33
How Many Are in This Image A Safety Evaluation Benchmark for Vision LLMs,) Current VLLMs struggle with OOD texts but not images, unless the visual information is limited; and 2) These VLLMs can be easily misled by deceiving vision encoders only, and their vision-language training often compromise safety protocols. We release this safety evaluation suite at ..作者: overwrought 時(shí)間: 2025-3-30 20:52 作者: 無力更進(jìn) 時(shí)間: 2025-3-31 01:00
,Coarse-to-Fine Implicit Representation Learning for?3D Hand-Object Reconstruction from?a?Single RGBocal geometric clues and the coarse-level visual priors to capture intricate details. Additionally, we propose a surface-aware efficient reconstruction strategy that sparsely performs SDF query based on the hand-object semantic priors. Experiments on two challenging hand-object datasets show that ou作者: 羞辱 時(shí)間: 2025-3-31 06:43 作者: HEPA-filter 時(shí)間: 2025-3-31 11:25
,Enhancing Recipe Retrieval with?Foundation Models: A Data Augmentation Perspective, reduce computation cost rather than fully fine-tuning all the parameters. In addition, multi-level circle loss is proposed to align the original and augmented data pairs, which assigns different penalties for positive and negative pairs. On the Recipe1M dataset, our DAR outperforms all existing met作者: SNEER 時(shí)間: 2025-3-31 13:35
PapMOT: Exploring Adversarial Patch Attack Against Multiple Object Tracking,de the temporal consistency of tracking results across video frames, resulting in more aggressive attacks. We further develop new evaluation metrics to assess the robustness of MOT against such attacks. Extensive evaluations on multiple datasets demonstrate that our PapMOT can successfully attack va作者: 發(fā)酵 時(shí)間: 2025-3-31 17:38
,HiDiffusion: Unlocking Higher-Resolution Creativity and?Efficiency in?Pretrained Diffusion Models,etrained diffusion models to scale image generation resolutions even to . at . the inference speed of previous methods. Extensive experiments demonstrate that our approach can address object duplication and heavy computation issues, achieving state-of-the-art performance on higher-resolution image s作者: 不幸的人 時(shí)間: 2025-3-31 23:25
,Syn-to-Real Domain Adaptation for?Point Cloud Completion via?Part-Based Approach,ule, which operates in a part-wise manner to produce complete point clouds. Within PAC, we devise a novel part-aware transformer to learn relationships between parts and utilize this information to infer missing parts in incomplete point clouds. Extensive experiments demonstrate that our part-based 作者: 乳汁 時(shí)間: 2025-4-1 01:57
,Learn to?Preserve and?Diversify: Parameter-Efficient Group with?Orthogonal Regularization for?Domair-Efficient Group with Orthogonal regularization (PEGO) for vision transformers, which effectively preserves the generalization ability of the pre-trained network and learns more diverse knowledge compared with conventional PEFT. Specifically, we inject a group of trainable Low-Rank Adaptation (LoRA作者: 復(fù)習(xí) 時(shí)間: 2025-4-1 06:44 作者: 遍及 時(shí)間: 2025-4-1 13:10 作者: Accomplish 時(shí)間: 2025-4-1 15:59 作者: Mechanics 時(shí)間: 2025-4-1 21:45
,PointRegGPT: Boosting 3D Point Cloud Registration Using Generative Point-Cloud Pairs for?Training,oud registration. When equipped with our approach, several recent algorithms can improve their performance significantly and achieve SOTA consistently on two common benchmarks. The code and dataset will be released on ..作者: doxazosin 時(shí)間: 2025-4-2 02:10
General Geometry-Aware Weakly Supervised 3D Object Detection,es on the image plane, and . to build a Point-to-Box alignment loss to further refine the pose of estimated 3D boxes. Experiments on KITTI and SUN-RGBD datasets demonstrate that our method yields surprisingly high-quality 3D bounding boxes with only 2D annotation. The source code is available at ..作者: addition 時(shí)間: 2025-4-2 03:12
,Long-CLIP: Unlocking the?Long-Text Capability of?CLIP,is goal is far from straightforward, as simplistic fine-tuning can result in a significant degradation of CLIP’s performance. Moreover, substituting the text encoder with a language model supporting longer contexts necessitates pretraining with vast amounts of data, incurring significant expenses. A
