作者: 合同 時間: 2025-3-21 21:40 作者: 燈泡 時間: 2025-3-22 02:38 作者: V洗浴 時間: 2025-3-22 08:09 作者: 貪婪的人 時間: 2025-3-22 10:38 作者: 綠州 時間: 2025-3-22 13:06
Behavioral and psychological impairmentsity, realistic adversarial examples by integrating gradients of the target classifier interpretably. Experimental results on MNIST and ImageNet datasets demonstrate that AdvDiff is effective in generating unrestricted adversarial examples, which outperforms state-of-the-art unrestricted adversarial 作者: 綠州 時間: 2025-3-22 19:06 作者: 吼叫 時間: 2025-3-23 01:11
Cognitive impairment in Alzheimer diseaseification model and propose sharing partial parameters between the target classification model and the auxiliary classifier to condense model parameters. We conduct extensive experiments on several datasets of which results demonstrate that pFedDIL outperforms state-of-the-art methods by up to 14.35作者: 丑惡 時間: 2025-3-23 01:54
https://doi.org/10.1007/978-1-4612-4116-4attention mechanisms that only focus on existing visual features by introducing deformable feature alignment to hierarchically refine spatial positioning fused with multi-scale visual and linguistic information. Extensive Experiments demonstrate that our model enhances the localization of attention 作者: Watemelon 時間: 2025-3-23 06:20 作者: 亞麻制品 時間: 2025-3-23 11:10 作者: 外向者 時間: 2025-3-23 15:58 作者: Ordeal 時間: 2025-3-23 20:31
Sanjay W. Pimplikar,Anupama Suryanarayanar complicated training strategies, .?curates a smaller yet more feature-balanced data subset, fostering the development of spuriousness-robust models. Experimental validations across key benchmarks demonstrate that .?competes with or exceeds the performance of leading methods while significantly red作者: Proclaim 時間: 2025-3-23 22:49
Mathew A. Sherman,Sylvain E. Lesnétruggle to accurately estimate uncertainty when processing inputs drawn from the wild dataset. To address this issue, we introduce a novel instance-wise calibration method based on an energy model. Our method incorporates energy scores instead of softmax confidence scores, allowing for adaptive cons作者: 不遵守 時間: 2025-3-24 03:39 作者: CROW 時間: 2025-3-24 07:59 作者: excrete 時間: 2025-3-24 14:04
Alzheimer: 100 Years and Beyondth the proposed encoder layer and DyHead, a new dynamic TAD model, DyFADet, achieves promising performance on a series of challenging TAD benchmarks, including HACS-Segment, THUMOS14, ActivityNet-1.3, Epic-Kitchen?100, Ego4D-Moment QueriesV1.0, and FineAction. Code is released to ..作者: panorama 時間: 2025-3-24 17:42
,Teddy: Efficient Large-Scale Dataset Distillation via?Taylor-Approximated Matching,ents to a . one. On the other hand, rather than repeatedly training a novel model in each iteration, we unveil that employing a pre-cached pool of . models, which can be generated from a . base model, enhances both time efficiency and performance concurrently, particularly when dealing with large-sc作者: Instinctive 時間: 2025-3-24 22:42 作者: 睨視 時間: 2025-3-25 02:09
,-VTON: Dynamic Semantics Disentangling for?Differential Diffusion Based Virtual Try-On,to handle multiple degradations independently, thereby minimizing learning ambiguities and achieving realistic results with minimal overhead. Extensive experiments demonstrate that .-VTON significantly outperforms existing methods in both quantitative metrics and qualitative evaluations, demonstrati作者: bonnet 時間: 2025-3-25 04:10 作者: 果仁 時間: 2025-3-25 09:13
,Blind Image Deconvolution by?Generative-Based Kernel Prior and?Initializer via?Latent Encoding,nd initializer, one can obtain a high-quality initialization of the blur kernel, and enable optimization within a compact latent kernel manifold. Such a framework results in an evident performance improvement over existing DIP-based BID methods. Extensive experiments on different datasets demonstrat作者: Ceremony 時間: 2025-3-25 12:51
AdvDiff: Generating Unrestricted Adversarial Examples Using Diffusion Models,ity, realistic adversarial examples by integrating gradients of the target classifier interpretably. Experimental results on MNIST and ImageNet datasets demonstrate that AdvDiff is effective in generating unrestricted adversarial examples, which outperforms state-of-the-art unrestricted adversarial 作者: 傳授知識 時間: 2025-3-25 19:24 作者: 鋪子 時間: 2025-3-25 23:18 作者: watertight, 時間: 2025-3-26 02:10 作者: craven 時間: 2025-3-26 05:40 作者: 不能強(qiáng)迫我 時間: 2025-3-26 11:20 作者: 礦石 時間: 2025-3-26 14:25 作者: Medley 時間: 2025-3-26 19:38
,: Spuriousness Mitigation with?Minimal Human Annotations,r complicated training strategies, .?curates a smaller yet more feature-balanced data subset, fostering the development of spuriousness-robust models. Experimental validations across key benchmarks demonstrate that .?competes with or exceeds the performance of leading methods while significantly red作者: THROB 時間: 2025-3-26 23:49
,Uncertainty Calibration with?Energy Based Instance-Wise Scaling in?the?Wild Dataset,truggle to accurately estimate uncertainty when processing inputs drawn from the wild dataset. To address this issue, we introduce a novel instance-wise calibration method based on an energy model. Our method incorporates energy scores instead of softmax confidence scores, allowing for adaptive cons作者: 有惡意 時間: 2025-3-27 02:44 作者: Silent-Ischemia 時間: 2025-3-27 06:02
,UniMD: Towards Unifying Moment Retrieval and?Temporal Action Detection,ce the mutual benefits between TAD and MR. Extensive experiments demonstrate that the proposed task fusion learning scheme enables the two tasks to help each other and outperform the separately trained counterparts. Impressively, . achieves state-of-the-art results on three paired datasets Ego4D, Ch作者: extrovert 時間: 2025-3-27 11:15
,DyFADet: Dynamic Feature Aggregation for?Temporal Action Detection,th the proposed encoder layer and DyHead, a new dynamic TAD model, DyFADet, achieves promising performance on a series of challenging TAD benchmarks, including HACS-Segment, THUMOS14, ActivityNet-1.3, Epic-Kitchen?100, Ego4D-Moment QueriesV1.0, and FineAction. Code is released to ..作者: perpetual 時間: 2025-3-27 17:03 作者: Ankylo- 時間: 2025-3-27 20:28
https://doi.org/10.1007/978-3-540-37652-1ures to reduce the ambiguity between foreground and background and strengthen the depth edges. Extensive experimental results on nuScenes and DDAD benchmarks show M.Depth achieves state-of-the-art performance. More results can be found in ..作者: mastopexy 時間: 2025-3-27 23:12
Colin L Masters,Konrad Beyreuthermpowers existing frameworks to support hour-long videos and pushes their upper limit with an extra context token. It is demonstrated to surpass previous methods on most of video- or image-based benchmarks. Code and models are available at?..作者: 最有利 時間: 2025-3-28 05:07
M,Depth: Self-supervised Two-Frame ,ulti-camera ,etric Depth Estimation,ures to reduce the ambiguity between foreground and background and strengthen the depth edges. Extensive experimental results on nuScenes and DDAD benchmarks show M.Depth achieves state-of-the-art performance. More results can be found in ..作者: 奇思怪想 時間: 2025-3-28 10:10
,LLaMA-VID: An Image is Worth 2 Tokens in?Large Language Models,mpowers existing frameworks to support hour-long videos and pushes their upper limit with an extra context token. It is demonstrated to surpass previous methods on most of video- or image-based benchmarks. Code and models are available at?..作者: 防止 時間: 2025-3-28 13:04 作者: 散布 時間: 2025-3-28 17:37 作者: Coronation 時間: 2025-3-28 22:36
0302-9743 e 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. They deal with topics such as Computer vision, Machine learning, Deep neural networks, Re作者: BADGE 時間: 2025-3-29 00:29
,Teddy: Efficient Large-Scale Dataset Distillation via?Taylor-Approximated Matching,taset to generalize effectively on real data. Tackling this challenge, as defined, relies on a bi-level optimization algorithm: a novel model is trained in each iteration within a nested loop, with gradients propagated through an unrolled computation graph. However, this approach incurs high memory 作者: confide 時間: 2025-3-29 06:45
,Rethinking and?Improving Visual Prompt Selection for?In-Context Learning Segmentation,xel level. Recently, inspired by In-Context Learning (ICL), several generalist segmentation frameworks have been proposed, providing a promising paradigm for segmenting specific objects. However, existing works mostly ignore the value of visual prompts or simply apply similarity sorting to select co作者: FRET 時間: 2025-3-29 07:54 作者: 奇思怪想 時間: 2025-3-29 13:40
TC4D: Trajectory-Conditioned Text-to-4D Generation,presentations, such as deformation models or time-dependent neural representations, are limited in the amount of motion they can generate—they cannot synthesize motion extending far beyond the bounding box used for volume rendering. The lack of a more flexible motion model contributes to the gap in 作者: CLEFT 時間: 2025-3-29 18:57
,Blind Image Deconvolution by?Generative-Based Kernel Prior and?Initializer via?Latent Encoding,motivated a series of DIP-based approaches, demonstrating remarkable success in BID. However, due to the high non-convexity of the inherent optimization process, these methods are notorious for their sensitivity to the initialized kernel. To alleviate this issue and further improve their performance作者: 你正派 時間: 2025-3-29 21:29
AdvDiff: Generating Unrestricted Adversarial Examples Using Diffusion Models,ms for deep learning applications because they can effectively bypass defense mechanisms. However, previous attack methods often directly inject Projected Gradient Descent (PGD) gradients into the sampling of generative models, which are not theoretically provable and thus generate unrealistic examp作者: 威脅你 時間: 2025-3-30 03:06 作者: Junction 時間: 2025-3-30 07:18 作者: 純樸 時間: 2025-3-30 10:02
,ST-LDM: A Universal Framework for?Text-Grounded Object Generation in?Real Images, conditioned by textual descriptions. Existing diffusion models exhibit limitations of spatial perception in complex real-world scenes, relying on additional modalities to enforce constraints, and TOG imposes heightened challenges on scene comprehension under the weak supervision of linguistic infor作者: Anhydrous 時間: 2025-3-30 14:03 作者: entitle 時間: 2025-3-30 16:39
,Region-Adaptive Transform with?Segmentation Prior for?Image Compression,s transform methods for compression. However, there is no prior research on neural transform that focuses on specific regions. In response, we introduce the class-agnostic segmentation masks (. semantic masks without category labels) for extracting region-adaptive contextual information. Our propose作者: spondylosis 時間: 2025-3-30 21:14 作者: aspect 時間: 2025-3-31 03:36
,: Spuriousness Mitigation with?Minimal Human Annotations,orld scenarios where such correlations do not hold. Despite the increasing research effort, existing solutions often face two main challenges: they either demand substantial annotations of spurious attributes, or they yield less competitive outcomes with expensive training when additional annotation作者: 決定性 時間: 2025-3-31 06:53 作者: 衣服 時間: 2025-3-31 12:16 作者: IVORY 時間: 2025-3-31 16:09 作者: 喚起 時間: 2025-3-31 20:11
,UniMD: Towards Unifying Moment Retrieval and?Temporal Action Detection,ded natural language within untrimmed videos. Despite that they focus on different events, we observe they have a significant connection. For instance, most descriptions in MR involve multiple actions from TAD. In this paper, we aim to investigate the potential synergy between TAD and MR. Firstly, w作者: 格子架 時間: 2025-3-31 23:14
,DyFADet: Dynamic Feature Aggregation for?Temporal Action Detection,d modeling action instances with various lengths from complex scenes by shared-weights detection heads. Inspired by the successes in dynamic neural networks, in this paper, we build a novel dynamic feature aggregation (DFA) module that can simultaneously adapt kernel weights and receptive fields at
