作者: 觀點(diǎn) 時(shí)間: 2025-3-21 21:35 作者: idiopathic 時(shí)間: 2025-3-22 00:29
,Exploring Guided Sampling of?Conditional GANs,e FID score to 4.37. It is noteworthy that our sampling strategy sufficiently closes the gap between GANs and one-step diffusion models (.., with FID 4.02) under comparable model size. Code is available at ..作者: peritonitis 時(shí)間: 2025-3-22 07:11 作者: 懶洋洋 時(shí)間: 2025-3-22 12:06 作者: Instantaneous 時(shí)間: 2025-3-22 15:08
,MacDiff: Unified Skeleton Modeling with?Masked Conditional Diffusion, a diffusion decoder conditioned on the representations extracted by a semantic encoder. Random masking is applied to encoder inputs to introduce a information bottleneck and remove redundancy of skeletons. Furthermore, we theoretically demonstrate that our generative objective involves the contrast作者: Instantaneous 時(shí)間: 2025-3-22 17:10
,TCC-Det: Temporarily Consistent Cues for?Weakly-Supervised 3D Detection,and allows training using orders of magnitude more data than traditional fully-supervised methods..The method is evaluated on KITTI and Waymo Open datasets, where it outperforms all previous weakly-supervised methods and where it narrows the gap when compared to methods using human 3D labels..The so作者: 節(jié)省 時(shí)間: 2025-3-22 21:33 作者: Detoxification 時(shí)間: 2025-3-23 02:52
,FoundPose: Unseen Object Pose Estimation with?Foundation Features,. Such descriptors carry stronger positional information than descriptors from the last layer, and we show their importance when semantic information is ambiguous due to object symmetries or a lack of texture. To avoid establishing correspondences against all object templates, we develop an efficien作者: 癡呆 時(shí)間: 2025-3-23 08:47 作者: GROG 時(shí)間: 2025-3-23 13:05
,Select and?Distill: Selective Dual-Teacher Knowledge Transfer for?Continual Learning on?Vision-Langnowledge while preserving the zero-shot capabilities of pre-trained VLMs. Extensive experiments on benchmark datasets demonstrate that our framework is favorable against state-of-the-art continual learning approaches for preventing catastrophic forgetting and zero-shot degradation. Project page: ..作者: 拋射物 時(shí)間: 2025-3-23 16:02
,SAFNet: Selective Alignment Fusion Network for?Efficient HDR Imaging,introduced which enjoys privileges from previous optical flow, selection masks and initial prediction. Moreover, to facilitate learning on samples with large motion, a new window partition cropping method is presented during training. Experiments on public and newly developed challenging datasets sh作者: AMBI 時(shí)間: 2025-3-23 21:48
,Reason2Drive: Towards Interpretable and?Chain-Based Reasoning for?Autonomous Driving,ric to assess chain-based reasoning performance in autonomous systems, addressing the reasoning ambiguities of existing metrics such as BLEU and CIDEr. Based on the proposed benchmark, we conduct experiments to assess various existing VLMs, revealing insights into their reasoning capabilities. Addit作者: shrill 時(shí)間: 2025-3-23 23:41
,Omniview-Tuning: Boosting Viewpoint Invariance of?Vision-Language Pre-training Models,ining efficiency, we design a novel fine-tuning framework named Omniview-Tuning (OVT). Specifically, OVT introduces a Cross-Viewpoint Alignment objective through a minimax-like optimization strategy, which effectively aligns representations of identical objects from diverse viewpoints without causin作者: 地名表 時(shí)間: 2025-3-24 04:32 作者: 輕率看法 時(shí)間: 2025-3-24 07:10
,Soziales – Vom Sinn des Zusammen Seins, Network (BGAN) that learns to predict the constructed correction biases, which can be utilized to correct the original predictions from coarse-grained relationships to fine-grained ones. The extensive experimental results on VG, GQA, and VG-1800 datasets demonstrate that our SBG outperforms the sta作者: PALSY 時(shí)間: 2025-3-24 14:32
https://doi.org/10.1007/978-3-662-63158-4e FID score to 4.37. It is noteworthy that our sampling strategy sufficiently closes the gap between GANs and one-step diffusion models (.., with FID 4.02) under comparable model size. Code is available at ..作者: intertwine 時(shí)間: 2025-3-24 16:08
Theoretischer Hintergrund der Untersuchungeraging large-scale language, vision-language, and vision-motion data to assist motion-related generation tasks, MotionChain thus comprehends each instruction in multi-turn conversation and generates human motions followed by these prompts. Extensive experiments validate the efficacy of MotionChain,作者: JIBE 時(shí)間: 2025-3-24 20:00 作者: 盡管 時(shí)間: 2025-3-25 00:50
A. Koocheki,B. Lalegani,S. A. Hosseini a diffusion decoder conditioned on the representations extracted by a semantic encoder. Random masking is applied to encoder inputs to introduce a information bottleneck and remove redundancy of skeletons. Furthermore, we theoretically demonstrate that our generative objective involves the contrast作者: 使迷惑 時(shí)間: 2025-3-25 06:01 作者: objection 時(shí)間: 2025-3-25 08:38
Allelopathic interactions in soil first employ an object-wise depth encoder, which takes the pixel-wise depth map as a prior, to accurately estimate the object-wise depth. Then, we utilize the proposed object-wise position embedding to encode the object-wise depth information into the transformer decoder, thereby producing 3D objec作者: ventilate 時(shí)間: 2025-3-25 13:43 作者: 變化 時(shí)間: 2025-3-25 16:23
Robert E. Hoagland,Stephen J. Cutleridering the cross-task class similarity to initialize matrices used in the transformation, helping achieve the stability-plasticity trade-off. Experiments on Pascal VOC 2012 and ADE20K datasets show that the proposed strategy can significantly improve the performance of previous methods. The code is作者: Impugn 時(shí)間: 2025-3-25 23:16 作者: 廢除 時(shí)間: 2025-3-26 00:56
https://doi.org/10.1007/978-0-387-77337-7introduced which enjoys privileges from previous optical flow, selection masks and initial prediction. Moreover, to facilitate learning on samples with large motion, a new window partition cropping method is presented during training. Experiments on public and newly developed challenging datasets sh作者: 小口啜飲 時(shí)間: 2025-3-26 08:14 作者: cauda-equina 時(shí)間: 2025-3-26 10:28
https://doi.org/10.1007/978-1-4612-1186-0ining efficiency, we design a novel fine-tuning framework named Omniview-Tuning (OVT). Specifically, OVT introduces a Cross-Viewpoint Alignment objective through a minimax-like optimization strategy, which effectively aligns representations of identical objects from diverse viewpoints without causin作者: 秘傳 時(shí)間: 2025-3-26 15:01 作者: 蒸發(fā) 時(shí)間: 2025-3-26 20:00
0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-73346-8978-3-031-73347-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: TERRA 時(shí)間: 2025-3-26 22:05 作者: 商業(yè)上 時(shí)間: 2025-3-27 01:55
Cataclysmic and Symbiotic Variables,the HGSL stage, we learn the graph structure by predicting the types of different directed edges. In the HGR stage, message passing among nodes is performed on the learned graph structure for scene graph prediction. Extensive experiments show that our method achieves comparable or superior performance to existing methods on 3DSSG dataset.作者: chemical-peel 時(shí)間: 2025-3-27 07:51 作者: gusher 時(shí)間: 2025-3-27 10:16
,Heterogeneous Graph Learning for?Scene Graph Prediction in?3D Point Clouds,the HGSL stage, we learn the graph structure by predicting the types of different directed edges. In the HGR stage, message passing among nodes is performed on the learned graph structure for scene graph prediction. Extensive experiments show that our method achieves comparable or superior performance to existing methods on 3DSSG dataset.作者: Agnosia 時(shí)間: 2025-3-27 14:31
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. They deal with topics such as computer vision; machine learning; deep neural networks; r作者: PLUMP 時(shí)間: 2025-3-27 18:43 作者: 事與愿違 時(shí)間: 2025-3-28 00:53 作者: 驚呼 時(shí)間: 2025-3-28 04:19 作者: 粉筆 時(shí)間: 2025-3-28 07:05
,Latent Guard: A?Safety Framework for?Text-to-Image Generation,s in the input text embeddings. Our framework is composed of a data generation pipeline specific to the task using large language models, ad-hoc architectural components, and a contrastive learning strategy to benefit from the generated data. Our method is evaluated on three datasets and against four baselines.作者: 認(rèn)為 時(shí)間: 2025-3-28 11:01 作者: 可互換 時(shí)間: 2025-3-28 16:34
,Deep Cost Ray Fusion for?Sparse Depth Video Completion,ramework consistently outperforms or rivals state-of-the-art approaches on diverse indoor and outdoor datasets, including the KITTI Depth Completion benchmark, VOID Depth Completion benchmark, and ScanNetV2 dataset, using much fewer network parameters.作者: frenzy 時(shí)間: 2025-3-28 22:13 作者: 鎮(zhèn)痛劑 時(shí)間: 2025-3-29 01:17 作者: cardiopulmonary 時(shí)間: 2025-3-29 05:05 作者: 裹住 時(shí)間: 2025-3-29 10:37
,Exploring Guided Sampling of?Conditional GANs,hat generative adversarial networks (GANs) can also benefit from guided sampling, not even requiring to pre-prepare a classifier (.., classifier guidance) or learn an unconditional counterpart (.., classifier-free guidance) as in diffusion models. Inspired by the organized latent space in GANs, we m作者: FLIRT 時(shí)間: 2025-3-29 14:14
,MotionChain: Conversational Motion Controllers via?Multimodal Prompts,ver, this proficiency remains largely unexplored in other multimodal generative models, particularly in human motion models. By integrating multi-turn conversations in controlling continuous virtual human movements,generative human motion models can achieve an intuitive and step-by-step process of h作者: 意外 時(shí)間: 2025-3-29 18:13
,Idempotent Unsupervised Representation Learning for?Skeleton-Based Action Recognition,ion recognition, the features obtained from existing pre-trained generative methods contain redundant information unrelated to recognition, which contradicts the nature of the skeleton’s spatially sparse and temporally consistent properties, leading to undesirable performance. To address this challe作者: Anguish 時(shí)間: 2025-3-29 22:57 作者: bacteria 時(shí)間: 2025-3-30 01:41 作者: 寒冷 時(shí)間: 2025-3-30 05:34 作者: Sedative 時(shí)間: 2025-3-30 11:36 作者: CRUMB 時(shí)間: 2025-3-30 14:49 作者: 無能性 時(shí)間: 2025-3-30 19:12 作者: invert 時(shí)間: 2025-3-30 23:45 作者: accomplishment 時(shí)間: 2025-3-31 03:17 作者: CUR 時(shí)間: 2025-3-31 07:00
,VideoMamba: State Space Model for?Efficient Video Understanding,o domain. The proposed VideoMamba overcomes the limitations of existing 3D convolution neural networks (CNNs) and video transformers. Its linear-complexity operator enables efficient long-term modeling, which is crucial for high-resolution long video understanding. Extensive evaluations reveal Video作者: conference 時(shí)間: 2025-3-31 10:23
,SAFNet: Selective Alignment Fusion Network for?Efficient HDR Imaging,ethods have achieved great success by either following the alignment and fusion pipeline or utilizing attention mechanism. However, the large computation cost and inference delay hinder them from deploying on resource limited devices. In this paper, to achieve better efficiency, a novel Selective Al作者: 棲息地 時(shí)間: 2025-3-31 14:39
,Heterogeneous Graph Learning for?Scene Graph Prediction in?3D Point Clouds,her exploit context information or emphasize knowledge prior to model the scene graph in a fully-connected homogeneous graph framework. However, these methods may lead to indiscriminate message passing among graph nodes (i.e., objects), resulting in sub-optimal performance. In this paper, we propose作者: DEMN 時(shí)間: 2025-3-31 17:44
,Reason2Drive: Towards Interpretable and?Chain-Based Reasoning for?Autonomous Driving,ning tasks essential for highly autonomous vehicle behavior. Despite their potential, research in autonomous systems is hindered by the lack of datasets with annotated reasoning chains that explain the decision-making processes in driving. To bridge this gap, we present Reason2Drive, a benchmark dat作者: CLEFT 時(shí)間: 2025-4-1 01:10
,Omniview-Tuning: Boosting Viewpoint Invariance of?Vision-Language Pre-training Models,ess to distribution shifts of 2D images. However, their robustness under 3D viewpoint variations is still limited, which can hinder the development for real-world applications. This paper successfully addresses this concern while keeping VLPs’ original performance by breaking through two primary obs作者: 微不足道 時(shí)間: 2025-4-1 04:25 作者: 使厭惡 時(shí)間: 2025-4-1 09:25
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242306.jpg作者: opinionated 時(shí)間: 2025-4-1 10:56
https://doi.org/10.1007/978-3-662-63158-4rceive and understand human emotions, we can significantly improve human-machine interactions. Current research in emotion recognition emphasizes facial expressions, speech and physiological signals, often overlooking body movement’s expressive potential. Existing most methods, reliant on full-body 作者: 小臼 時(shí)間: 2025-4-1 15:24 作者: allergy 時(shí)間: 2025-4-1 19:41
https://doi.org/10.1007/978-3-662-63158-4hat generative adversarial networks (GANs) can also benefit from guided sampling, not even requiring to pre-prepare a classifier (.., classifier guidance) or learn an unconditional counterpart (.., classifier-free guidance) as in diffusion models. Inspired by the organized latent space in GANs, we m作者: 潛移默化 時(shí)間: 2025-4-2 02:23
Theoretischer Hintergrund der Untersuchungver, this proficiency remains largely unexplored in other multimodal generative models, particularly in human motion models. By integrating multi-turn conversations in controlling continuous virtual human movements,generative human motion models can achieve an intuitive and step-by-step process of h作者: Allowance 時(shí)間: 2025-4-2 06:33
Allelopathic Dynamics in Resource Plantsion recognition, the features obtained from existing pre-trained generative methods contain redundant information unrelated to recognition, which contradicts the nature of the skeleton’s spatially sparse and temporally consistent properties, leading to undesirable performance. To address this challe作者: 翻動(dòng) 時(shí)間: 2025-4-2 10:44 作者: Ossification 時(shí)間: 2025-4-2 13:06
