作者: 拉開這車床 時間: 2025-3-21 22:39 作者: indubitable 時間: 2025-3-22 01:35
,Diagnosing and?Re-learning for?Balanced Multimodal Learning,ach modality is firstly estimated based on the separability of its uni-modal representation space, and then used to softly re-initialize the corresponding uni-modal encoder. In this way, the over-emphasizing of scarcely informative modalities is avoided. In addition, encoders of worse-learnt modalit作者: CURL 時間: 2025-3-22 04:40 作者: 長矛 時間: 2025-3-22 09:58 作者: Stable-Angina 時間: 2025-3-22 16:31 作者: Stable-Angina 時間: 2025-3-22 20:45
,SpaRP: Fast 3D Object Reconstruction and?Pose Estimation from?Sparse Views, the input sparse views. These predictions are then leveraged to accomplish 3D reconstruction and pose estimation, and the reconstructed 3D model can be used to further refine the camera poses of input views. Through extensive experiments on three datasets, we demonstrate that our method not only si作者: Clinch 時間: 2025-3-22 22:23 作者: 商品 時間: 2025-3-23 04:55 作者: exclusice 時間: 2025-3-23 05:52
LITA: Language Instructed Temporal-Localization Assistant,g video datasets with timestamps, we propose a new task, Reasoning Temporal Localization (RTL), along with the dataset, ActivityNet-RTL, for learning and evaluating this task. Reasoning temporal localization requires both the reasoning and temporal localization of Video LLMs. LITA demonstrates stron作者: Triglyceride 時間: 2025-3-23 12:20
,MARs: Multi-view Attention Regularizations for?Patch-Based Feature Recognition of?Space Terrain,ocus. We thoroughly analyze many modern metric learning losses with and without MARs and demonstrate improved terrain-feature recognition performance by upwards of 85%. We additionally introduce the Luna-1 dataset, consisting of Moon crater landmarks and reference navigation frames from NASA mission作者: 裝入膠囊 時間: 2025-3-23 14:30
,Ferret-UI: Grounded Mobile UI Understanding with?Multimodal LLMs,s are formatted for instruction-following with region annotations to facilitate precise referring and grounding. To augment the model’s reasoning ability, we further compile a dataset for advanced tasks, including detailed description, conversations, and function inference. After training on the cur作者: ONYM 時間: 2025-3-23 19:27
,Bridging the?Pathology Domain Gap: Efficiently Adapting CLIP for?Pathology Image Analysis with?Limi (DVC) techniques to mitigate overfitting issues. Finally, we present the Doublet Multimodal Contrastive Loss (DMCL) for fine-tuning CLIP for pathology tasks. We demonstrate that Path-CLIP adeptly adapts pre-trained CLIP to downstream pathology tasks, yielding competitive results. Specifically, Path作者: 鄙視讀作 時間: 2025-3-23 23:24
,AugUndo: Scaling Up Augmentations for?Monocular Depth Completion and?Estimation,g, geometric transformations to the coordinates of the output depth, warping the depth map back to the original reference frame. This enables computing the reconstruction losses using the original images and sparse depth maps, eliminating the pitfalls of naive loss computation on the augmented input作者: Kidnap 時間: 2025-3-24 02:55 作者: GRUEL 時間: 2025-3-24 09:32 作者: 莎草 時間: 2025-3-24 10:48
,Minimalist Vision with?Freeform Pixels, major advantages. First, it naturally tends to preserve the privacy of individuals in the scene since the captured information is inadequate for extracting visual details. Second, since the number of measurements made by a minimalist camera is very small, we show that it can be fully self-powered, 作者: STALE 時間: 2025-3-24 18:19 作者: lesion 時間: 2025-3-24 22:37 作者: Infantry 時間: 2025-3-25 02:17 作者: 抗原 時間: 2025-3-25 04:14
https://doi.org/10.1007/978-3-642-57658-4tion) for each new vision task with our contribution-based method to adaptively determine layer by layer capacity for that task to yield comparable performance to full tuning. Furthermore, our PROD strategy allows to extend the capability of pre-trained models with improved performance as well as ro作者: 填料 時間: 2025-3-25 08:40
Joint products and irreversibilityhout domain-specific feature extraction. Together, these components synergistically contribute towards unveiling the hierarchical organization of natural behavior. Models and benchmarks are available at ..作者: 傻瓜 時間: 2025-3-25 13:18
Ambivalente Gesellschaftlichkeitnd token limit of diffusion model, and then to seamlessly convert the base image to a higher-resolution output, exceeding training image size and incorporating details aware of text and instances via our novel instance-aware hierarchical enlargement process that consists of our proposed high-frequen作者: Ingredient 時間: 2025-3-25 16:14 作者: 搜尋 時間: 2025-3-25 20:26 作者: 我怕被刺穿 時間: 2025-3-26 01:47
Antisemitismus, Nation und Ordnungt bottleneck of attributes for classification. Our method produces state-of-the-art, interpretable fine-grained classifiers. We outperform the baselines by . on five fine-grained iNaturalist datasets and by . on two KikiBouba datasets, despite the baselines having access to privileged information.作者: 單獨 時間: 2025-3-26 07:37 作者: discord 時間: 2025-3-26 10:49
Stetige Innovation im Unternehmen verankernocus. We thoroughly analyze many modern metric learning losses with and without MARs and demonstrate improved terrain-feature recognition performance by upwards of 85%. We additionally introduce the Luna-1 dataset, consisting of Moon crater landmarks and reference navigation frames from NASA mission作者: 我要沮喪 時間: 2025-3-26 15:28
Einführung in den Wertsch?pfungsprozesss are formatted for instruction-following with region annotations to facilitate precise referring and grounding. To augment the model’s reasoning ability, we further compile a dataset for advanced tasks, including detailed description, conversations, and function inference. After training on the cur作者: 管理員 時間: 2025-3-26 17:08 作者: 樹膠 時間: 2025-3-26 22:19
Technologische Lenkungsversucheg, geometric transformations to the coordinates of the output depth, warping the depth map back to the original reference frame. This enables computing the reconstruction losses using the original images and sparse depth maps, eliminating the pitfalls of naive loss computation on the augmented input作者: inspiration 時間: 2025-3-27 02:32
Martin Heinrich,Barbara Kohlstocktion in situations of poor radar Doppler information or unfavorable camera viewing conditions. Experimental validations on public and our proposed datasets, along with benchmark comparisons, showcase CARB-Net’s superiority, boasting up to a . improvement in mAP performance. A series of ablation stud作者: 滑動 時間: 2025-3-27 06:10 作者: lattice 時間: 2025-3-27 10:50 作者: ethereal 時間: 2025-3-27 16:36 作者: 放逐某人 時間: 2025-3-27 19:56
https://doi.org/10.1007/978-3-319-94129-5 is then reflected by the change in model performance relative to unpermuted data. When applied to a set of concepts, the method generates a ranking of feature importance. We show this approach recovers underlying model feature importance on synthetic and real-world image classification tasks.作者: glans-penis 時間: 2025-3-28 01:38 作者: Instrumental 時間: 2025-3-28 03:13
,DEPICT: Diffusion-Enabled Permutation Importance for?Image Classification Tasks, is then reflected by the change in model performance relative to unpermuted data. When applied to a set of concepts, the method generates a ranking of feature importance. We show this approach recovers underlying model feature importance on synthetic and real-world image classification tasks.作者: Heretical 時間: 2025-3-28 10:04 作者: Trabeculoplasty 時間: 2025-3-28 14:22
Conference proceedings 2025nt 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: Control-Group 時間: 2025-3-28 15:09 作者: acrobat 時間: 2025-3-28 22:03
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: Intruder 時間: 2025-3-29 00:16
,Depth-Guided NeRF Training via?Earth Mover’s Distance,e enough information to disambiguate between different possible geometries yielding the same image. Previous work has thus incorporated depth supervision during NeRF training, leveraging dense predictions from pre-trained depth networks as pseudo-ground truth. While these depth priors are assumed to作者: Trypsin 時間: 2025-3-29 06:55 作者: 寒冷 時間: 2025-3-29 08:43 作者: EXTOL 時間: 2025-3-29 12:01 作者: 組裝 時間: 2025-3-29 18:31
,Diagnosing and?Re-learning for?Balanced Multimodal Learning,he training of uni-modal encoders from different perspectives, taking the inter-modal performance discrepancy as the basis. However, the intrinsic limitation of modality capacity is ignored. The scarcely informative modalities can be recognized as “worse-learnt” ones, which could force the model to 作者: altruism 時間: 2025-3-29 22:16 作者: 富饒 時間: 2025-3-30 01:10
,Elucidating the?Hierarchical Nature of?Behavior with?Masked Autoencoders,ehavioral benchmarks, we create a novel synthetic basketball playing benchmark (Shot7M2). Beyond synthetic data, we extend BABEL into a hierarchical action segmentation benchmark (hBABEL). Then, we develop a masked autoencoder framework (hBehaveMAE) to elucidate the hierarchical nature of motion cap作者: aquatic 時間: 2025-3-30 06:10
BeyondScene: Higher-Resolution Human-Centric Scene Generation with Pretrained Diffusion,lenge stems from limited training image size, text encoder capacity (limited tokens), and the inherent difficulty of generating complex scenes involving multiple humans. While current methods attempted to address training size limit only, they often yielded human-centric scenes with severe artifacts作者: 萬靈丹 時間: 2025-3-30 11:42
,SpaRP: Fast 3D Object Reconstruction and?Pose Estimation from?Sparse Views,, they often lack sufficient controllability and tend to produce hallucinated regions that may not align with users’ expectations. In this paper, we explore an important scenario in which the input consists of one or a few unposed 2D images of a single object, with little or no overlap. We propose a作者: 駁船 時間: 2025-3-30 13:15
MMEarth: Exploring Multi-modal Pretext Tasks for Geospatial Representation Learning, unique opportunity to pair data from different modalities and sensors automatically based on geographic location and time, at virtually no human labor cost. We seize this opportunity to create ., a diverse multi-modal pretraining dataset at global scale. Using this new corpus of 1.2 million locatio作者: 離開可分裂 時間: 2025-3-30 18:45
,Evolving Interpretable Visual Classifiers with?Large Language Models,owever, vision-language models, which compute similarity scores between images and class labels, are largely black-box, with limited interpretability, risk for bias, and inability to discover new visual concepts not written down. Moreover, in practical settings, the vocabulary for class names and at作者: dura-mater 時間: 2025-3-31 00:25 作者: Habituate 時間: 2025-3-31 01:52 作者: 竊喜 時間: 2025-3-31 07:42
,Ferret-UI: Grounded Mobile UI Understanding with?Multimodal LLMs,y to comprehend and interact effectively with user interface (UI) screens. In this paper, we present Ferret-UI, a new MLLM tailored for enhanced understanding of mobile UI screens, equipped with ., ., and . capabilities. Given that UI screens typically exhibit a more elongated aspect ratio and conta作者: MURAL 時間: 2025-3-31 10:49
,Bridging the?Pathology Domain Gap: Efficiently Adapting CLIP for?Pathology Image Analysis with?Limiization across diverse vision tasks. However, its effectiveness in pathology image analysis, particularly with limited labeled data, remains an ongoing area of investigation due to challenges associated with significant domain shifts and catastrophic forgetting. Thus, it is imperative to devise effi作者: Immunotherapy 時間: 2025-3-31 17:22
,AugUndo: Scaling Up Augmentations for?Monocular Depth Completion and?Estimation,tion, and occlusions are amongst the many undesirable by-products of common data augmentation schemes that affect image reconstruction quality, and thus the training signal. Hence, typical augmentations on images viewed as essential to training pipelines in other vision tasks have seen limited use b作者: Oscillate 時間: 2025-3-31 20:38 作者: laparoscopy 時間: 2025-3-31 23:46 作者: 沙發(fā) 時間: 2025-4-1 02:43
,Minimalist Vision with?Freeform Pixels,ls, a minimalist camera uses freeform pixels that can take on arbitrary shapes to increase their information content. We show that the hardware of a minimalist camera can be modeled as the first layer of a neural network, where the subsequent layers are used for inference. Training the network for a作者: 周興旺 時間: 2025-4-1 06:54
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242357.jpg作者: 枯燥 時間: 2025-4-1 10:44 作者: eulogize 時間: 2025-4-1 15:23
Forward Curve Modelling by Ambit Fields with new environments efficiently. Weakly supervised affordance grounding teaches agents the concept of affordance without costly pixel-level annotations, but with exocentric images. Although recent advances in weakly supervised affordance grounding yielded promising results, there remain challenge作者: FLING 時間: 2025-4-1 21:18
https://doi.org/10.1007/978-3-319-94129-5-based explanations in the pixel space, making it difficult to understand global model behavior. In contrast, permutation based explanations for tabular data classifiers measure feature importance by comparing model performance on data before and after permuting a feature. We propose an explanation
