作者: 尖 時(shí)間: 2025-3-21 21:33
Incorporating Reinforced Adversarial Learning in Autoregressive Image Generation,tized Variational AutoEncoders (VQ-VAE). However, autoregressive models have several limitations such as exposure bias and their training objective does not guarantee visual fidelity. To address these limitations, we propose to use Reinforced Adversarial Learning (RAL) based on policy gradient optim作者: 無底 時(shí)間: 2025-3-22 03:23 作者: 軍械庫 時(shí)間: 2025-3-22 08:04
Visual Question Answering on Image Sets,ettings. Taking a natural language question and a set of images as input, it aims to answer the question based on the content of the images. The questions can be about objects and relationships in one or more images or about the entire scene depicted by the image set. To enable research in this new 作者: achlorhydria 時(shí)間: 2025-3-22 11:59
Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots,rganize the points regularly, e.g. voxelize, pass them through a designed 2D/3D neural network, and then define object-level anchors that predict offsets of 3D bounding boxes using collective evidences from all the points on the objects of interest. Contrary to the state-of-the-art anchor-based meth作者: 駁船 時(shí)間: 2025-3-22 14:48 作者: 駁船 時(shí)間: 2025-3-22 18:48 作者: Pander 時(shí)間: 2025-3-23 00:56 作者: 社團(tuán) 時(shí)間: 2025-3-23 02:12 作者: Atheroma 時(shí)間: 2025-3-23 09:23 作者: 沉思的魚 時(shí)間: 2025-3-23 10:35
QuEST: Quantized Embedding Space for Transferring Knowledge,Most of the existing knowledge distillation methods direct the student to follow the teacher by matching the teacher’s output, feature maps or their distribution. In this work, we propose a novel way to achieve this goal: by distilling the knowledge through a . space. According to our method, the te作者: 決定性 時(shí)間: 2025-3-23 16:34 作者: 鉗子 時(shí)間: 2025-3-23 21:25 作者: Neutral-Spine 時(shí)間: 2025-3-23 22:18
Dense RepPoints: Representing Visual Objects with Dense Point Sets,g both box level and pixel level. Techniques are proposed to efficiently process these dense points, maintaining near-constant complexity with increasing point numbers. Dense RepPoints is shown to represent and learn object segments well, with the use of a novel distance transform sampling method co作者: 權(quán)宜之計(jì) 時(shí)間: 2025-3-24 03:49
On Dropping Clusters to Regularize Graph Convolutional Neural Networks,promising performance on various tasks. However, the information of individually zeroed entries could still present in other correlated entries by propagating (1) spatially between entries of different node feature vectors and (2) depth-wisely between different entries of each node feature vector, w作者: brother 時(shí)間: 2025-3-24 10:17 作者: 全國(guó)性 時(shí)間: 2025-3-24 12:04 作者: Hirsutism 時(shí)間: 2025-3-24 15:19
DR-KFS: A Differentiable Visual Similarity Metric for 3D Shape Reconstruction,pes by measuring distances between multi-view images . rendered from the shapes. Importantly, the image-space distance is also differentiable and measures visual ., rather than pixel-wise distortion. Specifically, the similarity is defined by mean-squared errors over HardNet features computed from p作者: COM 時(shí)間: 2025-3-24 20:15 作者: ASSAY 時(shí)間: 2025-3-24 23:51
Conference proceedings 2020n, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with top作者: Innocence 時(shí)間: 2025-3-25 03:36 作者: NICHE 時(shí)間: 2025-3-25 09:12 作者: 錯(cuò)事 時(shí)間: 2025-3-25 12:52
Second-Hand Tobacco Smoke Exposurembined with set-to-set supervision. The distance transform sampling combines the strengths of contour and grid representations, leading to performance that surpasses counterparts based on contours or grids. Code is available at ..作者: 財(cái)主 時(shí)間: 2025-3-25 17:13 作者: 聽寫 時(shí)間: 2025-3-25 23:39 作者: prostate-gland 時(shí)間: 2025-3-26 01:42 作者: Visual-Acuity 時(shí)間: 2025-3-26 04:58 作者: 啟發(fā) 時(shí)間: 2025-3-26 10:17 作者: 食物 時(shí)間: 2025-3-26 14:46
The Worldwide Consumption of Alcohol,tructions with better structural fidelity and visual quality. We demonstrate this both objectively, using well-known shape metrics for retrieval and classification tasks that are independent from our new metric, and subjectively through a perceptual study.作者: 冬眠 時(shí)間: 2025-3-26 16:58
Visual Question Answering on Image Sets,et has 49,617 questions for 12,746 image sets. We analyze the properties of the two datasets, including question-and-answer distributions, types of questions, biases in dataset, and question-image dependencies. We also build new baseline models to investigate new research challenges in ISVQA.作者: ostensible 時(shí)間: 2025-3-27 00:13 作者: 無聊的人 時(shí)間: 2025-3-27 05:05
Backpropagated Gradient Representations for Anomaly Detection,in benchmark image recognition datasets. Also, we highlight the computational efficiency and the simplicity of the proposed method in comparison with other state-of-the-art methods relying on adversarial networks or autoregressive models, which require at least 27 times more model parameters than the proposed method.作者: 細(xì)微的差異 時(shí)間: 2025-3-27 05:48 作者: 秘傳 時(shí)間: 2025-3-27 12:13
0302-9743 uter Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers dea作者: Watemelon 時(shí)間: 2025-3-27 16:35 作者: COLON 時(shí)間: 2025-3-27 17:53 作者: indices 時(shí)間: 2025-3-28 01:36 作者: 原來 時(shí)間: 2025-3-28 02:12
DSDNet: Deep Structured Self-driving Network, a safe maneuver by using a structured planning cost. Our sample-based formulation allows us to overcome the difficulty in probabilistic inference of continuous random variables. Experiments on a number of large-scale self driving datasets demonstrate that our model significantly outperforms the state-of-the-art.作者: 簡(jiǎn)略 時(shí)間: 2025-3-28 10:06
Dense RepPoints: Representing Visual Objects with Dense Point Sets,mbined with set-to-set supervision. The distance transform sampling combines the strengths of contour and grid representations, leading to performance that surpasses counterparts based on contours or grids. Code is available at ..作者: hypnotic 時(shí)間: 2025-3-28 12:17 作者: 無表情 時(shí)間: 2025-3-28 15:21 作者: anchor 時(shí)間: 2025-3-28 20:06
Pere Mir-Artigues,Pablo del Ríofic agents from videos and synthesize missing regions with the guidance of depth/point cloud. By building a dense 3D map from stitched point clouds, frames within a video are geometrically correlated via this common 3D map. In order to fill a target inpainting area in a frame, it is straightforward 作者: 詞匯 時(shí)間: 2025-3-29 00:17 作者: Fabric 時(shí)間: 2025-3-29 03:44 作者: 領(lǐng)導(dǎo)權(quán) 時(shí)間: 2025-3-29 09:34 作者: 粗語 時(shí)間: 2025-3-29 13:28 作者: 倒轉(zhuǎn) 時(shí)間: 2025-3-29 16:53
Economic Effects of Credit Reporting,, its cultural style, . ?Asian or European, as well as its economic type, . ?industry oriented or tourism oriented. While place recognition has been widely studied in previous work, there remains a long way towards comprehensive place understanding, which is far beyond categorizing a place with an i作者: 委屈 時(shí)間: 2025-3-29 22:19
The Institutions of Credit Reporting,e based approaches employing 3D convolutional neural networks (CNNs) have considerably improved the accuracy of MVS systems. However, this accuracy comes at a high computational cost which impedes practical adoption. Distinct from cost volume approaches, we propose an efficient depth estimation appr作者: Proponent 時(shí)間: 2025-3-30 02:42 作者: 維持 時(shí)間: 2025-3-30 04:06
Economic Effects of Credit Reporting,mbiguity associated with monocular images, these algorithms fail at correctly predicting true metric depth. In this work, we demonstrate how a depth histogram of the scene, which can be readily captured using a single-pixel time-resolved detector, can be fused with the output of existing monocular d作者: 出汗 時(shí)間: 2025-3-30 09:08 作者: 鋪?zhàn)?nbsp; 時(shí)間: 2025-3-30 14:04 作者: VEIL 時(shí)間: 2025-3-30 20:23
https://doi.org/10.1007/978-94-010-1073-3g from 1) the normal-abnormal class imbalance and 2) the rare but significant hard samples in fundus images. However, debiasing in CAD is not trivial because existing methods cannot cure the two types of bias to categorize fundus images. In this paper, we propose a novel curriculum learning paradigm作者: exclusice 時(shí)間: 2025-3-31 00:03
https://doi.org/10.1007/978-94-010-1073-3y detection algorithms use activation representations from forward propagation while not exploiting gradients from backpropagation to characterize data. Gradients capture model updates required to represent data. Anomalies require more drastic model updates to fully represent them compared to normal作者: Chameleon 時(shí)間: 2025-3-31 03:28
Second-Hand Tobacco Smoke Exposureg both box level and pixel level. Techniques are proposed to efficiently process these dense points, maintaining near-constant complexity with increasing point numbers. Dense RepPoints is shown to represent and learn object segments well, with the use of a novel distance transform sampling method co作者: 思考 時(shí)間: 2025-3-31 05:33 作者: 長(zhǎng)處 時(shí)間: 2025-3-31 12:53 作者: Genome 時(shí)間: 2025-3-31 15:05 作者: 糾纏,纏繞 時(shí)間: 2025-3-31 20:06 作者: RACE 時(shí)間: 2025-4-1 01:26
Causes of the Abuse of Illicit Drugs,oposed architecture efficiently and effectively models the relationship between image patches at multiple scales by constructing a pyramid of local self-attention blocks. The design includes a novel position projection to encode the spatial positions of the patches. SPAN is trained on a generic, syn作者: Aggregate 時(shí)間: 2025-4-1 04:58
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234232.jpg作者: nitroglycerin 時(shí)間: 2025-4-1 08:54
Computer Vision – ECCV 2020978-3-030-58589-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 食道 時(shí)間: 2025-4-1 11:15 作者: 按等級(jí) 時(shí)間: 2025-4-1 14:28 作者: 補(bǔ)角 時(shí)間: 2025-4-1 21:06
APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection,c data and using unsupervised methods such as kernel density estimation, Bayesian uncertainty, and reconstruction error. Our results suggest that adversarial patches can be effectively flagged, both in a high-knowledge, attack-specific scenario and in an unsupervised setting where patches are detect作者: BRINK 時(shí)間: 2025-4-1 22:45
