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Found 27 papers, 16 papers with code
BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation
BlinkSim consists of a configurable rendering engine and a flexible engine for event data simulation.
Revisiting the acceleration phenomenon via high-resolution differential equations
Furthermore, we also investigate NAG from the implicit-velocity scheme.
Decision-making with Imaginary Opponent Models
Specifically, the actor maintains a speculated belief of the opponents, which we call the \textit{imaginary opponent models}, to predict opponents' actions using local observations and makes decisions accordingly.
R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition
Recently, vision architectures based exclusively on multi-layer perceptrons (MLPs) have gained much attention in the computer vision community.
Introduction and Exemplars of Uncertainty Decomposition
Uncertainty plays a crucial role in the machine learning field.
IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis
Existing inverse rendering combined with neural rendering methods~\cite{zhang2021physg, zhang2022modeling} can only perform editable novel view synthesis on object-specific scenes, while we present intrinsic neural radiance fields, dubbed IntrinsicNeRF, which introduce intrinsic decomposition into the NeRF-based~\cite{mildenhall2020nerf} neural rendering method and can extend its application to room-scale scenes.
Gradient Norm Minimization of Nesterov Acceleration: $o(1/k^3)$
In the history of first-order algorithms, Nesterov's accelerated gradient descent (NAG) is one of the milestones.
Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks
Optical Coherence Tomography(OCT) is a non-invasive technique capturing cross-sectional area of the retina in micro-meter resolutions.
Higher-order accurate two-sample network inference and network hashing
Our method extends the classical two-sample t-test to the network setting.
PVO: Panoptic Visual Odometry
PVO models visual odometry (VO) and video panoptic segmentation (VPS) in a unified view, enabling the two tasks to facilitate each other.
Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation
Given a source product, a target product, and an art style image, our method produces a neural warping field that warps the source shape to imitate the geometric style of the target and a neural texture transformation network that transfers the artistic style to the warped source product.
MVT: Multi-view Vision Transformer for 3D Object Recognition
Nevertheless, multi-view CNN models cannot model the communications between patches from different views, limiting its effectiveness in 3D object recognition.
Diagnosing Errors in Video Relation Detectors
Video relation detection forms a new and challenging problem in computer vision, where subjects and objects need to be localized spatio-temporally and a predicate label needs to be assigned if and only if there is an interaction between the two.
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution
In this paper, we propose a novel blind SR framework to super-resolve LR images degraded by arbitrary blur kernel with accurate kernel estimation in frequency domain.
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?
Our main findings are: (i) predictive uncertainty indeed helps achieve reliable malware detection in the presence of dataset shift, but cannot cope with adversarial evasion attacks; (ii) approximate Bayesian methods are promising to calibrate and generalize malware detectors to deal with dataset shift, but cannot cope with adversarial evasion attacks; (iii) adversarial evasion attacks can render calibration methods useless, and it is an open problem to quantify the uncertainty associated with the predicted labels of adversarial examples (i. e., it is not effective to use predictive uncertainty to detect adversarial examples).
Social Fabric: Tubelet Compositions for Video Relation Detection
We also propose Social Fabric: an encoding that represents a pair of object tubelets as a composition of interaction primitives.
Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model
Inspired by biological evolution, we explain the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derive that both of them have consistent mathematical representation.
Contrastive Embedding for Generalized Zero-Shot Learning
To tackle this issue, we propose to integrate the generation model with the embedding model, yielding a hybrid GZSL framework.
DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection
Street scene change detection continues to capture researchers' interests in the computer vision community.
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations.
Ranked #104 on
Object Detection
on COCO test-dev
Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction
Our proposed method can reach a better trade-off between computation cost and reconstruction performance for MR reconstruction problem with good generalizability and offer insights to design neural networks for other medical image applications.
Curvilinear Distance Metric Learning
To address this issue, we first reveal that the traditional linear distance metric is equivalent to the cumulative arc length between the data pair's nearest points on the learned straight measurer lines.
GANSynth: Adversarial Neural Audio Synthesis
Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence.
Formal Specification and Verification of Smart Contracts for Azure Blockchain
In this paper, we describe the formal verification of Smart Contracts offered as part of the Azure Blockchain Content and Samples on github.
Programming Languages F.3.1
Adversarial Metric Learning
In distinguishment stage, a metric is exhaustively learned to try its best to distinguish both the adversarial pairs and the original training pairs.
Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift
Theoretically, we find that Dropout would shift the variance of a specific neural unit when we transfer the state of that network from train to test.
Deep Multi-Species Embedding
Additionally, we demonstrate the benefit of using a deep neural network to extract features within the embedding and show how they improve the predictive performance of species distribution modelling.
