Search Results for author:
Found 120 papers, 52 papers with code
Polyhedral Surface: Self-supervised Point Cloud Reconstruction Based on Polyhedral Surface
This method provides more flexible to represent sharp feature and surface boundary on open surface.
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments
In this work, we focus on imitator learning based on only one expert demonstration.
Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement
The capacity of a modern deep learning system to determine if a sample falls within its realm of knowledge is fundamental and important.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Accurate and Fast Compressed Video Captioning
Addressing this, we study video captioning from a different perspective in compressed domain, which brings multi-fold advantages over the existing pipeline: 1) Compared to raw images from the decoded video, the compressed video, consisting of I-frames, motion vectors and residuals, is highly distinguishable, which allows us to leverage the entire video for learning without manual sampling through a specialized model design; 2) The captioning model is more efficient in inference as smaller and less redundant information is processed.
Ranked #5 on
Video Captioning
on VATEX
Prompt-based Context- and Domain-aware Pretraining for Vision and Language Navigation
In the domain-aware stage, we apply a low-cost prompt tuning paradigm to learn soft visual prompts from an in-domain dataset for equipping the pretrained models with object-level and scene-level cross-modal alignment in VLN tasks.
Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction
Diffusion models have emerged as potential tools to tackle the challenge of sparse-view CT reconstruction, displaying superior performance compared to conventional methods.
SuperUDF: Self-supervised UDF Estimation for Surface Reconstruction
The key insight is that if the UDF is estimated correctly, the 3D points should be locally projected onto the underlying surface following the gradient of the UDF.
MIPS-Fusion: Multi-Implicit-Submaps for Scalable and Robust Online Neural RGB-D Reconstruction
For the first time, randomized optimization is made possible in neural tracking with several key designs to the learning process, enabling efficient and robust tracking even under fast camera motions.
2D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Clouds
The commonly adopted detect-then-match approach to registration finds difficulties in the cross-modality cases due to the incompatible keypoint detection and inconsistent feature description.
GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer
They seek correspondences over downsampled superpoints, which are then propagated to dense points.
PlaneRecTR: Unified Query Learning for 3D Plane Recovery from a Single View
3D plane recovery from a single image can usually be divided into several subtasks of plane detection, segmentation, parameter estimation and possibly depth estimation.
RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo
In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth.
Tensorformer: Normalized Matrix Attention Transformer for High-quality Point Cloud Reconstruction
Surface reconstruction from raw point clouds has been studied for decades in the computer graphics community, which is highly demanded by modeling and rendering applications nowadays.
Delving into Crispness: Guided Label Refinement for Crisp Edge Detection
Learning-based edge detection usually suffers from predicting thick edges.
Decoupled Diffusion Models: Image to Zero and Zero to Noise
We find that decoupling the diffusion process reduces the learning difficulty and the explicit transition probability improves the generative speed significantly.
Understanding how Differentially Private Generative Models Spend their Privacy Budget
Generative models trained with Differential Privacy (DP) are increasingly used to produce synthetic data while reducing privacy risks.
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression
We show that if these auxiliary densities are constructed such that they overlap with $p$ and $q$, then a multi-class logistic regression allows for estimating $\log p/q$ on the domain of any of the $K+2$ distributions and resolves the distribution shift problems of the current state-of-the-art methods.
An Implicit Alignment for Video Super-Resolution
Video super-resolution commonly uses a frame-wise alignment to support the propagation of information over time.
Ranked #1 on
Video Super-Resolution
on REDS4- 4x upscaling
Weakly-supervised Single-view Image Relighting
For re-rendering, we propose a differentiable specular rendering layer to render low-frequency non-Lambertian materials under various illuminations of spherical harmonics.
SOCS: Semantically-aware Object Coordinate Space for Category-Level 6D Object Pose Estimation under Large Shape Variations
We propose Semantically-aware Object Coordinate Space (SOCS) built by warping-and-aligning the objects guided by a sparse set of keypoints with semantically meaningful correspondence.
Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration
We first design a local spatial consistency measure over the deformation graph of the point cloud, which evaluates the spatial compatibility only between the correspondences in the vicinity of a graph node.
Learning Accurate Template Matching with Differentiable Coarse-to-Fine Correspondence Refinement
To tackle the challenges, we propose an accurate template matching method based on differentiable coarse-to-fine correspondence refinement.
NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Images
We study the problem of reconstructing 3D feature curves of an object from a set of calibrated multi-view images.
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
In this work, we present Multi-Symmetry Ensembles (MSE), a framework for constructing diverse ensembles by capturing the multiplicity of hypotheses along symmetry axes, which explore the hypothesis space beyond stochastic perturbations of model weights and hyperparameters.
SSR-2D: Semantic 3D Scene Reconstruction from 2D Images
In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations.
Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics
Light goods vehicles (LGV) used extensively in the last mile of delivery are one of the leading polluters in cities.
Multimodal Video Adapter for Parameter Efficient Video Text Retrieval
State-of-the-art video-text retrieval (VTR) methods usually fully fine-tune the pre-trained model (e. g.
Edge Preserving Implicit Surface Representation of Point Clouds
Among them, the differential Laplican regularizer can effectively alleviate the implicit surface unsmoothness caused by the point cloud quality deteriorates; Meanwhile, in order to reduce the excessive smoothing at the edge regions of implicit suface, we proposed a dynamic edge extract strategy for sampling near the sharp edge of point cloud, which can effectively avoid the Laplacian regularizer from smoothing all regions.
BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud Registration
Extensive experiments on real-world scenarios demonstrate that our method achieves the best of both worlds in accuracy, efficiency, and generalization.
Learning Physically Realizable Skills for Online Packing of General 3D Shapes
We study the problem of learning online packing skills for irregular 3D shapes, which is arguably the most challenging setting of bin packing problems.
Multi-resolution Monocular Depth Map Fusion by Self-supervised Gradient-based Composition
Therefore, we propose a novel depth map fusion module to combine the advantages of estimations with multi-resolution inputs.
3D-Aware Object Goal Navigation via Simultaneous Exploration and Identification
However, leveraging 3D scene representation can be prohibitively unpractical for policy learning in this floor-level task, due to low sample efficiency and expensive computational cost.
NIFT: Neural Interaction Field and Template for Object Manipulation
Given a few object manipulation demos, NIFT guides the generation of the interaction imitation for a new object instance by matching the Neural Interaction Template (NIT) extracted from the demos in the target Neural Interaction Field (NIF) defined for the new object.
6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features
We introduce a well-targeted down-sampling strategy that focuses more on edge area for efficient feature extraction of complex geometry.
HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks
One of the most important tasks in recommender systems is to predict the potential connection between two nodes under a specific edge type (i. e., relationship).
AutoTransition: Learning to Recommend Video Transition Effects
First we learn the embedding of video transitions through a video transition classification task.
Repairing Systematic Outliers by Learning Clean Subspaces in VAEs
Seeing as a systematic outlier is a combination of patterns of a clean instance and systematic error patterns, our main insight is that inliers can be modelled by a smaller representation (subspace) in a model than outliers.
dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generation
We propose a general, flexible, and scalable framework dpart, an open source Python library for differentially private synthetic data generation.
RayMVSNet: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo
In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth.
Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction
To resolve the sample efficiency issue in learning the high-dimensional and complex control of dexterous grasping, we propose an effective representation of grasping state characterizing the spatial interaction between the gripper and the target object.
DisARM: Displacement Aware Relation Module for 3D Detection
However, adopting relations between all the object or patch proposals for detection is inefficient, and an imbalanced combination of local and global relations brings extra noise that could mislead the training.
DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training
A standard hardware bottleneck when training deep neural networks is GPU memory.
3DRM:Pair-wise relation module for 3D object detection
Context has proven to be one of the most important factors in object layout reasoning for 3D scene understanding.
ARM3D: Attention-based relation module for indoor 3D object detection
In the field of 3D object detection, previous methods have been taking the advantage of context encoding, graph embedding, or explicit relation reasoning to extract relation context.
Geometric Transformer for Fast and Robust Point Cloud Registration
Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds.
RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structures
Each level of the tree corresponds to an assembly of shape parts, represented as implicit functions, to reconstruct the input shape.
STEdge: Self-training Edge Detection with Multi-layer Teaching and Regularization
In particular, we impose a consistency regularization which enforces the outputs from each of the multiple layers to be consistent for the input image and its perturbed counterpart.
Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision
In this paper, we introduce a neural architecture, termed Box2Seg, to learn point-level semantics of 3D point clouds with bounding box-level supervision.
Decoupling Makes Weakly Supervised Local Feature Better
Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences.
Ranked #1 on
Camera Localization
on Aachen Day-Night benchmark
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
In this paper, we propose a Bayesian-symbolic framework (BSP) for physical reasoning and learning that is close to human-level sample-efficiency and accuracy.
Targeted Neural Dynamical Modeling
These approaches, however, are limited in their ability to capture the underlying neural dynamics (e. g. linear) and in their ability to relate the learned dynamics back to the observed behaviour (e. g. no time lag).
Learning Efficient Online 3D Bin Packing on Packing Configuration Trees
PCT is a full-fledged description of the state and action space of bin packing which can support packing policy learning based on deep reinforcement learning (DRL).
Scaling Densities For Improved Density Ratio Estimation
As such, estimating density ratios accurately using only samples from $p$ and $q$ is of high significance and has led to a flurry of recent work in this direction.
Learning Practically Feasible Policies for Online 3D Bin Packing
In this problem, the items are delivered to the agent without informing the full sequence information.
Accelerating Video Object Segmentation with Compressed Video
We propose an efficient plug-and-play acceleration framework for semi-supervised video object segmentation by exploiting the temporal redundancies in videos presented by the compressed bitstream.
Objective-aware Traffic Simulation via Inverse Reinforcement Learning
Traffic simulators act as an essential component in the operating and planning of transportation systems.
ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion
We propose to tackle the difficulties of fast-motion camera tracking in the absence of inertial measurements using random optimization, in particular, the Particle Filter Optimization (PFO).
Potential Convolution: Embedding Point Clouds into Potential Fields
In this work, rather than defining a continuous or discrete kernel, we directly embed convolutional kernels into the learnable potential fields, giving rise to potential convolution.
Learning Fine-Grained Segmentation of 3D Shapes without Part Labels
Learning-based 3D shape segmentation is usually formulated as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of tags.
StablePose: Learning 6D Object Poses from Geometrically Stable Patches
According to the theory of geometric stability analysis, a minimal set of three planar/cylindrical patches are geometrically stable and determine the full 6DoFs of the object pose.
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction
In this paper, we propose a selective sensing framework that adopts the novel concept of data-driven nonuniform subsampling to reduce the dimensionality of acquired signals while retaining the information of interest in a computation-free fashion.
A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics
As such, learning the laws is then reduced to symbolic regression and Bayesian inference methods are used to obtain the distribution of unobserved properties.
Study of charmonium-like and fully-charm tetraquark spectroscopy
The masses of tetraquark states of all $qc\bar q \bar c$ and $cc\bar c \bar c$ quark configurations are evaluated in a constituent quark model, where the Cornell-like potential and one-gluon exchange spin-spin coupling are employed.
High Energy Physics - Phenomenology
Hausdorff Point Convolution with Geometric Priors
We show that HPC constitutes a powerful point feature learning with a rather compact set of only four types of geometric priors as kernels.
On Learning the Right Attention Point for Feature Enhancement
We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e. g., classification and segmentation.
A geometry-based relaxation algorithm for equilibrating a trivalent polygonal network in two dimensions and its implications
The succeed of simulation strongly supports the ellipse packing hypothesis that was proposed to explain the dynamic behaviors of a trivalent 2D structure.
Biological Physics Adaptation and Self-Organizing Systems Cell Behavior
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.
WarpCore: A Library for fast Hash Tables on GPUs
The rapidly growing amount of data emerging in many fields motivated the need for accelerated hash tables designed for modern parallel architectures.
Distributed, Parallel, and Cluster Computing
SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images
We propose an end-to-end deep neural network which is able to predict both reflectional and rotational symmetries of 3D objects present in the input RGB-D image.
PIE-NET: Parametric Inference of Point Cloud Edges
We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data.
Online 3D Bin Packing with Constrained Deep Reinforcement Learning
We solve a challenging yet practically useful variant of 3D Bin Packing Problem (3D-BPP).
AReLU: Attention-based Rectified Linear Unit
Adding the attention module with a rectified linear unit (ReLU) results in an amplification of positive elements and a suppression of negative ones, both with learned, data-adaptive parameters.
Telescoping Density-Ratio Estimation
Density-ratio estimation via classification is a cornerstone of unsupervised learning.
MLCVNet: Multi-Level Context VoteNet for 3D Object Detection
We demonstrate these by capturing contextual information at patch, object and scene levels.
Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation
Online semantic 3D segmentation in company with real-time RGB-D reconstruction poses special challenges such as how to perform 3D convolution directly over the progressively fused 3D geometric data, and how to smartly fuse information from frame to frame.
Learning in the Frequency Domain
Experiment results show that learning in the frequency domain with static channel selection can achieve higher accuracy than the conventional spatial downsampling approach and meanwhile further reduce the input data size.
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
Since DynamicPPL is a modular, stand-alone library, any probabilistic programming system written in Julia, such as Turing. jl, can use DynamicPPL to specify models and trace their model parameters.
Deep Differentiable Grasp Planner for High-DOF Grippers
We present an end-to-end algorithm for training deep neural networks to grasp novel objects.
Robotics
Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation
To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category.
Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering
Based on further studying the low-rank subspace clustering (LRSC) and L2-graph subspace clustering algorithms, we propose a F-graph subspace clustering algorithm with a symmetric constraint (FSSC), which constructs a new objective function with a symmetric constraint basing on F-norm, whose the most significant advantage is to obtain a closed-form solution of the coefficient matrix.
Reinforcement Learning-based Visual Navigation with Information-Theoretic Regularization
The regularization maximizes the mutual information between navigation actions and visual observation transforms of an agent, thus promoting more informed navigation decisions.
Robotics
Decoupling Features and Coordinates for Few-shot RGB Relocalization
To address this issue, we approach camera relocalization with a decoupled solution where feature extraction, coordinate regression, and pose estimation are performed separately.
PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes
We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly.
Bijectors.jl: Flexible transformations for probability distributions
Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning.
AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms
Stan's Hamilton Monte Carlo (HMC) has demonstrated remarkable sampling robustness and efficiency in a wide range of Bayesian inference problems through carefully crafted adaption schemes to the celebrated No-U-Turn sampler (NUTS) algorithm.
Rescan: Inductive Instance Segmentation for Indoor RGBD Scans
In depth-sensing applications ranging from home robotics to AR/VR, it will be common to acquire 3D scans of interior spaces repeatedly at sparse time intervals (e. g., as part of regular daily use).
Experimental demonstration of entanglement-enabled universal quantum cloning in a circuit
No-cloning theorem forbids perfect cloning of an unknown quantum state.
Quantum Physics
Multiple instance dense connected convolution neural network for aerial image scene classification
While the current convolution neural network tends to extract global features and global semantic information in a scene, the geo-spatial objects can be located at anywhere in an aerial image scene and their spatial arrangement tends to be more complicated.
Building change detection based on multi-scale filtering and grid partition
Exhaustive experiments indicate that the proposed method can detect building change types directly and outperform the current multi-index learning method.
Active Scene Understanding via Online Semantic Reconstruction
In our method, the exploratory robot scanning is both driven by and targeting at the recognition and segmentation of semantic objects from the scene.
NeoNav: Improving the Generalization of Visual Navigation via Generating Next Expected Observations
First, the latent distribution is conditioned on current observations and the target view, leading to a model-based, target-driven navigation.
Learning Part Generation and Assembly for Structure-aware Shape Synthesis
Enlightened by the fact that 3D shape structure is characterized as part composition and placement, we propose to model 3D shape variations with a part-aware deep generative network, coined as PAGENet.
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Determining the positions of neurons in an extracellular recording is useful for investigating functional properties of the underlying neural circuitry.
Diagnosing Reinforcement Learning for Traffic Signal Control
In this paper, we propose to re-examine the RL approaches through the lens of classic transportation theory.
Learning Phase Competition for Traffic Signal Control
Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions.
CoLight: Learning Network-level Cooperation for Traffic Signal Control
To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.
Spatiotemporal CNN for Video Object Segmentation
Specifically, the temporal coherence branch pretrained in an adversarial fashion from unlabeled video data, is designed to capture the dynamic appearance and motion cues of video sequences to guide object segmentation.
Ranked #2 on
Semi-Supervised Video Object Segmentation
on YouTube
AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss
While the part prior network can be trained with noisy and inconsistently segmented shapes, the final output of AdaCoSeg is a consistent part labeling for the input set, with each shape segmented into up to (a user-specified) K parts.
Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes
For the task of mobility analysis of 3D shapes, we propose joint analysis for simultaneous motion part segmentation and motion attribute estimation, taking a single 3D model as input.
Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction
Indoor scenes exhibit rich hierarchical structure in 3D object layouts.
PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation
Meanwhile, to increase the segmentation accuracy at each node, we enhance the recursive contextual feature with the shape feature extracted for the corresponding part.
Ranked #14 on
3D Part Segmentation
on ShapeNet-Part
(Class Average IoU metric)
Generating Grasp Poses for a High-DOF Gripper Using Neural Networks
The quality of the grasp poses is on par with the groundtruth poses in the dataset.
Robotics
SCORES: Shape Composition with Recursive Substructure Priors
The network may significantly alter the geometry and structure of the input parts and synthesize a novel shape structure based on the inputs, while adding or removing parts to minimize a structure plausibility loss.
Learning to Group and Label Fine-Grained Shape Components
We propose to generate part hypotheses from the components based on a hierarchical grouping strategy, and perform labeling on those part groups instead of directly on the components.
VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification
Multi-view deep neural network is perhaps the most successful approach in 3D shape classification.
Triangle Lasso for Simultaneous Clustering and Optimization in Graph Datasets
The reason is that it finds the similar instances according to their features directly, which is usually impacted by the imperfect data, and thus returns sub-optimal results.
Learning Discriminative 3D Shape Representations by View Discerning Networks
In this network, a Score Generation Unit is devised to evaluate the quality of each projected image with score vectors.
LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction
We propose a scalable Laplacian pyramid reconstructive adversarial network (LAPRAN) that enables high-fidelity, flexible and fast CS images reconstruction.
GRAINS: Generative Recursive Autoencoders for INdoor Scenes
We present a generative neural network which enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently.
Graphics
Generative Ratio Matching Networks
In this work, we take their insight of using kernels as fixed adversaries further and present a novel method for training deep generative models that does not involve saddlepoint optimization.
Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines
We present a semi-supervised co-analysis method for learning 3D shape styles from projected feature lines, achieving style patch localization with only weak supervision.
Im2Struct: Recovering 3D Shape Structure from a Single RGB Image
We propose to recover 3D shape structures from single RGB images, where structure refers to shape parts represented by cuboids and part relations encompassing connectivity and symmetry.
PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction
We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction.
LCANet: End-to-End Lipreading with Cascaded Attention-CTC
In this paper, we propose LCANet, an end-to-end deep neural network based lipreading system.
Ranked #2 on
Lipreading
on GRID corpus (mixed-speech)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge
Interpreting black box classifiers, such as deep networks, allows an analyst to validate a classifier before it is deployed in a high-stakes setting.
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures.
A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks
For processing static data in large batch sizes, the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9. 5x higher energy efficiency.
A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing
Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring.
CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing
This paper addresses the real-time encoding-decoding problem for high-frame-rate video compressive sensing (CS).
Recurrent 3D Attentional Networks for End-to-End Active Object Recognition
Active vision is inherently attention-driven: The agent actively selects views to attend in order to fast achieve the vision task while improving its internal representation of the scene being observed.
