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06/30/2023
Situated Cameras, Situated Knowledges: Towards an Egocentric Epistemology for Computer Vision
In her influential 1988 paper, Situated Knowledges, Donna Haraway uses v...
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03/29/2023
A Tensor-based Convolutional Neural Network for Small Dataset Classification
Inspired by the ConvNets with structured hidden representations, we prop...
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03/05/2023
SePaint: Semantic Map Inpainting via Multinomial Diffusion
Prediction beyond partial observations is crucial for robots to navigate...
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01/19/2023
LoCoNet: Long-Short Context Network for Active Speaker Detection
Active Speaker Detection (ASD) aims to identify who is speaking in each ...
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12/09/2022
VindLU: A Recipe for Effective Video-and-Language Pretraining
The last several years have witnessed remarkable progress in video-and-l...
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09/22/2022
Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper
The success of deep learning has led to the rapid transformation and gro...
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08/15/2022
Action Recognition based on Cross-Situational Action-object Statistics
Machine learning models of visual action recognition are typically train...
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07/26/2022
Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds
Previous works for LiDAR-based 3D object detection mainly focus on the s...
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04/22/2022
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition
One-shot fine-grained visual recognition often suffers from the problem ...
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12/19/2021
Controlling the Quality of Distillation in Response-Based Network Compression
The performance of a distillation-based compressed network is governed b...
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11/15/2021
Error Diagnosis of Deep Monocular Depth Estimation Models
Estimating depth from a monocular image is an ill-posed problem: when th...
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10/29/2021
Polyline Based Generative Navigable Space Segmentation for Autonomous Visual Navigation
Detecting navigable space is a fundamental capability for mobile robots ...
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07/15/2021
Applying the Case Difference Heuristic to Learn Adaptations from Deep Network Features
The case difference heuristic (CDH) approach is a knowledge-light method...
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07/02/2021
A Survey on Deep Learning Technique for Video Segmentation
Video segmentation, i.e., partitioning video frames into multiple segmen...
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06/12/2021
Reverse-engineer the Distributional Structure of Infant Egocentric Views for Training Generalizable Image Classifiers
We analyze egocentric views of attended objects from infants. This paper...
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04/06/2021
How to Accelerate Capsule Convolutions in Capsule Networks
How to improve the efficiency of routing procedures in CapsNets has been...
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11/23/2020
Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning
We present a novel way for self-supervised video representation learning...
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11/17/2020
Whose hand is this? Person Identification from Egocentric Hand Gestures
Recognizing people by faces and other biometrics has been extensively st...
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10/08/2020
Deep Tiered Image Segmentation forDetecting Internal Ice Layers in Radar Imagery
Understanding the structure of the ice at the Earth's poles is important...
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06/04/2020
A Computational Model of Early Word Learning from the Infant's Point of View
Human infants have the remarkable ability to learn the associations betw...
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04/06/2020
When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos
Video anomaly detection (VAD) has been extensively studied. However, res...
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03/31/2020
HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation
Hand-object pose estimation (HOPE) aims to jointly detect the poses of b...
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03/12/2020
Interaction Graphs for Object Importance Estimation in On-road Driving Videos
A vehicle driving along the road is surrounded by many objects, but only...
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03/10/2020
Learning Video Object Segmentation from Unlabeled Videos
We propose a new method for video object segmentation (VOS) that address...
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01/19/2020
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
This work proposes a novel attentive graph neural network (AGNN) for zer...
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12/18/2019
P-CapsNets: a General Form of Convolutional Neural Networks
We propose Pure CapsNets (P-CapsNets) which is a generation of normal CN...
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11/17/2019
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition
One-shot fine-grained visual recognition often suffers from the problem ...
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10/31/2019
A Self Validation Network for Object-Level Human Attention Estimation
Due to the foveated nature of the human vision system, people can focus ...
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06/04/2019
Active Object Manipulation Facilitates Visual Object Learning: An Egocentric Vision Study
Inspired by the remarkable ability of the infant visual learning system,...
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04/12/2019
Conveying Situational Information to People with Visual Impairments
Knowing who is in one's vicinity is key to managing privacy in everyday ...
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04/09/2019
Embodied Visual Recognition
Passive visual systems typically fail to recognize objects in the amodal...
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12/02/2018
Unsupervised Domain Adaptation using Generative Models and Self-ensembling
Transferring knowledge across different datasets is an important approac...
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08/27/2018
Generalized Capsule Networks with Trainable Routing Procedure
CapsNet (Capsule Network) was first proposed by capsule and later anothe...
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02/21/2018
Detecting Small, Densely Distributed Objects with Filter-Amplifier Networks and Loss Boosting
Detecting small, densely distributed objects is a significant challenge:...
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06/20/2017
Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation
Recent work in computer vision has yielded impressive results in automat...
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03/15/2017
A Hybrid Supervised-unsupervised Method on Image Topic Visualization with Convolutional Neural Network and LDA
Given the progress in image recognition with recent data driven paradigm...
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03/15/2017
A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks
A key problem in automatic analysis and understanding of scientific pape...
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10/07/2016
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
Neural sequence models are widely used to model time-series data in many...
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06/24/2016
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
Many practical perception systems exist within larger processes that inc...
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01/01/2016
Sentiment/Subjectivity Analysis Survey for Languages other than English
Subjective and sentiment analysis have gained considerable attention rec...
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11/19/2015
Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks
Convolutional Neural Networks have achieved state-of-the-art performance...
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09/26/2012