Search Results for author:
Found 19 papers, 9 papers with code
Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering
The usage of convolutional neural networks (CNNs) for unsupervised image segmentation was investigated in this study.
Path Planning using Neural A* Search
We present Neural A*, a novel data-driven search method for path planning problems.
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category.
CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces
Multi-agent path planning (MAPP) in continuous spaces is a challenging problem with significant practical importance.
Salient object detection on hyperspectral images using features learned from unsupervised segmentation task
Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes.
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification
The conventional few-shot classification aims at learning a model on a large labeled base dataset and rapidly adapting to a target dataset that is from the same distribution as the base dataset.
OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching
Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedious.
Point Anywhere: Directed Object Estimation from Omnidirectional Images
One of the intuitive instruction methods in robot navigation is a pointing gesture.
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper. challenge.
Recognizing Activities of Daily Living with a Wrist-mounted Camera
We present a novel dataset and a novel algorithm for recognizing activities of daily living (ADL) from a first-person wearable camera.
An Integration of Bottom-up and Top-Down Salient Cues on RGB-D Data: Saliency from Objectness vs. Non-Objectness
Bottom-up and top-down visual cues are two types of information that helps the visual saliency models.
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path
Specifically, we train a deep convolutional network that can predict collision-free paths based on a map of the environment-- this is then used by a reinforcement learning algorithm to learn to closely follow the path.
Deep Reactive Planning in Dynamic Environments
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution.
Training Larger Networks for Deep Reinforcement Learning
Previous work has shown that this is mostly due to instability during training of deep RL agents when using larger networks.
Object Memory Transformer for Object Goal Navigation
This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-term observations of objects and scenes.
H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions
The world is filled with articulated objects that are difficult to determine how to use from vision alone, e. g., a door might open inwards or outwards.
Multi-goal Audio-visual Navigation using Sound Direction Map
However, there has been no proposal for a generalized navigation task combining these two types of tasks and using both visual and auditory information in a situation where multiple sound sources are goals.
Tactile Estimation of Extrinsic Contact Patch for Stable Placement
In particular, we estimate the contact patch between a grasped object and its environment using force and tactile observations to estimate the stability of the object during a contact formation.
Leveraging Large Language Model-based Room-Object Relationships Knowledge for Enhancing Multimodal-Input Object Goal Navigation
In this study, we propose a data-driven, modular-based approach, trained on a dataset that incorporates common-sense knowledge of object-to-room relationships extracted from a large language model.
