Object Discovery
33 papers with code • 0 benchmarks • 2 datasets
Object Discovery is the task of identifying previously unseen objects.
Source: Unsupervised Object Discovery and Segmentation of RGBD-images
Benchmarks
These leaderboards are used to track progress in Object Discovery
ShapeStacks
UVO
Most implemented papers
Learn To Pay Attention
SaoYan/LearnToPayAttention
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ICLR 2018
We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification.
GuessWhat?! Visual object discovery through multi-modal dialogue
GuessWhatGame/guesswhat
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CVPR 2017
Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images.
MONet: Unsupervised Scene Decomposition and Representation
deepmind/multi_object_datasets
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The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence.
Object-Centric Learning with Slot Attention
google-research/google-research
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NeurIPS 2020
Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features.
Efficient Dialog Policy Learning via Positive Memory Retention
This paper is concerned with the training of recurrent neural networks as goal-oriented dialog agents using reinforcement learning.
Learning Open-World Object Proposals without Learning to Classify
mcahny/object_localization_network
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In this paper, we identify that the problem is that the binary classifiers in existing proposal methods tend to overfit to the training categories.
Unsupervised Image Decomposition with Phase-Correlation Networks
angelvillar96/Unsupervised-Decomposition-PCDNet-1
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The ability to decompose scenes into their object components is a desired property for autonomous agents, allowing them to reason and act in their surroundings.
Unsupervised Domain Adaptation for Nighttime Aerial Tracking
Previous advances in object tracking mostly reported on favorable illumination circumstances while neglecting performance at nighttime, which significantly impeded the development of related aerial robot applications.
Ego-Object Discovery
Given an egocentric video/images sequence acquired by the camera, our algorithm uses both the appearance extracted by means of a convolutional neural network and an object refill methodology that allows to discover objects even in case of small amount of object appearance in the collection of images.
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work
We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy.
