Object Discovery
50 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
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.
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.
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.
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.
Localizing Objects with Self-Supervised Transformers and no Labels
valeoai/LOST
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We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points.
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.
What Matters For Meta-Learning Vision Regression Tasks?
boschresearch/what-matters-for-meta-learning
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CVPR 2022
To this end, we (i) exhaustively evaluate common meta-learning techniques on these tasks, and (ii) quantitatively analyze the effect of various deep learning techniques commonly used in recent meta-learning algorithms in order to strengthen the generalization capability: data augmentation, domain randomization, task augmentation and meta-regularization.
Unsupervised Domain Adaptation for Nighttime Aerial Tracking
vision4robotics/udat
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CVPR 2022
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.
