Object Discovery
89 papers with code • 0 benchmarks • 3 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
UVO
ShapeStacks
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.
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.
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.
Vision Transformers Need Registers
Transformers have recently emerged as a powerful tool for learning visual representations.
GuessWhat?! Visual object discovery through multi-modal dialogue
zhanyang-nwpu/rsvg-pytorch
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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.
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.
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.
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
Data efficiency and robustness to task-irrelevant perturbations are long-standing challenges for deep reinforcement learning algorithms.
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
applied-ai-lab/genesis
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ICLR 2020
Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning.
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.
