Object Discovery
74 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
UVO
ShapeStacks
Latest papers
Detecting Every Object from Events
Object detection is critical in autonomous driving, and it is more practical yet challenging to localize objects of unknown categories: an endeavour known as Class-Agnostic Object Detection (CAOD).
CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers
In this paper, we introduce VoteCut, an innovative method for unsupervised object discovery that leverages feature representations from multiple self-supervised models.
MobileSAMv2: Faster Segment Anything to Everything
The efficiency bottleneck of SegEvery with SAM, however, lies in its mask decoder because it needs to first generate numerous masks with redundant grid-search prompts and then perform filtering to obtain the final valid masks.
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object Discovery
aitor-martinez-seras/snn-automotive-object-detection
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Besides performance, efficiency is a key design driver of technologies supporting vehicular perception.
Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation
In this paper, we propose a simple yet effective approach for self-supervised video object segmentation (VOS).
Unsupervised Musical Object Discovery from Audio
Our novel MusicSlots method adapts SlotAttention to the audio domain, to achieve unsupervised music decomposition.
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
katieluo88/drift
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NeurIPS 2023
Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences.
Three Pillars improving Vision Foundation Model Distillation for Lidar
valeoai/scalr
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In particular, thanks to our scalable distillation method named ScaLR, we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality.
CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
cvmi-lab/codet
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NeurIPS 2023
CoDet then leverages visual similarities to discover the co-occurring objects and align them with the shared concept.
CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detection
yangcaoai/CoDA_NeurIPS2023
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NeurIPS 2023
Open-vocabulary 3D Object Detection (OV-3DDet) aims to detect objects from an arbitrary list of categories within a 3D scene, which remains seldom explored in the literature.
