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
Most implemented papers
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.
Object Discovery from Motion-Guided Tokens
zpbao/discovery_obj_move
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CVPR 2023
Object discovery -- separating objects from the background without manual labels -- is a fundamental open challenge in computer vision.
Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset
Using our dataset and annotations, we release benchmarks for 3D object detection and 3D semantic segmentation using established metrics.
Detecting Every Object from Events
hatins/deoe
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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).
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.
Object-Proposal Evaluation Protocol is 'Gameable'
Finally, we plan to release an easy-to-use toolbox which combines various publicly available implementations of object proposal algorithms which standardizes the proposal generation and evaluation so that new methods can be added and evaluated on different datasets.
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.
Object proposal generation applying the distance dependent Chinese restaurant process
In application domains such as robotics, it is useful to represent the uncertainty related to the robot's belief about the state of its environment.
Deep Patch Learning for Weakly Supervised Object Classification and Discovery
Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background.
