Current object detectors are limited in vocabulary size due to the small scale of detection
datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as …

Unsupervised semantic segmentation aims to discover and localize semantically meaningful
categories within image corpora without any form of annotation. To solve this task …

The goal of semi-supervised object detection is to learn a detection model using only a few
labeled data and large amounts of unlabeled data, thereby reducing the cost of data …

We propose an embarrassingly simple point annotation scheme to collect weak supervision
for instance segmentation. In addition to bounding boxes, we collect binary labels for a set of …

We tackle the problem of localizing temporal intervals of actions with only a single frame
label for each action instance for training. Owing to label sparsity, existing work fails to learn …

Abstract 3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving
vehicles. Existing methods are predominantly based on the tracking-by-detection pipeline …

We consider the problem of omni-supervised object detection, which can use unlabeled,
fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for …

We propose a novel point annotated setting for the weakly semi-supervised object detection
task, in which the dataset comprises small fully annotated images and large weakly …

Existing semi-supervised learning (SSL) algorithms use a single weight to balance the loss
of labeled and unlabeled examples, ie, all unlabeled examples are equally weighted. But …

We introduce an approach for selecting objects in neural volumetric 3D representations,
such as multi-plane images (MPI) and neural radiance fields (NeRF). Our approach takes a …