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Found 14 papers, 10 papers with code
Open-Vocabulary Temporal Action Detection with Off-the-Shelf Image-Text Features
Detecting actions in untrimmed videos should not be limited to a small, closed set of classes.
The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift
We introduce baseline results on our dataset across modalities as well as metrics for the detailed analysis of generalization with respect to geographic distribution shifts, vital for such a system to be deployed at-scale.
The surprising impact of mask-head architecture on novel class segmentation
Under this family, we study Mask R-CNN and discover that instead of its default strategy of training the mask-head with a combination of proposals and groundtruth boxes, training the mask-head with only groundtruth boxes dramatically improves its performance on novel classes.
DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes
In contrast, we propose a general-purpose method that works on both indoor and outdoor scenes.
RetinaTrack: Online Single Stage Joint Detection and Tracking
Traditionally multi-object tracking and object detection are performed using separate systems with most prior works focusing exclusively on one of these aspects over the other.
Ranked #1 on
Multiple Object Tracking
on Waymo Open Dataset
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera.
Pooling Pyramid Network for Object Detection
We share box predictors across all scales, and replace convolution between scales with max pooling.
Semantic Instance Segmentation via Deep Metric Learning
We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together.
Deep Metric Learning via Facility Location
Learning the representation and the similarity metric in an end-to-end fashion with deep networks have demonstrated outstanding results for clustering and retrieval.
Speed/accuracy trade-offs for modern convolutional object detectors
On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.
Ranked #221 on
Object Detection
on COCO test-dev
Im2Calories: Towards an Automated Mobile Vision Food Diary
We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.
Bayesian Dark Knowledge
We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities, e. g., for applications involving bandits or active learning.
What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision
We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task.
