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Found 7 papers, 4 papers with code
Detecting out-of-context objects using contextual cues
GCRN consists of two separate graphs to predict object labels based on the contextual cues in the image: 1) a representation graph to learn object features based on the neighboring objects and 2) a context graph to explicitly capture contextual cues from the neighboring objects.
Ranked #1 on
Anomaly Detection
on COCO-OOC
2nd Place Solution for SODA10M Challenge 2021 -- Continual Detection Track
In this technical report, we present our approaches for the continual object detection track of the SODA10M challenge.
RODEO: Replay for Online Object Detection
Humans can incrementally learn to do new visual detection tasks, which is a huge challenge for today's computer vision systems.
REMIND Your Neural Network to Prevent Catastrophic Forgetting
While there is neuroscientific evidence that the brain replays compressed memories, existing methods for convolutional networks replay raw images.
RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking
Accurate eye segmentation can improve eye-gaze estimation and support interactive computing based on visual attention; however, existing eye segmentation methods suffer from issues such as person-dependent accuracy, lack of robustness, and an inability to be run in real-time.
Ranked #1 on
Semantic Segmentation
on OpenEDS
VQD: Visual Query Detection in Natural Scenes
We propose Visual Query Detection (VQD), a new visual grounding task.
Ranked #1 on
Referring Expression Comprehension
on VQDv1
TallyQA: Answering Complex Counting Questions
Most counting questions in visual question answering (VQA) datasets are simple and require no more than object detection.
Ranked #3 on
Object Counting
on HowMany-QA
