Search Results for author:
Found 11 papers, 8 papers with code
Do DALL-E and Flamingo Understand Each Other?
In this work, we argue that the best text or caption for a given image is the text which would generate the image which is the most similar to that image.
InstanceFormer: An Online Video Instance Segmentation Framework
We propose three novel components to model short-term and long-term dependency and temporal coherence.
Ranked #1 on
Video Instance Segmentation
on Youtube-VIS 2022 Validation
(using extra training data)
Relationformer: A Unified Framework for Image-to-Graph Generation
We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly.
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space
It is essential for safety-critical applications of deep neural networks to determine when new inputs are significantly different from the training distribution.
Box Supervised Video Segmentation Proposal Network
Video Object Segmentation (VOS) has been targeted by various fully-supervised and self-supervised approaches.
OODformer: Out-Of-Distribution Detection Transformer
A serious problem in image classification is that a trained model might perform well for input data that originates from the same distribution as the data available for model training, but performs much worse for out-of-distribution (OOD) samples.
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering
We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs.
Scenes and Surroundings: Scene Graph Generation using Relation Transformer
Identifying objects in an image and their mutual relationships as a scene graph leads to a deep understanding of image content.
Scene Graph Reasoning for Visual Question Answering
We propose a novel method that approaches the task by performing context-driven, sequential reasoning based on the objects and their semantic and spatial relationships present in the scene.
Relation Transformer Network
In this work, we propose a novel transformer formulation for scene graph generation and relation prediction.
Improving Visual Relation Detection using Depth Maps
We argue that depth maps can additionally provide valuable information on object relations, e. g. helping to detect not only spatial relations, such as standing behind, but also non-spatial relations, such as holding.
Ranked #1 on
Relationship Detection
on VRD
