Search Results for author:
Found 11 papers, 5 papers with code
Flamingo: a Visual Language Model for Few-Shot Learning
Building models that can be rapidly adapted to numerous tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research.
Ranked #1 on
Zero-Shot Learning
on iVQA
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.
The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding
In our model, perception, episodic memory, and semantic memory are realized by different functional and operational modes of the oscillating interactions between an index layer and a representation layer in a bilayer tensor network (BTN).
Improving Scene Graph Classification by Exploiting Knowledge from Texts
We show that by fine-tuning the classification pipeline with the extracted knowledge from texts, we can achieve ~8x more accurate results in scene graph classification, ~3x in object classification, and ~1. 5x in predicate classification, compared to the supervised baselines with only 1% of the annotated images.
Classification by Attention: Scene Graph Classification with Prior Knowledge
A major challenge in scene graph classification is that the appearance of objects and relations can be significantly different from one image to another.
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.
Ranked #1 on
Link Prediction
on WN18
(training time (s) metric)
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
The Tensor Brain: Semantic Decoding for Perception and Memory
In particular, we propose that explicit perception and declarative memories require a semantic decoder, which, in a simple realization, is based on four layers: First, a sensory memory layer, as a buffer for sensory input, second, an index layer representing concepts, third, a memoryless representation layer for the broadcasting of information ---the "blackboard", or the "canvas" of the brain--- and fourth, a working memory layer as a processing center and data buffer.
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.
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing
To this end, we present the first approach to unsupervised text generation from KGs and show simultaneously how it can be used for unsupervised semantic parsing.
Ranked #1 on
Unsupervised KG-to-Text Generation
on VG graph-text
Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks
We propose an inverse reinforcement learning (IRL) approach using Deep Q-Networks to extract the rewards in problems with large state spaces.
