Search Results for author:
Found 18 papers, 12 papers with code
Flamingo: a Visual Language Model for Few-Shot Learning
Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research.
Ranked #1 on
Action Recognition
on RareAct
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.
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)
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.
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
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 WebNLG v2.1
InstanceFormer: An Online Video Instance Segmentation Framework
We propose three novel components to model short-term and long-term dependency and temporal coherence.
Ranked #5 on
Video Instance Segmentation
on Youtube-VIS 2022 Validation
(using extra training data)
Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
Simulating high-resolution detector responses is a storage-costly and computationally intensive process that has long been challenging in particle physics.
Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays
The extraction of structured clinical information from free-text radiology reports in the form of radiology graphs has been demonstrated to be a valuable approach for evaluating the clinical correctness of report-generation methods.
PQA: Zero-shot Protein Question Answering for Free-form Scientific Enquiry with Large Language Models
We introduce the novel task of zero-shot Protein Question Answering (PQA) for free-form scientific enquiry.
MemeGraphs: Linking Memes to Knowledge Graphs
In this work, we propose to use scene graphs, that express images in terms of objects and their visual relations, and knowledge graphs as structured representations for meme classification with a Transformer-based architecture.
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.
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.
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.
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.
The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding
Although memory appears to be about the past, its main purpose is to support the agent in the present and the future.
Do DALL-E and Flamingo Understand Each Other?
To study this question, we propose a reconstruction task where Flamingo generates a description for a given image and DALL-E uses this description as input to synthesize a new image.
Synth$^2$: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings
The creation of high-quality human-labeled image-caption datasets presents a significant bottleneck in the development of Visual-Language Models (VLMs).
