Search Results for author:
Found 10 papers, 8 papers with code
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
The unprecedented photorealistic results achieved by recent text-to-image generative systems and their increasing use as plug-and-play content creation solutions make it crucial to understand their potential biases.
GeneCIS: A Benchmark for General Conditional Image Similarity
In this paper, we propose the GeneCIS ('genesis') benchmark, which measures models' ability to adapt to a range of similarity conditions.
Joint Embedding Predictive Architectures Focus on Slow Features
Many common methods for learning a world model for pixel-based environments use generative architectures trained with pixel-level reconstruction objectives.
Light-weight probing of unsupervised representations for Reinforcement Learning
Unsupervised visual representation learning offers the opportunity to leverage large corpora of unlabeled trajectories to form useful visual representations, which can benefit the training of reinforcement learning (RL) algorithms.
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
We also investigate the utility of our model as an object detector on a given label set when fine-tuned in a few-shot setting.
Ranked #1 on
Visual Question Answering (VQA)
on CLEVR-Humans
Generalized Referring Expression Comprehension
Phrase Grounding
+9
MDETR - Modulated Detection for End-to-End Multi-Modal Understanding
We also investigate the utility of our model as an object detector on a given label set when fine-tuned in a few-shot setting.
Ranked #2 on
Referring Expression Comprehension
on Talk2Car
(using extra training data)
End-to-End Object Detection with Transformers
We present a new method that views object detection as a direct set prediction problem.
Ranked #21 on
Panoptic Segmentation
on COCO minival
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning
While centralized reinforcement learning methods can optimally solve small MAC instances, they do not scale to large problems and they fail to generalize to scenarios different from those seen during training.
Multi-agent Reinforcement Learning
reinforcement-learning
+4
Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger
We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games.
Multi-Organ Cancer Classification and Survival Analysis
Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology.
