Search Results for author:
Found 12 papers, 6 papers with code
Video OWL-ViT: Temporally-consistent open-world localization in video
Our model is end-to-end trainable on video data and enjoys improved temporal consistency compared to tracking-by-detection baselines, while retaining the open-world capabilities of the backbone detector.
Conditional Object-Centric Learning from Video
Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built.
ViViT: A Video Vision Transformer
We present pure-transformer based models for video classification, drawing upon the recent success of such models in image classification.
Ranked #8 on
Action Classification
on Moments in Time
(Top 5 Accuracy metric, using extra
training data)
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Object-Centric Learning with Slot Attention
Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features.
Cross-lingual Character-Level Neural Morphological Tagging
Even for common NLP tasks, sufficient supervision is not available in many languages {--} morphological tagging is no exception.
Cross-lingual, Character-Level Neural Morphological Tagging
Even for common NLP tasks, sufficient supervision is not available in many languages -- morphological tagging is no exception.
How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse?
In this paper, we study the impact of noisy input.
Common Round: Application of Language Technologies to Large-Scale Web Debates
Web debates play an important role in enabling broad participation of constituencies in social, political and economic decision-taking.
An Extensive Empirical Evaluation of Character-Based Morphological Tagging for 14 Languages
This paper investigates neural character-based morphological tagging for languages with complex morphology and large tag sets.
Neural Morphological Tagging from Characters for Morphologically Rich Languages
We systematically explore a variety of neural architectures (DNN, CNN, CNNHighway, LSTM, BLSTM) to obtain character-based word vectors combined with bidirectional LSTMs to model across-word context in an end-to-end setting.
End-to-End Text-Dependent Speaker Verification
In this paper we present a data-driven, integrated approach to speaker verification, which maps a test utterance and a few reference utterances directly to a single score for verification and jointly optimizes the system's components using the same evaluation protocol and metric as at test time.
