Search Results for author:
Found 9 papers, 4 papers with code
Pixel-Level BPE for Auto-Regressive Image Generation
Pixel-level autoregression with Transformer models (Image GPT or iGPT) is one of the recent approaches to image generation that has not received massive attention and elaboration due to quadratic complexity of attention as it imposes huge memory requirements and thus restricts the resolution of the generated images.
OmniFusion Technical Report
We propose an \textit{OmniFusion} model based on a pretrained LLM and adapters for visual modality.
Ranked #39 on
Visual Question Answering
on MM-Vet
The Shape of Learning: Anisotropy and Intrinsic Dimensions in Transformer-Based Models
In this study, we present an investigation into the anisotropy dynamics and intrinsic dimension of embeddings in transformer architectures, focusing on the dichotomy between encoders and decoders.
Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs
Pre-trained Text-to-Text Language Models (LMs), such as T5 or BART yield promising results in the Knowledge Graph Question Answering (KGQA) task.
Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion
Text-to-image generation is a significant domain in modern computer vision and has achieved substantial improvements through the evolution of generative architectures.
Ranked #22 on
Text-to-Image Generation
on MS COCO
MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering
Knowledge Graphs (KGs) are symbolically structured storages of facts.
SkoltechNLP at SemEval-2021 Task 2: Generating Cross-Lingual Training Data for the Word-in-Context Task
In our experiments, we used a neural system based on the XLM-R, a pre-trained transformer-based masked language model, as a baseline.
Darker than Black-Box: Face Reconstruction from Similarity Queries
Several methods for inversion of face recognition models were recently presented, attempting to reconstruct a face from deep templates.
Black-Box Face Recovery from Identity Features
In this work, we present a novel algorithm based on an it-erative sampling of random Gaussian blobs for black-box face recovery, given only an output feature vector of deep face recognition systems.
