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Found 13 papers, 9 papers with code
TUDa at WMT21: Sentence-Level Direct Assessment with Adapters
This paper presents our submissions to the WMT2021 Shared Task on Quality Estimation, Task 1 Sentence-Level Direct Assessment.
Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model
Most Large Vision-Language Models (LVLMs) to date are trained predominantly on English data, which makes them struggle to understand non-English input and fail to generate output in the desired target language.
African or European Swallow? Benchmarking Large Vision-Language Models for Fine-Grained Object Classification
We benchmark 12 public LVLMs on \texttt{FOCI} and show that it tests for a \textit{complementary skill} to established image understanding and reasoning benchmarks.
Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models?
Large vision-language models (LVLMs) have recently dramatically pushed the state of the art in image captioning and many image understanding tasks (e. g., visual question answering).
InstructIR: High-Quality Image Restoration Following Human Instructions
All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model.
mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs
Modular vision-language models (Vision-LLMs) align pretrained image encoders with (frozen) large language models (LLMs) and post-hoc condition LLMs to `understand' the image input.
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations
Vision-and-language (VL) models with separate encoders for each modality (e. g., CLIP) have become the go-to models for zero-shot image classification and image-text retrieval.
One does not fit all! On the Complementarity of Vision Encoders for Vision and Language Tasks
While many VEs -- of different architectures, trained on different data and objectives -- are publicly available, they are not designed for the downstream V+L tasks.
UKP-SQUARE: An Online Platform for Question Answering Research
Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e. g., extractive, abstractive), require different model architectures (e. g., generative, discriminative), and setups (e. g., with or without retrieval).
xGQA: Cross-Lingual Visual Question Answering
In this work, we address this gap and provide xGQA, a new multilingual evaluation benchmark for the visual question answering task.
TWEAC: Transformer with Extendable QA Agent Classifiers
We argue that there exist a wide range of specialized QA agents in literature.
Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval
Current state-of-the-art approaches to cross-modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image.
AdapterDrop: On the Efficiency of Adapters in Transformers
Massively pre-trained transformer models are computationally expensive to fine-tune, slow for inference, and have large storage requirements.
