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Found 8 papers, 6 papers with code
Matryoshka Query Transformer for Large Vision-Language Models
This raises the question: can we achieve flexibility in the number of visual tokens to suit different tasks and computational resources?
What's "up" with vision-language models? Investigating their struggle with spatial reasoning
Recent vision-language (VL) models are powerful, but can they reliably distinguish "right" from "left"?
Text encoders bottleneck compositionality in contrastive vision-language models
We first curate CompPrompts, a set of increasingly compositional image captions that VL models should be able to capture (e. g., single object, to object+property, to multiple interacting objects).
Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models
As general purpose vision models get increasingly effective at a wide set of tasks, it is imperative that they be consistent across the tasks they support.
Webly Supervised Concept Expansion for General Purpose Vision Models
This work presents an effective and inexpensive alternative: learn skills from supervised datasets, learn concepts from web image search, and leverage a key characteristic of GPVs: the ability to transfer visual knowledge across skills.
Ranked #2 on
Visual Question Answering (VQA)
on GRIT
Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture
To reduce the time and expertise required to develop new applications, we would like to create general purpose vision systems that can learn and perform a range of tasks without any modification to the architecture or learning process.
Towards General Purpose Vision Systems
To reduce the time and expertise required to develop new applications, we would like to create general purpose vision systems that can learn and perform a range of tasks without any modification to the architecture or learning process.
Selective Question Answering under Domain Shift
In this work, we propose the setting of selective question answering under domain shift, in which a QA model is tested on a mixture of in-domain and out-of-domain data, and must answer (i. e., not abstain on) as many questions as possible while maintaining high accuracy.
