1,356 research outputs found
Peer to Peer Information Retrieval: An Overview
Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been developed. Unfortunately, none of these have seen widespread real- world adoption and thus, in contrast with file sharing, information retrieval is still dominated by centralised solutions. In this paper we provide an overview of the key challenges for peer-to-peer information retrieval and the work done so far. We want to stimulate and inspire further research to overcome these challenges. This will open the door to the development and large-scale deployment of real-world peer-to-peer information retrieval systems that rival existing centralised client-server solutions in terms of scalability, performance, user satisfaction and freedom
ClipCrop: Conditioned Cropping Driven by Vision-Language Model
Image cropping has progressed tremendously under the data-driven paradigm.
However, current approaches do not account for the intentions of the user,
which is an issue especially when the composition of the input image is
complex. Moreover, labeling of cropping data is costly and hence the amount of
data is limited, leading to poor generalization performance of current
algorithms in the wild. In this work, we take advantage of vision-language
models as a foundation for creating robust and user-intentional cropping
algorithms. By adapting a transformer decoder with a pre-trained CLIP-based
detection model, OWL-ViT, we develop a method to perform cropping with a text
or image query that reflects the user's intention as guidance. In addition, our
pipeline design allows the model to learn text-conditioned aesthetic cropping
with a small cropping dataset, while inheriting the open-vocabulary ability
acquired from millions of text-image pairs. We validate our model through
extensive experiments on existing datasets as well as a new cropping test set
we compiled that is characterized by content ambiguity
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