437 research outputs found
CLIP Guided Image-perceptive Prompt Learning for Image Enhancement
Image enhancement is a significant research area in the fields of computer
vision and image processing. In recent years, many learning-based methods for
image enhancement have been developed, where the Look-up-table (LUT) has proven
to be an effective tool. In this paper, we delve into the potential of
Contrastive Language-Image Pre-Training (CLIP) Guided Prompt Learning,
proposing a simple structure called CLIP-LUT for image enhancement. We found
that the prior knowledge of CLIP can effectively discern the quality of
degraded images, which can provide reliable guidance. To be specific, We
initially learn image-perceptive prompts to distinguish between original and
target images using CLIP model, in the meanwhile, we introduce a very simple
network by incorporating a simple baseline to predict the weights of three
different LUT as enhancement network. The obtained prompts are used to steer
the enhancement network like a loss function and improve the performance of
model. We demonstrate that by simply combining a straightforward method with
CLIP, we can obtain satisfactory results.Comment: A trial work to the image enhancemen
A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information
Social-network-based recommendation algorithms leverage rich social network information to alleviate the problem of data sparsity and boost the recommendation performance. However, traditional social-network-based recommendation algorithms ignore high-order collaborative signals or only consider the first-order collaborative signal when learning users’ and items’ latent representations, resulting in suboptimal recommendation performance. In this paper, we propose a graph neural network (GNN)-based social recommendation model that utilizes the GNN framework to capture high-order collaborative signals in the process of learning the latent representations of users and items. Specifically, we formulate the representations of entities, i.e., users and items, by stacking multiple embedding propagation layers to recursively aggregate multi-hop neighborhood information on both the user–item interaction graph and the social network graph. Hence, the collaborative signals hidden in both the user–item interaction graph and the social network graph are explicitly injected into the final representations of entities. Moreover, we ease the training process of the proposed GNN-based social recommendation model and alleviate overfitting by adopting a lightweight GNN framework that only retains the neighborhood aggregation component and abandons the feature transformation and nonlinear activation components. The experimental results on two real-world datasets show that our proposed GNN-based social recommendation method outperforms the state-of-the-art recommendation algorithms
mPMR: A Multilingual Pre-trained Machine Reader at Scale
We present multilingual Pre-trained Machine Reader (mPMR), a novel method for
multilingual machine reading comprehension (MRC)-style pre-training. mPMR aims
to guide multilingual pre-trained language models (mPLMs) to perform natural
language understanding (NLU) including both sequence classification and span
extraction in multiple languages. To achieve cross-lingual generalization when
only source-language fine-tuning data is available, existing mPLMs solely
transfer NLU capability from a source language to target languages. In
contrast, mPMR allows the direct inheritance of multilingual NLU capability
from the MRC-style pre-training to downstream tasks. Therefore, mPMR acquires
better NLU capability for target languages. mPMR also provides a unified solver
for tackling cross-lingual span extraction and sequence classification, thereby
enabling the extraction of rationales to explain the sentence-pair
classification process.Comment: To appear at ACL 2023 main conferenc
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