1,052 research outputs found
Attention-Aware Face Hallucination via Deep Reinforcement Learning
Face hallucination is a domain-specific super-resolution problem with the
goal to generate high-resolution (HR) faces from low-resolution (LR) input
images. In contrast to existing methods that often learn a single
patch-to-patch mapping from LR to HR images and are regardless of the
contextual interdependency between patches, we propose a novel Attention-aware
Face Hallucination (Attention-FH) framework which resorts to deep reinforcement
learning for sequentially discovering attended patches and then performing the
facial part enhancement by fully exploiting the global interdependency of the
image. Specifically, in each time step, the recurrent policy network is
proposed to dynamically specify a new attended region by incorporating what
happened in the past. The state (i.e., face hallucination result for the whole
image) can thus be exploited and updated by the local enhancement network on
the selected region. The Attention-FH approach jointly learns the recurrent
policy network and local enhancement network through maximizing the long-term
reward that reflects the hallucination performance over the whole image.
Therefore, our proposed Attention-FH is capable of adaptively personalizing an
optimal searching path for each face image according to its own characteristic.
Extensive experiments show our approach significantly surpasses the
state-of-the-arts on in-the-wild faces with large pose and illumination
variations
Trustworthy Large Models in Vision: A Survey
The rapid progress of Large Models (LMs) has recently revolutionized various
fields of deep learning with remarkable grades, ranging from Natural Language
Processing (NLP) to Computer Vision (CV). However, LMs are increasingly
challenged and criticized by academia and industry due to their powerful
performance but untrustworthy behavior, which urgently needs to be alleviated
by reliable methods. Despite the abundance of literature on trustworthy LMs in
NLP, a systematic survey specifically delving into the trustworthiness of LMs
in CV remains absent. In order to mitigate this gap, we summarize four relevant
concerns that obstruct the trustworthy usage in vision of LMs in this survey,
including 1) human misuse, 2) vulnerability, 3) inherent issue and 4)
interpretability. By highlighting corresponding challenge, countermeasures, and
discussion in each topic, we hope this survey will facilitate readers'
understanding of this field, promote alignment of LMs with human expectations
and enable trustworthy LMs to serve as welfare rather than disaster for human
society
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