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The Density-assisted Adaptive Alignment Network (DAANet) is proposed, a novel framework that enables motion-adaptive and scale-aware alignment in VIC, and achieves state-of-the-art performance, outperforming prior methods and reducing Mean Squared Error by up to 31.6%.
This work proposes a global-to-local two-stage framework for zero-shot Chinese character recognition that achieves state-of-the-art performance under the character-level zero-shot setting, and shows that the explicit geometric priors and radical-level verification provide complementary improvements.
Look Before You Judge, a training-free framework that formulates explainable deepfake detection as a sequential evidence acquisition process, and improves detection accuracy by up to 12.8%, reduces CHAIR by up to 33.4% and hallucination rate by up to 21.3%, and outperforms representative training-free decoding and atte...
HalluPeer is introduced, a benchmark for detecting hallucinations in scientific peer reviews, providing aligned triples of paper content, human-written reviews, and hallucination-injected reviews, annotated for detection, classification, and localization.
This article introduces Uncertainty-Guided Adaptive Knowledge Distillation (UGAKD), a novel framework designed to enhance UDA while simultaneously reducing model size through targeted knowledge distillation and proposing a two-stage, difficulty-aware training scheme to facilitate learning.
RADIANCE is proposed, a training-free framework that treats inference as a closed-loop feedback process that consistently enhances compositional alignment and perceptual quality over state-of-the-art baselines.
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