Order-free Learning Alleviating Exposure Bias in Multi-label Classification

Multi-label classification (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural network (RNN) decoder to model the label dependency. However, training a RNN decoder requires a predefined order of labels, which is not directly available in the MLC specification. Besides, RNN thus trained tends to overfit the label combinations in the training set and have difficulty generating unseen label sequences. In this paper, we propose a new framework for MLC which does not rely on a predefined label order and thus alleviates exposure bias. The experimental results on three multi-label classification benchmark datasets show that our method outperforms competitive baselines by a large margin. We also find the proposed approach has a higher probability of generating label combinations not seen during training than the baseline models. The result shows that the proposed approach has better generalization capability

Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.Comment: CVPR 2023 camera-ready versio

Multimodal Transformer Distillation for Audio-Visual Synchronization

Audio-visual synchronization aims to determine whether the mouth movements and speech in the video are synchronized. VocaLiST reaches state-of-the-art performance by incorporating multimodal Transformers to model audio-visual interact information. However, it requires high computing resources, making it impractical for real-world applications. This paper proposed an MTDVocaLiST model, which is trained by our proposed multimodal Transformer distillation (MTD) loss. MTD loss enables MTDVocaLiST model to deeply mimic the cross-attention distribution and value-relation in the Transformer of VocaLiST. Our proposed method is effective in two aspects: From the distillation method perspective, MTD loss outperforms other strong distillation baselines. From the distilled model's performance perspective: 1) MTDVocaLiST outperforms similar-size SOTA models, SyncNet, and PM models by 15.69% and 3.39%; 2) MTDVocaLiST reduces the model size of VocaLiST by 83.52%, yet still maintaining similar performance.Comment: Submitted to ICASSP 202

Isolated tracheal injury after whiplash

AbstractWhiplash, a sudden acceleration–deceleration movement that can cause diverse symptoms such as neck pain, cervicogenic headache, restricted neck movement, tingling of the arms (central cord syndrome), and dizziness. However, laryngotracheal injuries after whiplash are extremely rare. We report the case of a 25-year-old Taiwanese female who presented to the emergency department with severe posterior midline neck pain after a rear-end motorcycle collision. Her C-spine X-ray showed no definite fracture; furthermore, her neck noncontrast-enhanced CT scan revealed paratracheal free air. She was discharged uneventfully after a 12-h observation period. Laryngotracheal injuries after whiplash, a hyperextension–hyperflexion movement, are potentially life-threatening and could lead to airway obstruction. Such injuries should not be overlooked. To the best of our knowledge, this is the first case report of isolated laryngotracheal injury after whiplash

Acute immune thrombocytopenic purpura in an adolescent with 2009 novel H1N1 influenza A virus infection

AbstractAlthough both leukopenia and thrombocytopenia are not uncommon hematological findings among patients with novel 2009 H1N1 influenza virus infection, immune thrombocytopenic purpura has rarely been shown to be associated with this novel influenza A infection. Here, we describe a previously healthy adolescent who presented with fever, influenza-like symptoms and acute onset of generalized petechiae and active oral mucosa bleeding on the third day of his illness. Severe leukopenia and thrombocytopenia were found. There was neither malignancy nor blast cells found by bone marrow aspiration. Real-time reverse transcriptase polymerase chain reaction was positive for novel 2009 H1N1 influenza infection. Novel influenza-associated atypical immune thrombocytopenic purpura was diagnosed. The patient recovered uneventfully after oseltamivir and methylprednisolone therapy