Few-Shot Image Recognition by Predicting Parameters from Activations

In this paper, we are interested in the few-shot learning problem. In particular, we focus on a challenging scenario where the number of categories is large and the number of examples per novel category is very limited, e.g. 1, 2, or 3. Motivated by the close relationship between the parameters and the activations in a neural network associated with the same category, we propose a novel method that can adapt a pre-trained neural network to novel categories by directly predicting the parameters from the activations. Zero training is required in adaptation to novel categories, and fast inference is realized by a single forward pass. We evaluate our method by doing few-shot image recognition on the ImageNet dataset, which achieves the state-of-the-art classification accuracy on novel categories by a significant margin while keeping comparable performance on the large-scale categories. We also test our method on the MiniImageNet dataset and it strongly outperforms the previous state-of-the-art methods

Association of lactate-to-albumin ratio with in-hospital and intensive care unit mortality in patients with intracerebral hemorrhage

BackgroundIntracerebral hemorrhage (ICH) is a severe stroke subtype with a high mortality rate; the lactate-to-albumin ratio (LAR) is a new biomarker for predicting clinical outcomes in patients with ICH. However, the relationship between LAR and mortality in patients with ICH treated in the intensive care unit (ICU) remains controversial. Therefore, in this study, we aimed to investigate the association between LAR and in-hospital and ICU mortality in patients with ICH.MethodsPatients with ICH were selected from the Medical Information Mart for Intensive Care III (MIMIC-III) database; their clinical information, including baseline characteristics, vital signs, comorbidities, laboratory test results, and scoring systems, was extracted. Univariate and multivariate Cox proportional hazards analyses were used to investigate the association of LAR with in-hospital and ICU mortality. The maximum selection statistical method and subgroup analysis were used to investigate these relationships further. Kaplan–Meier (KM) analysis was used to draw survival curves.ResultsThis study enrolled 237 patients with ICH whose lactate and albumin levels, with median values of 1.975 and 3.6 mg/dl, respectively, were measured within the first 24 h after ICU admission. LAR had an association with increased risk of in-hospital mortality [unadjusted hazards ratio (HR), 1.79; 95% confidence interval (CI), 1.32–2.42; p < 0.001] and ICU mortality (unadjusted HR, 1.88; 95% CI, 1.38–2.55; p < 0.001). A cut-off value of 0.963 mg/dl was used to classify patients into high LAR (≥0.963) and low LAR (<0.963) groups, and survival curves suggested that those two groups had significant survival differences (p = 0.0058 and 0.0048, respectively). Furthermore, the high LAR group with ICH had a significantly increased risk of in-hospital and ICU mortality compared to the low LAR group.ConclusionOur study suggests that a high LAR is associated with an increased risk of in-hospital and ICU mortality in patients with ICH. Thus, the LAR is a useful prognostic predictor of clinical outcomes in patients with ICH

Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers

Although vision transformers (ViTs) have shown promising results in various computer vision tasks recently, their high computational cost limits their practical applications. Previous approaches that prune redundant tokens have demonstrated a good trade-off between performance and computation costs. Nevertheless, errors caused by pruning strategies can lead to significant information loss. Our quantitative experiments reveal that the impact of pruned tokens on performance should be noticeable. To address this issue, we propose a novel joint Token Pruning & Squeezing module (TPS) for compressing vision transformers with higher efficiency. Firstly, TPS adopts pruning to get the reserved and pruned subsets. Secondly, TPS squeezes the information of pruned tokens into partial reserved tokens via the unidirectional nearest-neighbor matching and similarity-based fusing steps. Compared to state-of-the-art methods, our approach outperforms them under all token pruning intensities. Especially while shrinking DeiT-tiny&small computational budgets to 35%, it improves the accuracy by 1%-6% compared with baselines on ImageNet classification. The proposed method can accelerate the throughput of DeiT-small beyond DeiT-tiny, while its accuracy surpasses DeiT-tiny by 4.78%. Experiments on various transformers demonstrate the effectiveness of our method, while analysis experiments prove our higher robustness to the errors of the token pruning policy. Code is available at https://github.com/megvii-research/TPS-CVPR2023.Comment: Accepted to CVPR202