Recent advances in deep neural networks (DNNs) have mainly focused on innovations in
network architecture and loss function. In this paper, we introduce a flexible high-order …

Metric-based methods achieve promising performance on few-shot classification by learning
clusters on support samples and generating shared decision boundaries for query samples …

This paper proposes to handle the practical problem of learning a universal model for crowd
counting across scenes and datasets. We dissect that the crux of this problem is the …

Deep models have shown to be vulnerable to catastrophic forgetting, a phenomenon that
the recognition performance on old data degrades when a pre-trained model is fine-tuned …

The self-attention mechanism is crucial for enhancing various backbone neural networks'
performance. However, current methods add self-attention modules (SAMs) to each network …

An accurate early-stage leukemia diagnosis plays a critical role in treating and saving
patients' lives. The two primary forms of leukemia are acute and chronic leukemia, which is …

Sketch is a well-researched topic in the vision community by now. Sketch semantic
segmentation in particular, serves as a fundamental step towards finer-level sketch …

Multiple instance learning (MIL) is a weakly supervised learning paradigm that is becoming
increasingly popular because it requires less labeling effort than fully supervised methods …

Feature extraction is a key step for deep-learning-based point cloud registration. In the
correspondence-free point cloud registration task, the previous work commonly aggregates …

Object counting aims to estimate the number of objects in images. The leading counting
approaches focus on single-category counting tasks and achieve impressive performance …