Noisy-Aware Unsupervised Domain Adaptation for Scene Text Recognition.

Unsupervised Domain Adaptation (UDA) has shown promise in Scene Text Recognition (STR) by facilitating knowledge transfer from labeled synthetic text (source) to more challenging unlabeled real scene text (target). However, existing UDA-based STR methods fully rely on the pseudo-labels of target samples, which ignores the impact of domain gaps (inter-domain noise) and various natural environments (intra-domain noise), resulting in poor pseudo-label quality. In this paper, we propose a novel noisy-aware unsupervised domain adaptation framework tailored for STR, which aims to enhance model robustness against both inter- and intra-domain noise, thereby providing more precise pseudo-labels for target samples.

Angular Isotonic Loss Guided Multi-Layer Integration for Few-Shot Fine-Grained Image Classification.

Recent research on few-shot fine-grained image classification (FSFG) has predominantly focused on extracting discriminative features. The limited attention paid to the role of loss functions has resulted in weaker preservation of similarity relationships between query and support instances, thereby potentially limiting the performance of FSFG. In this regard, we analyze the limitations of widely adopted cross-entropy loss and introduce a novel Angular ISotonic (AIS) loss.

Fine-Grained Hashing With Double Filtering.

Fine-grained hashing is a new topic in the field of hashing-based retrieval and has not been well explored up to now. In this paper, we raise three key issues that fine-grained hashing should address simultaneously, i.e.

Fast Cross-Modal Hashing With Global and Local Similarity Embedding.

Recently, supervised cross-modal hashing has attracted much attention and achieved promising performance. To learn hash functions and binary codes, most methods globally exploit the supervised information, for example, preserving an at-least-one pairwise similarity into hash codes or reconstructing the label matrix with binary codes. However, due to the hardness of the discrete optimization problem, they are usually time consuming on large-scale datasets.

Neural Compatibility Modeling with Probabilistic Knowledge Distillation.

In modern society, clothing matching plays a pivotal role in people's daily life, as suitable outfits can beautify their appearance directly. Nevertheless, how to make a suitable outfit has become a daily headache for many people, especially those who do not have much sense of aesthetics. In the light of this, many research efforts have been dedicated to the task of complementary clothing matching and have achieved great success relying on the advanced data-driven neural networks.

Discrete Hashing with Multiple Supervision.

Supervised hashing methods have achieved more promising results than unsupervised ones by leveraging label information to generate compact and accurate hash codes. Most of the prior supervised hashing methods construct an n × n instance-pairwise similarity matrix, where n is the number of training samples. Nevertheless, this kind of similarity matrix results in high memory space cost and makes the optimization time-consuming, which make it unacceptable in many real applications.

Semi-supervised clustering algorithm for haplotype assembly problem based on MEC model.

Haplotype assembly is to infer a pair of haplotypes from localized polymorphism data. In this paper, a semi-supervised clustering algorithm-SSK (semi-supervised K-means) is proposed for it, which, to our knowledge, is the first semi-supervised clustering method for it. In SSK, some positive information is firstly extracted.