Molecular odor prediction involves using a molecule's structure to estimate its odor. While accurate prediction remains challenging, AI models can suggest potential odors. Existing methods, however, often rely on basic descriptors or handcrafted fingerprints, which lack expressive power and hinder effective learning. Furthermore, these methods suffer from severe class imbalance, limiting the training effectiveness of AI models. To address these challenges, we propose a Feature Contribution-driven Hierarchical Multi-Feature Mapping Network (HMFNet). Specifically, we introduce a fine-grained, Local Multi-Hierarchy Feature Extraction module (LMFE) that performs deep feature extraction at the atomic level, capturing detailed features crucial for odor prediction. To enhance the extraction of discriminative atomic features, we integrate a Harmonic Modulated Feature Mapping (HMFM). This module dynamically learns feature importance and frequency modulation, improving the model's capability to capture relevant patterns. Additionally, a Global Multi-Hierarchy Feature Extraction module (GMFE) is designed to learn global features from the molecular graph topology, enabling the model to fully leverage global information and enhance its discriminative power for odor prediction. To further mitigate the issue of class imbalance, we propose a Chemically-Informed Loss (CIL). Experimental results demonstrate that our approach significantly improves performance across various deep learning models, highlighting its potential to advance molecular structure representation and accelerate the development of AI-driven technologies.

Despite recent advances, scene text recognition remains a challenging problem due to the significant variability, irregularity and distortion in text appearance and localization. Attention-based methods have become the mainstream due to their superior vocabulary learning and observation ability. Nonetheless, they are susceptible to attention drift which can lead to word recognition errors. Most works focus on correcting attention drift in decoding but completely ignore the error accumulated during the encoding process. In this paper, we propose a novel scheme, called the Attention Guidance by Cross-Domain Supervision Signals for Scene Text Recognition (ACDS-STR), which can mitigate the attention drift at the feature encoding stage. At the heart of the proposed scheme is the cross-domain attention guidance and feature encoding fusion module (CAFM) that uses the core areas of characters to recursively guide attention to learn in the encoding process. With precise attention information sourced from CAFM, we propose a non-attention-based adaptive transformation decoder (ATD) to guarantee decoding performance and improve decoding speed. In the training stage, we fuse manual guidance and subjective learning to learn the core areas of characters, which notably augments the recognition performance of the model. Experiments are conducted on public benchmarks and show the state-of-the-art performance. The source will be available at https://github.com/xuefanfu/ACDS-STR.

Although there are lots of studies on scene text recognition, few of them focus on the recognition of the incomplete text. The recognition performance of existing text recognition algorithms on the incomplete text is far from the expected, and the recognition of the incomplete text is still challenging. In this paper, an end-to-end Two-Stage Inpainting Network for Incomplete Text (TSINIT) is proposed to reconstruct the incomplete text into the complete one even when the text is in various styles and with various backgrounds, and the reconstructed text can be recognized by the existing text recognition algorithms correctly. The proposed TSINIT is divided into text extraction module (TEM) and text reconstruction module (TRM) to make the inpainting only focus on the text. TEM separates the incomplete text from the background and character-like regions at the pixel level, which can reduce the ambiguity of text reconstruction caused by the background. TRM reconstructs the incomplete text towards the most possible text with the consideration of the abstract and semantic structures of the text. Furthermore, we build a synthetic incomplete text dataset (SITD), which contains contaminated and abraded text images. SITD is divided into 6 incomplete levels according to the number of pixels in the incomplete regions and the ratio of the incomplete characters to all characters. The experimental results show that the proposed method has better inpainting ability for the incomplete text compared with traditional image inpainting algorithms on the proposed SITD and real images. When using the same text recognition method, the recognition accuracy of the incomplete text on SITD can be improved much more with the help of the proposed TSINIT than with the traditional image inpainting methods.

In recent years, scene text recognition algorithms have achieved great progress, but they still face some challenges in practical environment, such as the incomplete scene text, which includes structurally broken characters and occluded characters, as shown in Fig. 1. Existing scene text recognition algorithms can not accurately recognize such incomplete scene text. In this paper, we design an end-to-end Inpainting Network for Incomplete Text (INIT), which can reconstruct each incomplete character into complete character. INIT can separate the text from the background and just reconstruct the text regions, which reduces the influence caused by the background. And INIT is supervised by reconstruction loss and semantic loss to reach the most likely text. Furthermore, to compensate for the absence of incomplete scene text dataset, we propose an incomplete text synthesis method and build an incomplete text dataset (SITD), which is more suitable for practical scenarios. On SITD, the recognition accuracy can achieve 82.99% by the existing text recognition method with the help of INIT, while the accuracy is only 61.99% by the same recognition method with the conventional image inpainting method. Experimental results show that the proposed method has better reconstruction ability for incomplete text compared with the existing image inpainting algorithms.