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Scene text recognition has attracted increasing interest in recent years due to its wide range of applications in multilingual translation, autonomous driving, etc. In this report, we describe our solution to the Out of Vocabulary Scene Text Understanding (OOV-ST) Challenge, which aims to extract out-of-vocabulary (OOV) words from natural scene images. Our oCLIP-based model achieves 28.59\% in h-mean which ranks 1st in end-to-end OOV word recognition track of OOV Challenge in ECCV2022 TiE Workshop., Comment: Report to ECCV TiE OOV competition

This report presents our 2nd place solution to ECCV 2022 challenge on Out-of-Vocabulary Scene Text Understanding (OOV-ST) : Cropped Word Recognition. This challenge is held in the context of ECCV 2022 workshop on Text in Everything (TiE), which aims to extract out-of-vocabulary words from natural scene images. In the competition, we first pre-train SCATTER on the synthetic datasets, then fine-tune the model on the training set with data augmentations. Meanwhile, two additional models are trained specifically for long and vertical texts. Finally, we combine the output from different models with different layers, different backbones, and different seeds as the final results. Our solution achieves a word accuracy of 59.45\% when considering out-of-vocabulary words only.

In the dataset of image captioning, each image is aligned with several descriptions. Despite the fact that the quality of these descriptions varies, existing captioning models treat them equally in the training process. In this paper, we propose a new control signal of sentence quality, which is taken as an additional input to the captioning model. By integrating the control signal information, captioning models are aware of the quality level of the target sentences and handle them differently. Moreover, we propose a novel reinforcement training method specially designed for the control signal of sentence quality: Quality-oriented Self-Annotated Training (Q-SAT). Extensive experiments on MSCOCO dataset show that without extra information from ground truth captions, models controlled by the highest quality level outperform baseline models on accuracy-based evaluation metrics, which validates the effectiveness of our proposed methods., Comment: Accepted by ICASSP2023

The Controllable Image Captioning (CIC) task aims to generate captions conditioned on designated control signals. Several structure-related control signals are proposed to control the semantic structure of sentences, such as sentence length and Part-of-Speech tag sequences. However, due to the fact that the accuracy-based reward focuses mainly on contents rather than semantic structures, existing reinforcement training methods are not applicable to structure-related CIC models. The lack of reinforcement training leads to exposure bias and the inconsistency between the optimizing function and evaluation metrics. In this paper, we propose a novel reinforcement training method for structure-related control signals: Self-Annotated Training (SAT), to improve both the accuracy and controllability of CIC models. In SAT, a recursive annotation mechanism (RAM) is designed to force the input control signal to match the actual output sentence. Moreover, we propose an extra alignment reward to finetune the CIC model trained after SAT method, which further enhances the controllability of models. On the MSCOCO benchmark, we conduct extensive experiments on different structure-related control signals and on different baseline models, the results of which demonstrate the effectiveness and generalizability of our methods.

Despite the fact that image captioning models have been able to generate impressive descriptions for a given image, challenges remain: (1) the controllability and diversity of existing models are still far from satisfactory; (2) models sometimes may produce extremely poor-quality captions. In this paper, two novel methods are introduced to solve the problems respectively. Specifically, for the former problem, we introduce a control signal which can control the macroscopic sentence attributes, such as sentence quality, sentence length, sentence tense and number of nouns etc. With such a control signal, the controllability and diversity of existing captioning models are enhanced. For the latter problem, we innovatively propose a strategy that an image-text matching model is trained to measure the quality of sentences generated in both forward and backward directions and finally choose the better one. As a result, this strategy can effectively reduce the proportion of poorquality sentences. Our proposed methods can be easily applie on most image captioning models to improve their overall performance. Based on the Up-Down model, the experimental results show that our methods achieve BLEU- 4/CIDEr/SPICE scores of 37.5/120.3/21.5 on MSCOCO Karpathy test split with cross-entropy training, which surpass the results of other state-of-the-art methods trained by cross-entropy loss.