Connecting Vision and Language plays an essential role in Generative Intelligence. For this reason, large research efforts have been devoted to image captioning, i.e. describing images with syntactically and semantically meaningful sentences. Starting from 2015 the task has generally been addressed with pipelines composed of a visual encoder and a language model for text generation. During these years, both components have evolved considerably through the exploitation of object regions, attributes, the introduction of multi-modal connections, fully-attentive approaches, and BERT-like early-fusion strategies. However, regardless of the impressive results, research in image captioning has not reached a conclusive answer yet. This work aims at providing a comprehensive overview of image captioning approaches, from visual encoding and text generation to training strategies, datasets, and evaluation metrics. In this respect, we quantitatively compare many relevant state-of-the-art approaches to identify the most impactful technical innovations in architectures and training strategies. Moreover, many variants of the problem and its open challenges are discussed. The final goal of this work is to serve as a tool for understanding the existing literature and highlighting the future directions for a research area where Computer Vision and Natural Language Processing can find an optimal synergy. 

/pdf/from-show-to-tell-a-survey-on-deep-learning-based-image-35obkkod.pdf

From Show to Tell: A Survey on Deep Learning-Based Image Captioning

Vision-language models (VLMs) such as CLIP have shown promising performance on a variety of recognition tasks using the standard zero-shot classification procedure -- computing similarity between the query image and the embedded words for each category. By only using the category name, they neglect to make use of the rich context of additional information that language affords. The procedure gives no intermediate understanding of why a category is chosen, and furthermore provides no mechanism for adjusting the criteria used towards this decision. We present an alternative framework for classification with VLMs, which we call classification by description. We ask VLMs to check for descriptive features rather than broad categories: to find a tiger, look for its stripes; its claws; and more. By basing decisions on these descriptors, we can provide additional cues that encourage using the features we want to be used. In the process, we can get a clear idea of what features the model uses to construct its decision; it gains some level of inherent explainability. We query large language models (e.g., GPT-3) for these descriptors to obtain them in a scalable way. Extensive experiments show our framework has numerous advantages past interpretability. We show improvements in accuracy on ImageNet across distribution shifts; demonstrate the ability to adapt VLMs to recognize concepts unseen during training; and illustrate how descriptors can be edited to effectively mitigate bias compared to the baseline.

https://export.arxiv.org/pdf/2210.07183v2.pdf

Visual Classification via Description from Large Language Models

Dual Global Enhanced Transformer for image captioning

Recently, much advance has been made in image captioning, and an encoder-decoder framework has been adopted by all the state-of-the-art models. Under this framework, an input image is encoded by a convolutional neural network (CNN) and then translated into natural language with a recurrent neural network (RNN). The existing models counting on this framework merely employ one kind of CNNs, e.g., ResNet or Inception-X, which describe image contents from only one specific view point. Thus, the semantic meaning of an input image cannot be comprehensively understood, which restricts the performance of captioning. In this paper, in order to exploit the complementary information from multiple encoders, we propose a novel Recurrent Fusion Network (RFNet) for tackling image captioning. The fusion process in our model can exploit the interactions among the outputs of the image encoders and then generate new compact yet informative representations for the decoder. Experiments on the MSCOCO dataset demonstrate the effectiveness of our proposed RFNet, which sets a new state-of-the-art for image captioning.

Recurrent Fusion Network for Image Captioning

Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretability. Drawing inspiration from the knowledge-driven nature of human driving, we explore the question of how to instill similar capabilities into autonomous driving systems and summarize a paradigm that integrates an interactive environment, a driver agent, as well as a memory component to address this question. Leveraging large language models with emergent abilities, we propose the DiLu framework, which combines a Reasoning and a Reflection module to enable the system to perform decision-making based on common-sense knowledge and evolve continuously. Extensive experiments prove DiLu's capability to accumulate experience and demonstrate a significant advantage in generalization ability over reinforcement learning-based methods. Moreover, DiLu is able to directly acquire experiences from real-world datasets which highlights its potential to be deployed on practical autonomous driving systems. To the best of our knowledge, we are the first to instill knowledge-driven capability into autonomous driving systems from the perspective of how humans drive.

pdf/dilu-a-knowledge-driven-approach-to-autonomous-driving-with-57g8xlizit.pdf

DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models

Natural language explanation (NLE) models aim at explaining the decision-making process of a black box system via generating natural language sentences which are human-friendly, high-level and fine-grained. Current NLE models11Throughout this paper, we refer to NLE models as Natural Language Explanation models aimed for vision and vision-language tasks. explain the decision-making process of a vision or vision-language model (a.k.a., task model), e.g., a VQA model, via a language model (a.k.a., explanation model), e.g., GPT. Other than the additional memory resources and inference time required by the task model, the task and explanation models are completely independent, which disassociates the explanation from the reasoning process made to predict the answer. We introduce NLX-GPT, a general, compact and faithful language model that can simultaneously predict an answer and explain it. We first conduct pre-training on large scale data of image-caption pairs for general understanding of images, and then formulate the answer as a text prediction task along with the explanation. Without region proposals nor a task model, our resulting overall framework attains better evaluation scores, contains much less parameters and is 15× faster than the current SoA model. We then address the problem of evaluating the explanations which can be in many times generic, data-biased and can come in several forms. We therefore design 2 new evaluation measures: (1) explain-predict and (2) retrieval-based attack, a selfevaluation framework that requires no labels. Code is at: https://github.com/fawazsammani/nlxgpt.

NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks

Attention-based neural encoder-decoder frameworks have been widely used for image captioning. Many of these frameworks deploy their full focus on generating the caption from scratch by relying solely on the image features or the object detection regional features. In this paper, we introduce a novel framework that learns to modify existing captions from a given framework by modeling the residual information, where at each timestep the model learns what to keep, remove or add to the existing caption allowing the model to fully focus on "what to modify" rather than on "what to predict". We evaluate our method on the COCO dataset, trained on top of several image captioning frameworks and show that our model successfully modifies captions yielding better ones with better evaluation scores.

Look and Modify: Modification Networks for Image Captioning

Look and modify: Modification networks for image captioning

The measurement of image blurriness is a very important issue in image quality assessment. A number of algorithms have been developed by other researchers and used to calculate image blurriness. In this paper a new method is proposed and developed based on the full reference concept and demonstrated to show excellent results in measuring the blurriness in any image with a reference. We then compared our proposed method with previously developed methods and, based on the experimental results, the proposed method was shown to offer significant improvement in term of accuracy.

A New Method for Full Reference Image Blur Measure

The problem of measuring the color difference in images have been an interesting topic for quite some time due to the contribution that colors give to the images' quality and characteristics. Many algorithms have been used in measuring the color difference between two images such as AE76 and AE94. However, measuring the color difference in a single image is a very important aspect for quite a number of applications such as indicating the quality of the dyes or production errors in monochromatic materials, matching paint colors to images, matching fabric colors, and many more. In this paper, we propose an accurate method to calculate the color difference in an image that represents a monochromatic object or substance with the same color level, or in other words, a single image. Luminance, Chrominance, and Hue “LCH” color space parameters are derived from Luminance and color opponent's channels green-red and blue-yellow “LAB” color space parameters, and difference of the single image is performed with the aid of the CIEDE2000 color difference formula. The proposed method has shown a significant improvement in terms of accuracy. The results of this method have proven that the proposed method is the most reliable and accurate among the existing methods.

An accurate method to calculate the color difference in a single image

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