We propose Strongly Supervised pre-training with ScreenShots (S4) - a novel pre-training paradigm for Vision-Language Models using data from large-scale web screenshot rendering. Using web screenshots unlocks a treasure trove of visual and textual cues that are not present in using image-text pairs. In S4, we leverage the inherent tree-structured hierarchy of HTML elements and the spatial localization to carefully design 10 pre-training tasks with large scale annotated data. These tasks resemble down- stream tasks across different domains and the annotations are cheap to obtain. We demonstrate that, compared to current screenshot pre-training objectives, our innovative pre-training method significantly enhances performance of image-to-text model in nine varied and popular downstream tasks - up to 76.1% improvements on Table Detection, and at least 1 % on Widget Captioning.

Understanding data visualizations like charts and plots requires reasoning about both visual elements and numer-ics. Although strong in extractive questions, current chart visual question answering (chart VQA) models suffer on complex reasoning questions. In this work, we address the lack of reasoning ability by data augmentation. We lever-age Large Language Models (LLMs), which have shown to have strong reasoning ability, as an automatic data anno-tator that generates question-answer annotations for chart images. The key innovation in our method lies in the Syn-thesize Step-by-Step strategy: our LLM-based data genera-tor learns to decompose the complex question into step-by-step sub-questions (rationales), which are then used to de-rive the final answer using external tools, i.e. Python. This step-wise generation procedure is trained on synthetic data generated using a template-based QA generation pipeline. Experimental results highlight the significance of the pro-posed step-by-step generation. By training with the LLM- augmented data (LAMENDA), we significantly enhance the chart VQA models, achieving the state-of-the-art accuracy on the ChartQA and PlotQA datasets. In particular, our approach improves the accuracy of the previous state-of-the-art approach from 38% to 54% on the human-written questions in the ChartQA dataset, which needs strong rea-soning. We hope our work underscores the potential of syn-thetic data and encourages further exploration of data augmentation using LLMs for reasoning-heavy tasks.

Several recent papers introduced techniques to adjust the attributes of human faces generated by unconditional GANs such as StyleGAN. Despite efforts to disentangle the attributes, a request to change one attribute often triggers unwanted changes to other attributes as well. More importantly, in some cases, a human observer would not recognize the edited face to belong to the same person. We propose an approach where a neural network takes as input the latent encoding of a face and the desired attribute changes and outputs the latent space encoding of the edited image. The network is trained offline using unsupervised data, with training labels generated by an off-the-shelf attribute classifier. The desired attribute changes and conservation laws, such as identity maintenance, are encoded in the training loss. The number of attributes the mapper can simultaneously modify is only limited by the attributes available to the classifier – we trained a network that handles 35 attributes, more than any previous approach. As no optimization is performed at deployment time, the computation time is negligible, allowing real-time attribute editing. Qualitative and quantitative comparisons with the current state-of-the-art show our method is better at conserving the identity of the face and restricting changes to the requested attributes.

Current face image retrieval solutions are limited, since they treat different facial attributes the same and cannot incorporate user’s preference for a subset of attributes in their search criteria. This paper introduces a new face image retrieval framework, where the input face query is augmented by both an adjustment vector that specifies the desired modifications to the facial attributes, and a preference vector that assigns different levels of importance to different attributes. For example, a user can ask for retrieving images similar to a query image, but with a different hair color, and no preference for absence/presence of eyeglasses in the results. To achieve this, we propose to disentangle the semantics, corresponding to various attributes, by learning a set of sparse and orthogonal basis vectors in the latent space of StyleGAN. Such basis vectors are then employed to decompose the dissimilarity between face images in terms of dissimilarity between their attributes, assign preference to the attributes, and adjust the attributes in the query. Enforcing sparsity on the basis vectors helps us to disentangle the latent space and adjust each attribute independently from other attributes, while enforcing orthogonality facilitates preference assignment and the dissimilarity decomposition. The effectiveness of our approach is illustrated by achieving state-of-the-art results for the face image retrieval task.

We propose a novel method for automatic object recoloring based on Generative Adversarial Networks (GANs). The user can simply give commands of the form recolortowhich will be executed without any need of manual edit. Our approach takes advantage of pre-trained object detectors and saliency mask segmentation networks. The segmented mask of the given object along with the target color and the original image form the input to the GAN. The use of cycle consistency loss ensures the realistic look of the results. To our best knowledge, this is the first algorithm where the automatic recoloring is only limited by the ability of the mask extractor to map a natural language tag to a specific object in the image (several hundred object types at the time of this writing). For a performance comparison, we also adapted other state of the art methods to perform this task. We found that our method had consistently yielded qualitatively better recoloring results.