Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets for training, and therefore challenging the deployment in practical applications. This study addresses this challenge by breaking down the text-based video editing process into two separate stages. In the first stage, we leverage an existing text-to-image diffusion model to simultaneously edit a few keyframes without additional fine-tuning. In the second stage, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the keyframes, benefiting from structural guidance provided by intermediate frames. Our comprehensive set of experiments illustrates the efficacy and efficiency of MaskINT when compared to other diffusion-based methodologies. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.
Linked Data is used in various fields as a new way of structuring and connecting data. Cultural heritage institutions have been using linked data to improve archival descriptions and facilitate the discovery of information. Most archival records have digital representations of physical artifacts in the form of scanned images that are non-machine-readable. Optical Character Recognition (OCR) recognizes text in images and translates it into machine-encoded text. This paper evaluates the impact of image processing methods and parameter tuning in OCR applied to typewritten cultural heritage documents. The approach uses a multi-objective problem formulation to minimize Levenshtein edit distance and maximize the number of words correctly identified with a non-dominated sorting genetic algorithm (NSGA-II) to tune the methods' parameters. Evaluation results show that parameterization by digital representation typology benefits the performance of image pre-processing algorithms in OCR. Furthermore, our findings suggest that employing image pre-processing algorithms in OCR might be more suitable for typologies where the text recognition task without pre-processing does not produce good results. In particular, Adaptive Thresholding, Bilateral Filter, and Opening are the best-performing algorithms for the theatre plays' covers, letters, and overall dataset, respectively, and should be applied before OCR to improve its performance.
Current methods focusing on medical image segmentation suffer from incorrect annotations, which is known as the noisy label issue. Most medical image segmentation with noisy labels methods utilize either noise transition matrix, noise-robust loss functions or pseudo-labeling methods, while none of the current research focuses on clean label disentanglement. We argue that the main reason is that the severe class-imbalanced issue will lead to the inaccuracy of the selected ``clean'' labels, thus influencing the robustness of the model against the noises. In this work, we come up with a simple but efficient class-balanced sampling strategy to tackle the class-imbalanced problem, which enables our newly proposed clean label disentangling framework to successfully select clean labels from the given label sets and encourages the model to learn from the correct annotations. However, such a method will filter out too many annotations which may also contain useful information. Therefore, we further extend our clean label disentangling framework to a new noisy feature-aided clean label disentangling framework, which takes the full annotations into utilization to learn more semantics. Extensive experiments have validated the effectiveness of our methods, where our methods achieve new state-of-the-art performance. Our code is available at https://github.com/xiaoyao3302/2BDenoise.
This paper presents a novel multi modal deep learning framework for enhanced agricultural pest detection, combining tiny-BERT's natural language processing with R-CNN and ResNet-18's image processing. Addressing limitations of traditional CNN-based visual methods, this approach integrates textual context for more accurate pest identification. The R-CNN and ResNet-18 integration tackles deep CNN issues like vanishing gradients, while tiny-BERT ensures computational efficiency. Employing ensemble learning with linear regression and random forest models, the framework demonstrates superior discriminate ability, as shown in ROC and AUC analyses. This multi modal approach, blending text and image data, significantly boosts pest detection in agriculture. The study highlights the potential of multi modal deep learning in complex real-world scenarios, suggesting future expansions in diversity of datasets, advanced data augmentation, and cross-modal attention mechanisms to enhance model performance.
Diffusion-based models have gained significant popularity for text-to-image generation due to their exceptional image-generation capabilities. A risk with these models is the potential generation of inappropriate content, such as biased or harmful images. However, the underlying reasons for generating such undesired content from the perspective of the diffusion model's internal representation remain unclear. Previous work interprets vectors in an interpretable latent space of diffusion models as semantic concepts. However, existing approaches cannot discover directions for arbitrary concepts, such as those related to inappropriate concepts. In this work, we propose a novel self-supervised approach to find interpretable latent directions for a given concept. With the discovered vectors, we further propose a simple approach to mitigate inappropriate generation. Extensive experiments have been conducted to verify the effectiveness of our mitigation approach, namely, for fair generation, safe generation, and responsible text-enhancing generation.
We study image segmentation using spatiotemporal dynamics in a recurrent neural network where the state of each unit is given by a complex number. We show that this network generates sophisticated spatiotemporal dynamics that can effectively divide an image into groups according to a scene's structural characteristics. Using an exact solution of the recurrent network's dynamics, we present a precise description of the mechanism underlying object segmentation in this network, providing a clear mathematical interpretation of how the network performs this task. We then demonstrate a simple algorithm for object segmentation that generalizes across inputs ranging from simple geometric objects in grayscale images to natural images. Object segmentation across all images is accomplished with one recurrent neural network that has a single, fixed set of weights. This demonstrates the expressive potential of recurrent neural networks when constructed using a mathematical approach that brings together their structure, dynamics, and computation.
Perceiving 3D structures from RGB images based on CAD model primitives can enable an effective, efficient 3D object-based representation of scenes. However, current approaches rely on supervision from expensive annotations of CAD models associated with real images, and encounter challenges due to the inherent ambiguities in the task -- both in depth-scale ambiguity in monocular perception, as well as inexact matches of CAD database models to real observations. We thus propose DiffCAD, the first weakly-supervised probabilistic approach to CAD retrieval and alignment from an RGB image. We formulate this as a conditional generative task, leveraging diffusion to learn implicit probabilistic models capturing the shape, pose, and scale of CAD objects in an image. This enables multi-hypothesis generation of different plausible CAD reconstructions, requiring only a few hypotheses to characterize ambiguities in depth/scale and inexact shape matches. Our approach is trained only on synthetic data, leveraging monocular depth and mask estimates to enable robust zero-shot adaptation to various real target domains. Despite being trained solely on synthetic data, our multi-hypothesis approach can even surpass the supervised state-of-the-art on the Scan2CAD dataset by 5.9% with 8 hypotheses.
Recent generative AI platforms are able to create texts or impressive images from simple text prompts. This makes them powerful tools for summarizing knowledge about architectural history or deriving new creative work in early design tasks like ideation, sketching and modelling. But, how good is the understanding of the generative AI models of the history of architecture? Has it learned to properly distinguish styles, or is it hallucinating information? In this chapter, we investigate this question for generative AI platforms for text and image generation for different architectural styles, to understand the capabilities and boundaries of knowledge of those tools. We also analyze how they are already being used by analyzing a data set of 101 million Midjourney queries to see if and how practitioners are already querying for specific architectural concepts.
The joint source coding and modulation (JSCM) framework was enabled by recent developments in deep learning, which allows to automatically learn from data, and in an end-to-end fashion, the best compression codes and modulation schemes. In this paper, we show the existence of a strict tradeoff between channel rate, distortion, perception, and classification accuracy in a JSCM scenario. We then propose two image compression methods to navigate that tradeoff: an inverse-domain generative adversarial network (ID-GAN), which achieves extreme compression, and a simpler, heuristic method that reveals insights about the performance of ID-GAN. Experiment results not only corroborate the theoretical findings, but also demonstrate that the proposed ID-GAN algorithm significantly improves system performance compared to traditional separation-based methods and recent deep JSCM architectures.
Despite significant advancements in medical vision-language pre-training, existing methods have largely overlooked the inherent entity-specific context within radiology reports and the complex cross-modality contextual relationships between text and images. To close this gap, we propose a novel Entity-centered Context-aware Medical Vision-language Pre-training (ECAMP) framework, which is designed to enable a more entity-centered and context-sensitive interpretation of medical data. Utilizing the recent powerful large language model, we distill entity-centered context from medical reports, which enables ECAMP to gain more effective supervision from the text modality. By further pre-training our model with carefully designed entity-aware, context-enhanced masked language modeling and context-guided super-resolution tasks, ECAMP significantly refines the interplay between text and image modalities, leading to an enhanced ability to extract entity-centered contextual features. Besides, our proposed multi-scale context fusion design also improves the semantic integration of both coarse and fine-level image representations, prompting better performance for multi-scale downstream applications. Combining these components leads to significant performance leaps over current state-of-the-art methods and establishes a new standard for cross-modality learning in medical imaging, whose effectiveness is demonstrated by our extensive experiments on various tasks including classification, segmentation, and detection across several public datasets. Code and models are available at https://github.com/ToniChopp/ECAMP.