Recognizing and organizing text in correct reading order plays a crucial role in historical document analysis and preservation. While existing methods have shown promising performance, they often struggle with challenges such as diverse layouts, low image quality, style variations, and distortions. This is primarily due to the lack of consideration for these issues in the current benchmarks, which hinders the development and evaluation of historical document analysis and recognition (HDAR) methods in complex real-world scenarios. To address this gap, this paper introduces a complex multi-style Chinese historical document analysis benchmark, named M5HisDoc. The M5 indicates five properties of style, ie., Multiple layouts, Multiple document types, Multiple calligraphy styles, Multiple backgrounds, and Multiple challenges.

Figure 10: A visualization of the layouts and unsupervised depth maps produced by GANformer2's planning stage while synthesizing varied images, making the generative process more structured and interpretable. GANformer2 creates the layout sequentially, segment-by-segment, to capture the scene's compositionality, effectively allowing us to add or remove objects from the resulting images. Since GANformer2 creates each scene as a composition of interacting segments, it supports adding and removal of objects while respecting various dependencies with their surroundings: Amodal completion of occluded objects is denoted by pink, updates of shadows and especially reflections by cyan, and other object removals cases by yellow. Shape manipulation is denoted by green, while position changes by yellow. Color manipulation is denoted by pink, while updates of material by cyan.

We introduce the GANformer2 model, an iterative object-oriented transformer, explored for the task of generative modeling. The network incorporates strong and explicit structural priors, to reflect the compositional nature of visual scenes, and synthesizes images through a sequential process. It operates in two stages: a fast and lightweight planning phase, where we draft a high-level scene layout, followed by an attention-based execution phase, where the layout is being refined, evolving into a rich and detailed picture. Our model moves away from conventional black-box GAN architectures that feature a flat and monolithic latent space towards a transparent design that encourages efficiency, controllability and interpretability. We demonstrate GANformer2's strengths and qualities through a careful evaluation over a range of datasets, from multi-object CLEVR scenes to the challenging COCO images, showing it successfully achieves state-of-the-art performance in terms of visual quality, diversity and consistency. Further experiments demonstrate the model's disentanglement and provide a deeper insight into its generative process, as it proceeds step-by-step from a rough initial sketch, to a detailed layout that accounts for objects' depths and dependencies, and up to the final high-resolution depiction of vibrant and intricate real-world scenes.

Massive datasets and high-capacity models have driven many recent advancements in computer vision and natural language understanding. This work presents a platform to enable similar success stories in Embodied AI. We propose PROCTHOR, a framework for procedural generation of Embodied AI environments. PROCTHOR enables us to sample arbitrarily large datasets of diverse, interactive, customizable, and performant virtual environments to train and evaluate embodied agents across navigation, interaction, and manipulation tasks. We demonstrate the power and potential of PROCTHOR via a sample of 10,000 generated houses and a simple neural model. Models trained using only RGB images on PROCTHOR, with no explicit mapping and no human task supervision produce state-of-the-art results across 6 embodied AI benchmarks for navigation, rearrangement, and arm manipulation, including the presently running Habitat 2022, AI2-THOR Rearrangement 2022, and RoboTHOR challenges. We also demonstrate strong 0-shot results on these benchmarks, via pre-training on PROCTHOR with no fine-tuning on the downstream benchmark, often beating previous state-of-the-art systems that access the downstream training data.

As large language models have demonstrated impressive performance in many domains, recent works have adopted language models (LMs) as controllers of visual modules for vision-and-language tasks. While existing work focuses on equipping LMs with visual understanding, we propose two novel interpretable/explainable visual programming frameworks for text-to-image (T2I) generation and evaluation. First, we introduce VPGEN, an interpretable step-by-step T2I generation framework that decomposes T2I generation into three steps: object/count generation, layout generation, and image generation. We employ an LM to handle the first two steps (object/count generation and layout generation), by finetuning it on textlayout pairs. Our step-by-step T2I generation framework provides stronger spatial control than end-to-end models, the dominant approach for this task.

We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios. We make comprehensive contributions to all levels of the embodied AI stack - data, simulation, and benchmark tasks.

Although text-to-image (T2I) models exhibit remarkable generation capabilities,they frequently fail to accurately bind semantically related objects or attributesin the input prompts; a challenge termed semantic binding. Previous approacheseither involve intensive fine-tuning of the entire T2I model or require users orlarge language models to specify generation layouts, adding complexity. In thispaper, we define semantic binding as the task of associating a given object with itsattribute, termed attribute binding, or linking it to other related sub-objects, referredto as object binding. We introduce a novel method called Token Merging (ToMe),which enhances semantic binding by aggregating relevant tokens into a singlecomposite token. This ensures that the object, its attributes and sub-objects all sharethe same cross-attention map.