Zhongping Zhang

Zhongping Zhang, Huiwen He, Bryan A. Plummer et al.

Conditional diffusion models have demonstrated impressive performance on various tasks like text-guided semantic image editing. Prior work requires image regions to be identified manually by human users or use an object detector that only perform well for object-centric manipulations. For example, if an input image contains multiple objects with the same semantic meaning (such as a group of birds), object detectors may struggle to recognize and localize the target object, let alone accurately manipulate it. To address these challenges, we propose a two-stage method for achieving complex scene image editing by Scene Graph Comprehension (SGC-Net). In the first stage, we train a Region of Interest (RoI) prediction network that uses scene graphs and predict the locations of the target objects. Unlike object detection methods based solely on object category, our method can accurately recognize the target object by comprehending the objects and their semantic relationships within a complex scene. The second stage uses a conditional diffusion model to edit the image based on our RoI predictions. We evaluate the effectiveness of our approach on the CLEVR and Visual Genome datasets. We report an 8 point improvement in SSIM on CLEVR and our edited images were preferred by human users by 9-33% over prior work on Visual Genome, validating the effectiveness of our proposed method. Code is available at github.com/Zhongping-Zhang/SGC_Net.

Zhongping Zhang, Yiwen Gu, Bryan A. Plummer

Article comprehension is an important challenge in natural language processing with many applications such as article generation or image-to-article retrieval. Prior work typically encodes all tokens in articles uniformly using pretrained language models. However, in many applications, such as understanding news stories, these articles are based on real-world events and may reference many named entities that are difficult to accurately recognize and predict by language models. To address this challenge, we propose an ENtity-aware article GeneratIoN and rEtrieval (ENGINE) framework, to explicitly incorporate named entities into language models. ENGINE has two main components: a named-entity extraction module to extract named entities from both metadata and embedded images associated with articles, and an entity-aware mechanism that enhances the model's ability to recognize and predict entity names. We conducted experiments on three public datasets: GoodNews, VisualNews, and WikiText, where our results demonstrate that our model can boost both article generation and article retrieval performance, with a 4-5 perplexity improvement in article generation and a 3-4% boost in recall@1 in article retrieval. We release our implementation at https://github.com/Zhongping-Zhang/ENGINE .