ViStoryBench: Comprehensive Benchmark Suite for Story Visualization

Story visualization, which aims to generate a sequence of visually coherent images aligning with a given narrative and reference images, has seen significant progress with recent advancements in generative models. To further enhance the performance of story visualization frameworks in real-world scenarios, we introduce a comprehensive evaluation benchmark, ViStoryBench. We collect a diverse dataset encompassing various story types and artistic styles, ensuring models are evaluated across multiple dimensions such as different plots (e.g., comedy, horror) and visual aesthetics (e.g., anime, 3D renderings). ViStoryBench is carefully curated to balance narrative structures and visual elements, featuring stories with single and multiple protagonists to test models' ability to maintain character consistency. Additionally, it includes complex plots and intricate world-building to challenge models in generating accurate visuals. To ensure comprehensive comparisons, our benchmark incorporates a wide range of evaluation metrics assessing critical aspects. This structured and multifaceted framework enables researchers to thoroughly identify both the strengths and weaknesses of different models, fostering targeted improvements.

Enhancing Code LLMs with Reinforcement Learning in Code Generation

With the rapid evolution of large language models (LLM), reinforcement learning (RL) has emerged as a pivotal technique for code generation and optimization in various domains. This paper presents a systematic survey of the application of RL in code optimization and generation, highlighting its role in enhancing compiler optimization, resource allocation, and the development of frameworks and tools. Subsequent sections first delve into the intricate processes of compiler optimization, where RL algorithms are leveraged to improve efficiency and resource utilization. The discussion then progresses to the function of RL in resource allocation, emphasizing register allocation and system optimization. We also explore the burgeoning role of frameworks and tools in code generation, examining how RL can be integrated to bolster their capabilities. This survey aims to serve as a comprehensive resource for researchers and practitioners interested in harnessing the power of RL to advance code generation and optimization techniques.

Hufu: A Modality-Agnositc Watermarking System for Pre-Trained Transformers via Permutation Equivariance

With the blossom of deep learning models and services, it has become an imperative concern to safeguard the valuable model parameters from being stolen. Watermarking is considered an important tool for ownership verification. However, current watermarking schemes are customized for different models and tasks, hard to be integrated as an integrated intellectual protection service. We propose Hufu, a modality-agnostic watermarking system for pre-trained Transformer-based models, relying on the permutation equivariance property of Transformers. Hufu embeds watermark by fine-tuning the pre-trained model on a set of data samples specifically permuted, and the embedded model essentially contains two sets of weights -- one for normal use and the other for watermark extraction which is triggered on permuted inputs. The permutation equivariance ensures minimal interference between these two sets of model weights and thus high fidelity on downstream tasks. Since our method only depends on the model itself, it is naturally modality-agnostic, task-independent, and trigger-sample-free. Extensive experiments on the state-of-the-art vision Transformers, BERT, and GPT2 have demonstrated Hufu's superiority in meeting watermarking requirements including effectiveness, efficiency, fidelity, and robustness, showing its great potential to be deployed as a uniform ownership verification service for various Transformers.