Tokenizing Buildings: A Transformer for Layout Synthesis

A new method for movie trailer generation, named SSMP, has been proposed, which utilizes self-paced and self-corrective masked prediction to enhance the quality of trailers by employing bi-directional contextual modeling. This approach addresses the limitations of traditional selection-then-ranking methods that often lead to error propagation in trailer creation.

A new framework called COMET has been introduced for generating stable and controllable character motion in real-time, addressing challenges in computer animation related to fine-grained control and motion degradation over long sequences. This autoregressive model utilizes a Transformer-based conditional VAE to allow precise control over user-specified joints, enhancing tasks such as goal-reaching and in-betweening.

A new method called Sliding-Window Merging (SWM) has been proposed to enhance the efficiency of large language models (LLMs) by compacting patch-redundant layers. This technique identifies and merges consecutive layers based on their functional similarity, thereby maintaining performance while simplifying model architecture. Extensive experiments indicate that SWM outperforms traditional pruning methods in zero-shot inference performance.

Researchers have introduced FusedKV, a novel approach to reconstructing key-value (KV) caches in transformer models, enhancing their efficiency by fusing information from bottom and middle layers. This method addresses the significant memory demands of KV caches during long sequence processing, which has been a bottleneck in transformer performance. Preliminary findings indicate that this fusion retains essential positional information without the computational burden of rotary embeddings.

A new framework named MAGE-ID has been introduced to enhance Intrusion Detection Systems (IDS) by addressing challenges such as heterogeneous network traffic and data imbalance between benign and attack flows. This multimodal generative framework utilizes a diffusion-based approach to synthesize data from tabular flow features and their transformed images, improving detection performance significantly on datasets like CIC-IDS-2017 and NSL-KDD.

A novel Transformer model named AutoBrep has been introduced to generate boundary representations (B-Reps) in Computer-Aided Design (CAD) with high quality and valid topology. This model addresses the challenge of end-to-end generation of B-Reps by employing a unified tokenization scheme that encodes geometric and topological characteristics as discrete tokens, facilitating a breadth-first traversal of the B-Rep face adjacency graph during inference.

A new study has introduced AbscessHeNe, a dataset of 4,926 contrast-enhanced CT slices specifically focused on head and neck abscesses, which are critical for timely diagnosis and treatment. This dataset aims to enhance the development of semantic segmentation models that can accurately identify abscess boundaries and assess deep neck space involvement.

A new framework named MLLMsent has been proposed to enhance the sentiment reasoning capabilities of Multimodal Large Language Models (MLLMs). This framework explores sentiment classification directly from images, sentiment analysis on generated image descriptions, and fine-tuning LLMs on sentiment-labeled descriptions, achieving state-of-the-art results in recent benchmarks.