5 papers
Memory Internalization via Test-Time Training for Long-Video Understanding
Haonan Zhao, Jiaxuan Chen, Qingyang Liu +4
Long-video understanding often requires answering many questions about a single video whose evidence is distributed across extended temporal contexts. Conventional video-language m…
Why Struggle with Continuous Latents? Interpretable Discrete Latent Reasoning via Rendered Compression
Shuochen Chang, Qingyang Liu, Shaobo Wang +8
Large language models achieve high reasoning performance via explicit chain-of-thought and reinforcement learning, but require long output sequences and extended inference time. La…
RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via Data Alignment and Test-Time Scaling
Bingjie Gao, Qianli Ma, Xiaoxue Wu +9
Prompt design plays a crucial role in text-to-video (T2V) generation, yet user-provided prompts are often short, unstructured, and misaligned with training data, limiting the gener…
The Devil is in the Prompts: Retrieval-Augmented Prompt Optimization for Text-to-Video Generation
Bingjie Gao, Xinyu Gao, Xiaoxue Wu +5
The evolution of Text-to-video (T2V) generative models, trained on large-scale datasets, has been marked by significant progress. However, the sensitivity of T2V generative models…
Object Placement for Anything
Bingjie Gao, Bo Zhang, Li Niu
Object placement aims to determine the appropriate placement (\emph{e.g.}, location and size) of a foreground object when placing it on the background image. Most previous works ar…