7 papers · 1 filter
Atom: Efficient On-Device Video-Language Pipelines Through Modular Reuse
Kunjal Panchal, Saayan Mitra, Somdeb Sarkhel +4
Recent advances in video-language models have enabled powerful applications like video retrieval, captioning, and assembly. However, executing such multi-stage pipelines efficientl…
Mitigating Forgetting Between Supervised and Reinforcement Learning Yields Stronger Reasoners
Xiangchi Yuan, Xiang Chen, Tong Yu +4
Large Language Models (LLMs) show strong reasoning abilities, often amplified by Chain-of-Thought (CoT) prompting and reinforcement learning (RL). Although RL algorithms can substa…
SKALD: Learning-Based Shot Assembly for Coherent Multi-Shot Video Creation
Chen Yi Lu, Md Mehrab Tanjim, Ishita Dasgupta +4
We present SKALD, a multi-shot video assembly method that constructs coherent video sequences from candidate shots with minimal reliance on text. Central to our approach is the Lea…
A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations
Vijeta Deshpande, Ishita Dasgupta, Uttaran Bhattacharya +3
Synthetic text generated by Large Language Models (LLMs) is increasingly used for further training and improvement of LLMs. Diversity is crucial for the effectiveness of synthetic…
CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement
Leitian Tao, Xiang Chen, Tong Yu +4
Large Language Models (LLMs) have revolutionized code generation but require significant resources and often over-generalize, limiting their task-specific efficiency. Fine-tuning s…
Shape My Moves: Text-Driven Shape-Aware Synthesis of Human Motions
Ting-Hsuan Liao, Yi Zhou, Yu Shen +4
We explore how body shapes influence human motion synthesis, an aspect often overlooked in existing text-to-motion generation methods due to the ease of learning a homogenized, can…