3 citations · 3 across the 3 of their papers we have counts for
7 papers
Thinking with DistilQwen: A Tale of Four Distilled Reasoning and Reward Model Series
Wenrui Cai, Chengyu Wang, Junbing Yan +2
Recently, the demand for small and efficient reasoning models to support real-world applications has driven the development of knowledge distillation techniques that balance reason…
Encapsulated Composition of Text-to-Image and Text-to-Video Models for High-Quality Video Synthesis
Tongtong Su, Chengyu Wang, Bingyan Liu +2
In recent years, large text-to-video (T2V) synthesis models have garnered considerable attention for their abilities to generate videos from textual descriptions. However, achievin…
Zero-to-Hero: Zero-Shot Initialization Empowering Reference-Based Video Appearance Editing
Tongtong Su, Chengyu Wang, Jun Huang +1
Appearance editing according to user needs is a pivotal task in video editing. Existing text-guided methods often lead to ambiguities regarding user intentions and restrict fine-gr…
Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations
Wenrui Cai, Chengyu Wang, Junbing Yan +2
The emergence of large reasoning models (LRMs) has transformed Natural Language Processing by excelling in complex tasks such as mathematical problem-solving and code generation. T…
Understanding Attention Mechanism in Video Diffusion Models
Bingyan Liu, Chengyu Wang, Tongtong Su +4
Text-to-video (T2V) synthesis models, such as OpenAI's Sora, have garnered significant attention due to their ability to generate high-quality videos from a text prompt. In diffusi…
Enhancing Reasoning Abilities of Small LLMs with Cognitive Alignment
Wenrui Cai, Chengyu Wang, Junbing Yan +2
The reasoning capabilities of large reasoning models (LRMs), such as OpenAI's o1 and DeepSeek-R1, have seen substantial advancements through deep thinking. However, these enhanceme…