Research Field:
Artificial Intelligence
Project Introduction：
### **Technical Value**
**Complementarity of Multimodal Generation and Understanding**
By integrating generation tasks (e.g., image/text generation) with understanding tasks (e.g., semantic reasoning), multimodal models can produce higher-quality images and text with richer semantic context and visual details, meeting diverse application demands.
**Enhanced Representation Learning**
Generation tasks provide low-level visual details (e.g., textures, object shapes), improving the quality of representation learning. In turn, understanding tasks supply high-level semantic information (e.g., object relationships, scene context), enabling models to generate more meaningful and contextually relevant content.
**Innovative Research Methodology**
Exploring the interaction mechanisms between generation and understanding tasks offers new theoretical perspectives for multimodal model research, bridging the gap between perception and cognition.
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### **Business Value**
**Improved User Experience**
Unified multimodal models enhance performance in complex scenarios like *Tantantan* (social discovery) and *Zhixiaobao* (AI assistant), driving user retention and satisfaction.
• Example: Smarter context-aware responses in conversational AI.
**Expanded Application Scenarios**
A unified model enables scalable deployment across complex business domains, such as:
• Cross-modal content creation (e.g., visual storytelling).
• Enhanced decision-making systems (e.g., medical imaging + clinical text analysis).
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### **Key Technical Concepts Explained**
1. **Self-Supervised Learning Advances**
• Models like **SD-DiT** (Self-Distillation with Dual-Image Transformers) and *Return of Unconditional Generation* demonstrate the synergy between generation and understanding.
• **Representation Alignment for Generation**: Aligning latent representations of generation and understanding tasks improves cross-modal alignment.
2. **Unified Model Architecture**
A single model handling both generation (e.g., text-to-image synthesis) and understanding (e.g., VQA, visual reasoning) reduces computational redundancy and enhances task interoperability.
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### **Why This Matters**
Current multimodal models often silo generation and understanding tasks, leading to:
• **Generation Phase**: Rich visual details but shallow semantics (e.g., nonsensical image captions).
• **Understanding Phase**: High-level reasoning but missing low-level specifics (e.g., misinterpreting object orientations).
By unifying these tasks, models achieve **joint optimization**, balancing perceptual fidelity with semantic depth—critical for real-world applications like autonomous systems and human-computer interaction.
For example: —Currently pursuing a bachelor, master or PhD degree in Computer Science or related STEM field
-Experience with one or more general purpose programming languages, including but not limited to: Java, C/C++, Python, JavaScript, or Go
-Relevant experience, including industry experience or as a researcher in a lab, in the research areas listed above
Preferred Qualifications:
-A strong passion for technical research, demonstrated ability to generate new ideas and innovations; Excellent in self-learning, problem analyzing and solving
-Demonstrated publication record, with one or more publications at international conferences
-At least 3 months of full-time work is preferred
