5 papers · 1 filter
Replacement Learning: Training Neural Networks with Fewer Parameters
Yuming Zhang, Peizhe Wang, Tianyang Han +5
End-to-end training with full-depth backpropagation remains the dominant paradigm for optimizing deep neural networks, but its efficiency deteriorates as models grow deeper. Since…
Beyond Words and Pixels: A Benchmark for Implicit World Knowledge Reasoning in Generative Models
Tianyang Han, Junhao Su, Junjie Hu +4
Text-to-image (T2I) models today are capable of producing photorealistic, instruction-following images, yet they still frequently fail on prompts that require implicit world knowle…
Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions
Junhao Su, Yuanliang Wan, Junwei Yang +4
Tool-augmented large language models (LLMs) are usually trained with supervised imitation or coarse-grained reinforcement learning that optimizes single tool calls. Current self-re…
MAN++: Scaling Momentum Auxiliary Network for Supervised Local Learning in Vision Tasks
Junhao Su, Feiyu Zhu, Hengyu Shi +5
Deep learning typically relies on end-to-end backpropagation for training, a method that inherently suffers from issues such as update locking during parameter optimization, high G…
PositionIC: Unified Position and Identity Consistency for Image Customization
Junjie Hu, Tianyang Han, Kai Ma +6
Recent subject-driven image customization excels in fidelity, yet fine-grained instance-level spatial control remains an elusive challenge, hindering real-world applications. This…