6 papers
RAISE: Requirement-Adaptive Evolutionary Refinement for Training-Free Text-to-Image Alignment
Liyao Jiang, Ruichen Chen, Chao Gao +1
Recent text-to-image (T2I) diffusion models achieve remarkable realism, yet faithful prompt-image alignment remains challenging, particularly for complex prompts with multiple obje…
Can LLMs Predict Their Own Failures? Self-Awareness via Internal Circuits
Amirhosein Ghasemabadi, Di Niu
Large language models (LLMs) generate fluent and complex outputs but often fail to recognize their own mistakes and hallucinations. Existing approaches typically rely on external j…
iCLP: Large Language Model Reasoning with Implicit Cognition Latent Planning
Sijia Chen, Di Niu
Large language models (LLMs), when guided by explicit textual plans, can perform reliable step-by-step reasoning during problem-solving. However, generating accurate and effective…
Grounding Degradations in Natural Language for All-In-One Video Restoration
Muhammad Kamran Janjua, Amirhosein Ghasemabadi, Kunlin Zhang +3
In this work, we propose an all-in-one video restoration framework that grounds degradation-aware semantic context of video frames in natural language via foundation models, offeri…
Re-ttention: Ultra Sparse Visual Generation via Attention Statistical Reshape
Ruichen Chen, Keith G. Mills, Liyao Jiang +2
Diffusion Transformers (DiT) have become the de-facto model for generating high-quality visual content like videos and images. A huge bottleneck is the attention mechanism where co…
MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices
Mohammadali Shakerdargah, Shan Lu, Chao Gao +1
The advent of foundation models have revolutionized various fields, enabling unprecedented task accuracy and flexibility in computational linguistics, computer vision and other dom…