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Ego-InBetween: Generating Object State Transitions in Ego-Centric Videos
Mengmeng Ge, Takashi Isobe, Xu Jia +7
Understanding physical transformation processes is crucial for both human cognition and artificial intelligence systems, particularly from an egocentric perspective, which serves a…
NutVLM: A Self-Adaptive Defense Framework against Full-Dimension Attacks for Vision Language Models in Autonomous Driving
Xiaoxu Peng, Dong Zhou, Jianwen Zhang +3
Vision Language Models (VLMs) have advanced perception in autonomous driving (AD), but they remain vulnerable to adversarial threats. These risks range from localized physical patc…
E-MMDiT: Revisiting Multimodal Diffusion Transformer Design for Fast Image Synthesis under Limited Resources
Tong Shen, Jingai Yu, Dong Zhou +2
Diffusion models have shown strong capabilities in generating high-quality images from text prompts. However, these models often require large-scale training data and significant c…
Light-Weight Cross-Modal Enhancement Method with Benchmark Construction for UAV-based Open-Vocabulary Object Detection
Zhenhai Weng, Xinjie Li, Can Wu +4
Open-Vocabulary Object Detection (OVD) faces severe performance degradation when applied to UAV imagery due to the domain gap from ground-level datasets. To address this challenge,…
Layout-Conditioned Autoregressive Text-to-Image Generation via Structured Masking
Zirui Zheng, Takashi Isobe, Tong Shen +12
Although autoregressive (AR) models have demonstrated remarkable success in image generation, extending these models to layout-conditioned generation remains challenging due to the…
AMD-Hummingbird: Towards an Efficient Text-to-Video Model
Takashi Isobe, He Cui, Dong Zhou +3
Text-to-Video (T2V) generation has attracted significant attention for its ability to synthesize realistic videos from textual descriptions. However, existing models struggle to ba…
Edit as You See: Image-guided Video Editing via Masked Motion Modeling
Zhi-Lin Huang, Yixuan Liu, Chujun Qin +4
Recent advancements in diffusion models have significantly facilitated text-guided video editing. However, there is a relative scarcity of research on image-guided video editing, a…
ReNeg: Learning Negative Embedding with Reward Guidance
Xiaomin Li, Yixuan Liu, Takashi Isobe +8
In text-to-image (T2I) generation applications, negative embeddings have proven to be a simple yet effective approach for enhancing generation quality. Typically, these negative em…
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
Bin Ren, Yawei Li, Nancy Mehta +129
This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this cha…