MA2.L1.4
Parallel Task-Prompts ICM: A Versatile Feature Codec for Machine Vision
Tianma Shen, Ying Liu, Santa clara university, United States of America
Session:
MA2.L1: Advances in Learned Image Compression Lecture
Track:
Special Sessions
Location:
Lecture Room 1
Presentation Time:
Mon, 28 Oct, 11:24 - 11:42 Gulf Standard Time (UTC +4)
Session Co-Chairs:
Li Li, University of Science and Technology of China and Fernando Pereira, IST-IT and Li Li, University of Science and Tech. of China
Session MA2.L1
MA2.L1.1: ON EFFICIENT NEURAL NETWORK ARCHITECTURES FOR IMAGE COMPRESSION
Yichi Zhang, Zhihao Duan, Fengqing Zhu, Purdue University, United States of America
MA2.L1.2: OPTIMIZED DECOUPLED STRUCTURE WITH NON-LOCAL ATTENTION FOR DEEP IMAGE COMPRESSION
Xuanye Zhang, University of Science and Technology of China, China; Zhaobin Zhang, Yaojun Wu, Semih Esenlik, Bytedance Inc., United States of America; Xiaoyan Sun, University of Science and Technology of China, China; Kai Zhang, Li Zhang, Bytedance Inc., United States of America
MA2.L1.3: OPTIMIZING LEARNED IMAGE COMPRESSION ON SCALAR AND ENTROPY-CONSTRAINT QUANTIZATION
Florian Borzechowski, Michael Schäfer, Heiko Schwarz, Jonathan Pfaff, Detlev Marpe, Thomas Wiegand, Fraunhofer HHI, Germany
MA2.L1.4: Parallel Task-Prompts ICM: A Versatile Feature Codec for Machine Vision
Tianma Shen, Ying Liu, Santa clara university, United States of America
MA2.L1.5: Image Coding for Machines with Edge Information Learning Using Segment Anything
Takahiro Shindo, Kein Yamada, Taiju Watanabe, Hiroshi Watanabe, Waseda University, Japan
Contacts