44 citations · 164 across the 35 of their papers we have counts for
6 papers · 1 filter
ABCAS: Adaptive Bound Control of spectral norm as Automatic Stabilizer
Shota Hirose, Shiori Maki, Naoki Wada +2
Spectral Normalization is one of the best methods for stabilizing the training of Generative Adversarial Network. Spectral Normalization limits the gradient of discriminator betwee…
Semantic Segmentation in Learned Compressed Domain
Jinming Liu, Heming Sun, Jiro Katto
Most machine vision tasks (e.g., semantic segmentation) are based on images encoded and decoded by image compression algorithms (e.g., JPEG). However, these decoded images in the p…
Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules
Ran Wang, Jinming Liu, Heming Sun +1
Lossless image compression is an essential research field in image compression. Recently, learning-based image compression methods achieved impressive performance compared with tra…
Streaming-capable High-performance Architecture of Learned Image Compression Codecs
Fangzheng Lin, Heming Sun, Jiro Katto
Learned image compression allows achieving state-of-the-art accuracy and compression ratios, but their relatively slow runtime performance limits their usage. While previous attemp…
Memory-Efficient Learned Image Compression with Pruned Hyperprior Module
Ao Luo, Heming Sun, Jinming Liu +1
Learned Image Compression (LIC) gradually became more and more famous in these years. The hyperprior-module-based LIC models have achieved remarkable rate-distortion performance. H…
Q-LIC: Quantizing Learned Image Compression with Channel Splitting
Heming Sun, Lu Yu, Jiro Katto
Learned image compression (LIC) has reached a comparable coding gain with traditional hand-crafted methods such as VVC intra. However, the large network complexity prohibits the us…