Object-Centric Learning (OCL) aims to decompose images into objects without human annotations. A major family of mainstream methods uses Slot Attention to aggregate image features into object-level representations and then from them reconstructs masked image content, i.e., Random Masking (RM), to provide self-supervisi...
You-Liang Tao, Yan-Hua Han, Bin Zhao et al.· 0 citations
The use of super-resolution as a post-processing step following a video codec allows videos to be encoded at reduced spatial resolution at the encoder side and to be reconstructed and upsampled to the original resolution at the decoder side. In this way, the required bitrate is reduced and the quality of the reconstruc...
Khoa Pham-Dinh, Francesco Cricri, María Santamaría et al.· 0 citations
Partial Optimal Transport (POT) extends the classical optimal transport problem by relaxing the strict mass conservation constraint, enabling its use in a wide range of real-world applications. In many of these settings, sparse transport plans are preferred for their interpretability and computational benefits. While s...
K. Nguyen, D. T. Nguyen, Thong Huynh et al.· 0 citations
Downstream reasoning depends on coarse structure and relative geometry rather than exact floating-point values, which argues for rate-task rather than rate-distortion optimization of AI traffic codecs.
Reza Heidari, H. R. Tavakoli, Juho Kannala· 1 citation
Across RealEstate10K, DL3DV, Tanks-and-Temples, and Mip-NeRF 360, SplatGuide achieves state-of-the-art pose-free novel view synthesis, and surpasses the ground-truth-pose baseline.
Ye-Jun Zhang, Zi-Han Wang, Xue-Si Ji et al.· 0 citations
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