13 accepted papers
Space-time video super-resolution (STVSR) is a task aimed at simultaneously upsampling a video in both spatial and temporal dimensions. Previous studies on STVSR have primarily focused on task-specific architectures and modeling paradigms, while effective pretraining strategies remain underexplored.
As large language models (LLMs) continue to grow in size, their practical deployment increasingly relies on a range of compression techniques, such as quantization, pruning, and low-rank approximation. Especially, post-training compression methods–which do not require re-training–have drawn consider
Diffusion transformers demonstrate significant potential for various generation tasks but are challenged by high computational cost. Recently, feature caching methods have been introduced to improve inference efficiency by storing features at certain timesteps and reusing them at subsequent timestep…
Attention mechanisms are central to the success of large language models (LLMs), enabling them to capture intricate token dependencies and implicitly assign importance to each token. Recent studies have revealed the sink token, which receives disproportionately high attention despite their limited s…
Designing a fast and effective entropy model is challenging but essential for practical application of neural codecs. Beyond spatial autoregressive entropy models, more efficient backward adaptation-based entropy models have been recently developed. They not only reduce decoding time by using smalle…
"Implicit neural representations (INR) has found successful applications across diverse domains. To employ INR in real-life, it is important to speed up training. In the field of INR for video applications, the state-of-the-art approach [?] employs grid-type parametric encoding and successfully achi…
Recent advances in text-guided image compression have shown great potential to enhance the perceptual quality of reconstructed images. These methods, however, tend to have significantly degraded pixel-wise fidelity, limiting their practicality. To fill this gap, we develop a new text-guided image co…
The estimation of 3D lanes from monocular RGB images is a fundamentally ill-posed problem. Previous studies have assumed that all lanes are on a flat ground plane. However, we argue that the algorithms based on this assumption have difficulty in detecting various lanes in actual driving environments…
Compact representation of multimedia signals using implicit neural representations (INRs) has advanced significantly over the past few years, and recent works address their applications to video. Existing studies on video INR have focused on network architecture design as all video information is co…
Distributional shifts in photometry and texture have been extensively studied for unsupervised domain adaptation, but their counterparts in optical distortion have been largely neglected. In this work, we tackle the task of unsupervised domain adaptation for semantic image segmentation where unknown…
Auxiliary loss is additional loss besides the main branch loss to help optimize the learning process of neural networks. In order to calculate the auxiliary loss between the feature maps of intermediate layers and the ground truth in the field of semantic segmentation, the size of each feature map m…
A current remarkable improvement of unsupervised visual representation learning is based on heavy networks with large-batch training. While recent methods have greatly reduced the gap between supervised and unsupervised performance of deep models such as ResNet-50, this development has been relative…
We propose a fast and lightweight end-to-end convolutional network architecture for real-time segmentation of high resolution videos, NfS-SegNet, that can segement 2K-videos at 36.5 FPS with 24.3 GFLOPS. This speed and computation-efficiency is due to following reasons: 1) The encoder network, NfS-N…