5 accepted papers
Diffusion models deliver state-of-the-art image quality but are expensive to deploy. Post-training quantization (PTQ) can shrink models and speed up inference, yet residual quantization errors distort the diffusion distribution (the timestep-wise marginal over $\vx_t$), degrading sample quality. We …
Dense semantic segmentation in dynamic environments is fundamentally limited by the low-frame-rate (LFR) nature of standard cameras, which creates critical perceptual gaps between frames. To solve this, we introduce *Anytime Interframe Semantic Segmentation*: a new task for predicting segmentation a…
Current text-to-image (T2I) generation models struggle to align spatial composition with the input text, especially in complex scenes. Even layout-based approaches yield suboptimal spatial control, as their generation process is decoupled from layout planning, making it difficult to refine the layo…
Robust 3D geometry estimation from videos is critical for applications such as autonomous navigation, SLAM, and 3D scene reconstruction. Recent methods like DUSt3R demonstrate that regressing dense pointmaps from image pairs enables accurate and efficient pose-free reconstruction. However, existing…
The combination of Spiking Neural Networks (SNNs) and Transformers has attracted significant attention due to their potential for high energy efficiency and high-performance nature. However, existing works on this topic typically rely on direct training, which can lead to suboptimal performance. To…