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SlotDiT: Object-Centric Representations for Diffusion Transformers

Paper recorded by Signals 4 on 2026-09-15 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-15 on arXiv · recorded by Signals 4 on 2026-09-16

Category: cs.CV · 计算机视觉 · first seen 2026-09-16

Abstract

Text-conditioned latent diffusion models perform strongly in video generation and are promising backbones for robotic applications. However, existing approaches rely on pixel-level or VAE-based latent representations that lack explicit semantic structure, leaving the impact of the representation space largely unexplored. Slot-based object-centric representations offer a structured alternative by d

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#32 most recent of 237 cs.CV papers we have recorded · ↑ newer: BrainFocus: EEG-Guided ROI Selection for Efficient Vision-Language Mod · ↓ older: SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Se
Cite this page: SlotDiT: Object-Centric Representations for Diffusion Transformers: the #32 most recent of 237 cs.CV papers we have recorded (as of 2026-09-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/slotdit-object-centric-representations-for-diffusion-transformers.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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