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Hierarchical Image Tokenization for Multi-Scale Image Super Resolution
Isma Hadji, Enrique Sanchez, Adrian Bulat +2
We introduce a multi-scale Image Super Resolution (ISR) method building on recent advances in Visual Auto-Regressive (VAR) modeling. VAR models break image tokenization into additi…
Restore, Assess, Repeat: A Unified Framework for Iterative Image Restoration
I-Hsiang Chen, Isma Hadji, Enrique Sanchez +5
Image restoration aims to recover high quality images from inputs degraded by various factors, such as adverse weather, blur, or low light. While recent studies have shown remarkab…
No Hard Negatives Required: Concept Centric Learning Leads to Compositionality without Degrading Zero-shot Capabilities of Contrastive Models
Hai X. Pham, David T. Hoffmann, Ricardo Guerrero +1
Contrastive vision-language (V&L) models remain a popular choice for various applications. However, several limitations have emerged, most notably the limited ability of V&L models…
More Images, More Problems? A Controlled Analysis of VLM Failure Modes
Anurag Das, Adrian Bulat, Alberto Baldrati +4
Large Vision Language Models (LVLMs) have demonstrated remarkable capabilities, yet their proficiency in understanding and reasoning over multiple images remains largely unexplored…
Multi-scale Image Super Resolution with a Single Auto-Regressive Model
Enrique Sanchez, Isma Hadji, Adrian Bulat +3
In this paper we tackle Image Super Resolution (ISR), using recent advances in Visual Auto-Regressive (VAR) modeling. VAR iteratively estimates the residual in latent space between…
Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning
Mehdi Noroozi, Isma Hadji, Victor Escorcia +3
There has been immense progress recently in the visual quality of Stable Diffusion-based Super Resolution (SD-SR). However, deploying large diffusion models on computationally rest…