19 citations · 97 across the 49 of their papers we have counts for
17 papers · 1 filter
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…
VladVA: Discriminative Fine-tuning of LVLMs
Yassine Ouali, Adrian Bulat, Alexandros Xenos +4
Contrastively-trained Vision-Language Models (VLMs) like CLIP have become the de facto approach for discriminative vision-language representation learning. However, these models ha…
FAM Diffusion: Frequency and Attention Modulation for High-Resolution Image Generation with Stable Diffusion
Haosen Yang, Adrian Bulat, Isma Hadji +4
Diffusion models are proficient at generating high-quality images. They are however effective only when operating at the resolution used during training. Inference at a scaled reso…
CemiFace: Center-based Semi-hard Synthetic Face Generation for Face Recognition
Zhonglin Sun, Siyang Song, Ioannis Patras +1
Privacy issue is a main concern in developing face recognition techniques. Although synthetic face images can partially mitigate potential legal risks while maintaining effective f…
MM2Latent: Text-to-facial image generation and editing in GANs with multimodal assistance
Debin Meng, Christos Tzelepis, Ioannis Patras +1
Generating human portraits is a hot topic in the image generation area, e.g. mask-to-face generation and text-to-face generation. However, these unimodal generation methods lack co…
CLIPCleaner: Cleaning Noisy Labels with CLIP
Chen Feng, Georgios Tzimiropoulos, Ioannis Patras
Learning with Noisy labels (LNL) poses a significant challenge for the Machine Learning community. Some of the most widely used approaches that select as clean samples for which th…
CLIP-DPO: Vision-Language Models as a Source of Preference for Fixing Hallucinations in LVLMs
Yassine Ouali, Adrian Bulat, Brais Martinez +1
Despite recent successes, LVLMs or Large Vision Language Models are prone to hallucinating details like objects and their properties or relations, limiting their real-world deploym…
MobileQuant: Mobile-friendly Quantization for On-device Language Models
Fuwen Tan, Royson Lee, Łukasz Dudziak +5
Large language models (LLMs) have revolutionized language processing, delivering outstanding results across multiple applications. However, deploying LLMs on edge devices poses sev…
Efficient Unsupervised Visual Representation Learning with Explicit Cluster Balancing
Ioannis Maniadis Metaxas, Georgios Tzimiropoulos, Ioannis Patras
Self-supervised learning has recently emerged as the preeminent pretraining paradigm across and between modalities, with remarkable results. In the image domain specifically, group…
MeMSVD: Long-Range Temporal Structure Capturing Using Incremental SVD
Ioanna Ntinou, Enrique Sanchez, Georgios Tzimiropoulos
This paper is on long-term video understanding where the goal is to recognise human actions over long temporal windows (up to minutes long). In prior work, long temporal context is…
FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-Language models
Adrian Bulat, Yassine Ouali, Georgios Tzimiropoulos
Despite noise and caption quality having been acknowledged as important factors impacting vision-language contrastive pre-training, in this paper, we show that the full potential o…
VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning
Alexandros Xenos, Niki Maria Foteinopoulou, Ioanna Ntinou +2
Recognising emotions in context involves identifying an individual's apparent emotions while considering contextual cues from the surrounding scene. Previous approaches to this tas…
LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition
Zhonglin Sun, Chen Feng, Ioannis Patras +1
In this work we focus on learning facial representations that can be adapted to train effective face recognition models, particularly in the absence of labels. Firstly, compared wi…
DiffusionAct: Controllable Diffusion Autoencoder for One-shot Face Reenactment
Stella Bounareli, Christos Tzelepis, Vasileios Argyriou +2
Video-driven neural face reenactment aims to synthesize realistic facial images that successfully preserve the identity and appearance of a source face, while transferring the targ…
One-shot Neural Face Reenactment via Finding Directions in GAN's Latent Space
Stella Bounareli, Christos Tzelepis, Vasileios Argyriou +2
In this paper, we present our framework for neural face/head reenactment whose goal is to transfer the 3D head orientation and expression of a target face to a source face. Previou…
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation
Mehdi Noroozi, Isma Hadji, Brais Martinez +2
In this paper, we introduce YONOS-SR, a novel stable diffusion-based approach for image super-resolution that yields state-of-the-art results using only a single DDIM step. We prop…
Graph Guided Question Answer Generation for Procedural Question-Answering
Hai X. Pham, Isma Hadji, Xinnuo Xu +6
In this paper, we focus on task-specific question answering (QA). To this end, we introduce a method for generating exhaustive and high-quality training data, which allows us to tr…