4 citations · 9 across the 12 of their papers we have counts for
14 papers
RFDM: Residual Flow Diffusion Model for Efficient Causal Video Editing
Mohammadreza Salehi, Mehdi Noroozi, Luca Morreale +4
Instructional video editing applies edits to an input video using only text prompts, enabling intuitive natural-language control. Despite rapid progress, most methods still require…
NanoFLUX: Distillation-Driven Compression of Large Text-to-Image Generation Models for Mobile Devices
Ruchika Chavhan, Malcolm Chadwick, Alberto Gil Couto Pimentel Ramos +3
While large-scale text-to-image diffusion models continue to improve in visual quality, their increasing scale has widened the gap between state-of-the-art models and on-device sol…
FraQAT: Quantization Aware Training with Fractional bits
Luca Morreale, Alberto Gil C. P. Ramos, Malcolm Chadwick +4
State-of-the-art (SOTA) generative models have demonstrated impressive capabilities in image synthesis or text generation, often with a large capacity model. However, these large m…
Guidance Free Image Editing via Explicit Conditioning
Mehdi Noroozi, Alberto Gil Ramos, Luca Morreale +4
Current sampling mechanisms for conditional diffusion models rely mainly on Classifier Free Guidance (CFG) to generate high-quality images. However, CFG requires several denoising…
Upcycling Text-to-Image Diffusion Models for Multi-Task Capabilities
Ruchika Chavhan, Abhinav Mehrotra, Malcolm Chadwick +4
Text-to-image synthesis has witnessed remarkable advancements in recent years. Many attempts have been made to adopt text-to-image models to support multiple tasks. However, existi…
EDiT: Efficient Diffusion Transformers with Linear Compressed Attention
Philipp Becker, Abhinav Mehrotra, Ruchika Chavhan +5
Diffusion Transformers (DiTs) have emerged as a leading architecture for text-to-image synthesis, producing high-quality and photorealistic images. However, the quadratic scaling p…
Memorized Images in Diffusion Models share a Subspace that can be Located and Deleted
Ruchika Chavhan, Ondrej Bohdal, Yongshuo Zong +2
Large-scale text-to-image diffusion models excel in generating high-quality images from textual inputs, yet concerns arise as research indicates their tendency to memorize and repl…
ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning
Ruchika Chavhan, Da Li, Timothy Hospedales
While large-scale text-to-image diffusion models have demonstrated impressive image-generation capabilities, there are significant concerns about their potential misuse for generat…
Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations
Yongshuo Zong, Tingyang Yu, Ruchika Chavhan +2
Large language and vision-language models are rapidly being deployed in practice thanks to their impressive capabilities in instruction following, in-context learning, and so on. T…
Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
Ondrej Bohdal, Yinbing Tian, Yongshuo Zong +5
Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and den…
Amortised Invariance Learning for Contrastive Self-Supervision
Ruchika Chavhan, Henry Gouk, Jan Stuehmer +3
Contrastive self-supervised learning methods famously produce high quality transferable representations by learning invariances to different data augmentations. Invariances establi…
HyperInvariances: Amortizing Invariance Learning
Ruchika Chavhan, Henry Gouk, Jan Stühmer +1
Providing invariances in a given learning task conveys a key inductive bias that can lead to sample-efficient learning and good generalisation, if correctly specified. However, the…
FRIDA -- Generative Feature Replay for Incremental Domain Adaptation
Sayan Rakshit, Anwesh Mohanty, Ruchika Chavhan +3
We tackle the novel problem of incremental unsupervised domain adaptation (IDA) in this paper. We assume that a labeled source domain and different unlabeled target domains are inc…
A Novel Actor Dual-Critic Model for Remote Sensing Image Captioning
Ruchika Chavhan, Biplab Banerjee, Xiao Xiang Zhu +1
We deal with the problem of generating textual captions from optical remote sensing (RS) images using the notion of deep reinforcement learning. Due to the high inter-class similar…