1 citations · 1 across the 6 of their papers we have counts for
7 papers
GoldiCLIP: The Goldilocks Approach for Balancing Explicit Supervision for Language-Image Pretraining
Deen Dayal Mohan, Hossein Souri, Vitali Petsiuk +4
Until recently, the success of large-scale vision-language models (VLMs) has primarily relied on billion-sample datasets, posing a significant barrier to progress. Latest works hav…
ARGENT: Adaptive Hierarchical Image-Text Representations
Chuong Huynh, Hossein Souri, Abhinav Kumar +3
Large-scale Vision-Language Models (VLMs) such as CLIP learn powerful semantic representations but operate in Euclidean space, which fails to capture the inherent hierarchical stru…
Towards High-Fidelity Gaussian Splatting with Queried-Convolution Neural Networks
Abhinav Kumar, Tristan Aumentado-Armstrong, Lazar Valkov +4
Gaussian Splatting has revolutionized the field of Novel View Synthesis (NVS) with faster training and real-time rendering. However, its reconstruction fidelity still trails behind…
Finetuning-Free Personalization of Text to Image Generation via Hypernetworks
Sagar Shrestha, Gopal Sharma, Luowei Zhou +1
Personalizing text-to-image diffusion models has traditionally relied on subject-specific fine-tuning approaches such as DreamBooth~\cite{ruiz2023dreambooth}, which are computation…
ADIEE: Automatic Dataset Creation and Scorer for Instruction-Guided Image Editing Evaluation
Sherry X. Chen, Yi Wei, Luowei Zhou +1
Recent advances in instruction-guided image editing underscore the need for effective automated evaluation. While Vision-Language Models (VLMs) have been explored as judges, open-s…
Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All
Mehmet Saygin Seyfioglu, Karim Bouyarmane, Suren Kumar +2
As online shopping is growing, the ability for buyers to virtually visualize products in their settings-a phenomenon we define as "Virtual Try-All"-has become crucial. Recent diffu…
Domain Aligned CLIP for Few-shot Classification
Muhammad Waleed Gondal, Jochen Gast, Inigo Alonso Ruiz +4
Large vision-language representation learning models like CLIP have demonstrated impressive performance for zero-shot transfer to downstream tasks while largely benefiting from int…