5 papers
InfSplign: Inference-Time Spatial Alignment of Text-to-Image Diffusion Models
Sarah Rastegar, Violeta Chatalbasheva, Sieger Falkena +5
Text-to-image (T2I) diffusion models generate high-quality images but often fail to capture the spatial relations specified in text prompts. This limitation can be traced to two fa…
Side Effects of Erasing Concepts from Diffusion Models
Shaswati Saha, Sourajit Saha, Manas Gaur +1
Concerns about text-to-image (T2I) generative models infringing on privacy, copyright, and safety have led to the development of concept erasure techniques (CETs). The goal of an e…
VOILA: Evaluation of MLLMs For Perceptual Understanding and Analogical Reasoning
Nilay Yilmaz, Maitreya Patel, Yiran Lawrence Luo +4
Multimodal Large Language Models (MLLMs) have become a powerful tool for integrating visual and textual information. Despite their exceptional performance on visual understanding b…
Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase Sampling
Sourajit Saha, Tejas Gokhale
Downsampling operators break the shift invariance of convolutional neural networks (CNNs) and this affects the robustness of features learned by CNNs when dealing with even small p…
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives
Maitreya Patel, Abhiram Kusumba, Sheng Cheng +4
Contrastive Language-Image Pretraining (CLIP) models maximize the mutual information between text and visual modalities to learn representations. This makes the nature of the train…