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20212024
most citedDiffusion Models Beat GANs on Image Classification

16 citations · 27 across the 17 of their papers we have counts for

collaborators

17 papers

cs.CV20241 cited

MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding

Bo He, Hengduo Li, Young Kyun Jang +5

With the success of large language models (LLMs), integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However,…

cs.CV2024

What is Point Supervision Worth in Video Instance Segmentation?

Shuaiyi Huang, De-An Huang, Zhiding Yu +5

Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos. Conventional VIS methods rely on densely-annotated object…

cs.CV20241 cited

Measuring Style Similarity in Diffusion Models

Gowthami Somepalli, Anubhav Gupta, Kamal Gupta +5

Generative models are now widely used by graphic designers and artists. Prior works have shown that these models remember and often replicate content from their training data durin…

cs.CV2023

SHACIRA: Scalable HAsh-grid Compression for Implicit Neural Representations

Sharath Girish, Abhinav Shrivastava, Kamal Gupta

Implicit Neural Representations (INR) or neural fields have emerged as a popular framework to encode multimedia signals such as images and radiance fields while retaining high-qual…

cs.CV2023

Chop & Learn: Recognizing and Generating Object-State Compositions

Nirat Saini, Hanyu Wang, Archana Swaminathan +4

Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions. In this paper, we study the task of cutting o…

cs.CV20232 cited

Diff2Lip: Audio Conditioned Diffusion Models for Lip-Synchronization

Soumik Mukhopadhyay, Saksham Suri, Ravi Teja Gadde +1

The task of lip synchronization (lip-sync) seeks to match the lips of human faces with different audio. It has various applications in the film industry as well as for creating vir…