304 citations · 441 across the 22 of their papers we have counts for
50 papers
Mitigating Hallucinations in Diffusion Models through Adaptive Attention Modulation
Trevine Oorloff, Yaser Yacoob, Abhinav Shrivastava
Diffusion models, while increasingly adept at generating realistic images, are notably hindered by hallucinations -- unrealistic or incorrect features inconsistent with the trained…
LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation
Archana Swaminathan, Anubhav Gupta, Kamal Gupta +3
Neural Radiance Fields (NeRFs) have revolutionized the reconstruction of static scenes and objects in 3D, offering unprecedented quality. However, extending NeRFs to model dynamic…
CNeRV: Content-adaptive Neural Representation for Visual Data
Hao Chen, Matt Gwilliam, Bo He +2
Compression and reconstruction of visual data have been widely studied in the computer vision community, even before the popularization of deep learning. More recently, some have u…
Disentangling Visual Embeddings for Attributes and Objects
Nirat Saini, Khoi Pham, Abhinav Shrivastava
We study the problem of compositional zero-shot learning for object-attribute recognition. Prior works use visual features extracted with a backbone network, pre-trained for object…
LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification
Sharath Girish, Kamal Gupta, Saurabh Singh +1
We introduce LilNetX, an end-to-end trainable technique for neural networks that enables learning models with specified accuracy-rate-computation trade-off. Prior works approach th…
ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization
Bo He, Xitong Yang, Le Kang +3
Weakly-supervised temporal action localization aims to recognize and localize action segments in untrimmed videos given only video-level action labels for training. Without the bou…