7 citations · 14 across the 9 of their papers we have counts for
13 papers
LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT Descriptors
Saksham Suri, Matthew Walmer, Kamal Gupta +1
We present a simple self-supervised method to enhance the performance of ViT features for dense downstream tasks. Our Lightweight Feature Transform (LiFT) is a straightforward and…
EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS
Sharath Girish, Kamal Gupta, Abhinav Shrivastava
Recently, 3D Gaussian splatting (3D-GS) has gained popularity in novel-view scene synthesis. It addresses the challenges of lengthy training times and slow rendering speeds associa…
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…
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…
ASIC: Aligning Sparse in-the-wild Image Collections
Kamal Gupta, Varun Jampani, Carlos Esteves +4
We present a method for joint alignment of sparse in-the-wild image collections of an object category. Most prior works assume either ground-truth keypoint annotations or a large d…
Teaching Matters: Investigating the Role of Supervision in Vision Transformers
Matthew Walmer, Saksham Suri, Kamal Gupta +1
Vision Transformers (ViTs) have gained significant popularity in recent years and have proliferated into many applications. However, their behavior under different learning paradig…
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…
Neural Space-filling Curves
Hanyu Wang, Kamal Gupta, Larry Davis +1
We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images. Linear ordering of pixels forms the basis for many ap…
PatchGame: Learning to Signal Mid-level Patches in Referential Games
Kamal Gupta, Gowthami Somepalli, Anubhav Gupta +3
We study a referential game (a type of signaling game) where two agents communicate with each other via a discrete bottleneck to achieve a common goal. In our referential game, the…
The Lottery Ticket Hypothesis for Object Recognition
Sharath Girish, Shishira R. Maiya, Kamal Gupta +3
Recognition tasks, such as object recognition and keypoint estimation, have seen widespread adoption in recent years. Most state-of-the-art methods for these tasks use deep network…
Improved Modeling of 3D Shapes with Multi-view Depth Maps
Kamal Gupta, Susmija Jabbireddy, Ketul Shah +2
We present a simple yet effective general-purpose framework for modeling 3D shapes by leveraging recent advances in 2D image generation using CNNs. Using just a single depth image…
LayoutTransformer: Layout Generation and Completion with Self-attention
Kamal Gupta, Justin Lazarow, Alessandro Achille +3
We address the problem of scene layout generation for diverse domains such as images, mobile applications, documents, and 3D objects. Most complex scenes, natural or human-designed…
PatchVAE: Learning Local Latent Codes for Recognition
Kamal Gupta, Saurabh Singh, Abhinav Shrivastava
Unsupervised representation learning holds the promise of exploiting large amounts of unlabeled data to learn general representations. A promising technique for unsupervised learni…