2 citations · 5 across the 12 of their papers we have counts for
13 papers
UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders
Matthew Walmer, Saksham Suri, Anirud Aggarwal +1
The space of task-agnostic feature upsampling has emerged as a promising area of research to efficiently create denser features from pre-trained visual backbones. These methods act…
VeriGraph: Scene Graphs for Execution Verifiable Robot Planning
Daniel Ekpo, Mara Levy, Saksham Suri +3
Recent progress in vision-language models (VLMs) has opened new possibilities for robot task planning, but these models often produce incorrect action sequences. To address these l…
LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior
Hanyu Wang, Saksham Suri, Yixuan Ren +2
We present LARP, a novel video tokenizer designed to overcome limitations in current video tokenization methods for autoregressive (AR) generative models. Unlike traditional patchw…
UVIS: Unsupervised Video Instance Segmentation
Shuaiyi Huang, Saksham Suri, Kamal Gupta +3
Video instance segmentation requires classifying, segmenting, and tracking every object across video frames. Unlike existing approaches that rely on masks, boxes, or category label…
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…
Gen2Det: Generate to Detect
Saksham Suri, Fanyi Xiao, Animesh Sinha +4
Recently diffusion models have shown improvement in synthetic image quality as well as better control in generation. We motivate and present Gen2Det, a simple modular pipeline to c…
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…
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…
SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining
Saksham Suri, Sai Saketh Rambhatla, Rama Chellappa +1
Training with sparse annotations is known to reduce the performance of object detectors. Previous methods have focused on proxies for missing ground truth annotations in the form o…
Towards Discovery and Attribution of Open-world GAN Generated Images
Sharath Girish, Saksham Suri, Saketh Rambhatla +1
With the recent progress in Generative Adversarial Networks (GANs), it is imperative for media and visual forensics to develop detectors which can identify and attribute images to…
Learned Spatial Representations for Few-shot Talking-Head Synthesis
Moustafa Meshry, Saksham Suri, Larry S. Davis +1
We propose a novel approach for few-shot talking-head synthesis. While recent works in neural talking heads have produced promising results, they can still produce images that do n…
On Matching Faces with Alterations due to Plastic Surgery and Disguise
Saksham Suri, Anush Sankaran, Mayank Vatsa +1
Plastic surgery and disguise variations are two of the most challenging co-variates of face recognition. The state-of-art deep learning models are not sufficiently successful due t…
An Interpretable Generative Model for Handwritten Digit Image Synthesis
Yao Zhu, Saksham Suri, Pranav Kulkarni +3
An interpretable generative model for handwritten digits synthesis is proposed in this work. Modern image generative models, such as Generative Adversarial Networks (GANs) and Vari…