most citedSpatio-temporal Relation Modeling for Few-shot Action Recognition

6 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023

Remote Sensing Change Detection With Transformers Trained from Scratch

Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal +4

Current transformer-based change detection (CD) approaches either employ a pre-trained model trained on large-scale image classification ImageNet dataset or rely on first pre-train…

cs.CV2023

Video Instance Segmentation in an Open-World

Omkar Thawakar, Sanath Narayan, Hisham Cholakkal +5

Existing video instance segmentation (VIS) approaches generally follow a closed-world assumption, where only seen category instances are identified and spatio-temporally segmented…

cs.CV2023

Generative Multiplane Neural Radiance for 3D-Aware Image Generation

Amandeep Kumar, Ankan Kumar Bhunia, Sanath Narayan +5

We present a method to efficiently generate 3D-aware high-resolution images that are view-consistent across multiple target views. The proposed multiplane neural radiance model, na…

cs.CV20216 cited

Spatio-temporal Relation Modeling for Few-shot Action Recognition

Anirudh Thatipelli, Sanath Narayan, Salman Khan +3

We propose a novel few-shot action recognition framework, STRM, which enhances class-specific feature discriminability while simultaneously learning higher-order temporal represent…

cs.CV2021

OW-DETR: Open-world Detection Transformer

Akshita Gupta, Sanath Narayan, K J Joseph +3

Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown o…