activity
20152022
most citedDualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks

202 citations · 356 across the 36 of their papers we have counts for

collaborators

49 papers

cs.LG20224 cited

Fast Parallel Bayesian Network Structure Learning

Jiantong Jiang, Zeyi Wen, Ajmal Mian

Bayesian networks (BNs) are a widely used graphical model in machine learning for representing knowledge with uncertainty. The mainstream BN structure learning methods require perf…

cs.CV20221 cited

Query Efficient Cross-Dataset Transferable Black-Box Attack on Action Recognition

Rohit Gupta, Naveed Akhtar, Gaurav Kumar Nayak +2

Black-box adversarial attacks present a realistic threat to action recognition systems. Existing black-box attacks follow either a query-based approach where an attack is optimized…

cs.CV2022

3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds

Jyoti Kini, Ajmal Mian, Mubarak Shah

We propose a method for joint detection and tracking of multiple objects in 3D point clouds, a task conventionally treated as a two-step process comprising object detection followe…

cs.CV202218 cited

Vision Transformers for Action Recognition: A Survey

Anwaar Ulhaq, Naveed Akhtar, Ganna Pogrebna +1

Vision transformers are emerging as a powerful tool to solve computer vision problems. Recent techniques have also proven the efficacy of transformers beyond the image domain to so…

cs.CV20221 cited

Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection

Mingtao Feng, Kendong Liu, Liang Zhang +3

Saliency detection with light field images is becoming attractive given the abundant cues available, however, this comes at the expense of large-scale pixel level annotated data wh…

cs.CV202216 cited

UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning

Nazmul Karim, Mamshad Nayeem Rizve, Nazanin Rahnavard +2

Supervised deep learning methods require a large repository of annotated data; hence, label noise is inevitable. Training with such noisy data negatively impacts the generalization…