activity
20192021
most citedChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations

31 citations · 42 across the 6 of their papers we have counts for

collaborators
Showing cs.CVShow all

8 papers · 1 filter

cs.CV20213 cited

Livestock Monitoring with Transformer

Bhavesh Tangirala, Ishan Bhandari, Daniel Laszlo +3

Tracking the behaviour of livestock enables early detection and thus prevention of contagious diseases in modern animal farms. Apart from economic gains, this would reduce the amou…

cs.CV202131 cited

ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations

Rishabh Tiwari, Udbhav Bamba, Arnav Chavan +1

Structured pruning methods are among the effective strategies for extracting small resource-efficient convolutional neural networks from their dense counterparts with minimal loss…

cs.CV2021

Rescaling CNN through Learnable Repetition of Network Parameters

Arnav Chavan, Udbhav Bamba, Rishabh Tiwari +1

Deeper and wider CNNs are known to provide improved performance for deep learning tasks. However, most such networks have poor performance gain per parameter increase. In this pape…

cs.CV2020

Rotation Equivariant Siamese Networks for Tracking

Deepak K. Gupta, Devanshu Arya, Efstratios Gavves

Rotation is among the long prevailing, yet still unresolved, hard challenges encountered in visual object tracking. The existing deep learning-based tracking algorithms use regular…

cs.CV20204 cited

Siamese Tracking with Lingual Object Constraints

Maximilian Filtenborg, Efstratios Gavves, Deepak Gupta

Classically, visual object tracking involves following a target object throughout a given video, and it provides us the motion trajectory of the object. However, for many practical…

cs.CV2020

Hard Occlusions in Visual Object Tracking

Thijs P. Kuipers, Devanshu Arya, Deepak K. Gupta

Visual object tracking is among the hardest problems in computer vision, as trackers have to deal with many challenging circumstances such as illumination changes, fast motion, occ…