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S. Majhi

11 papers hereh-index 9204 citations23 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author8

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • cs.CV11
same name
  • S. Majhi — 32 papers, h 7
  • S. Majhi — 31 papers, h 26
  • S. Majhi — 29 papers, h 9
  • S. Majhi — 23 papers, h 23
  • S. Majhi — 7 papers, h 28
  • S. Majhi — 7 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212026
collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CV2025

From SAM to DINOv2: Towards Distilling Foundation Models to Lightweight Baselines for Generalized Polyp Segmentation

Shivanshu Agnihotri, Snehashis Majhi, Deepak Ranjan Nayak +1

Accurate polyp segmentation during colonoscopy is critical for the early detection of colorectal cancer and still remains challenging due to significant size, shape, and color vari…

cs.CV2025

Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection

Giacomo D'Amicantonio, Snehashis Majhi, Quan Kong +4

Video Anomaly Detection (VAD) is a challenging task due to the variability of anomalous events and the limited availability of labeled data. Under the Weakly-Supervised VAD (WSVAD)…

cs.CV2025

Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation

Tapas K. Dutta, Snehashis Majhi, Deepak Ranjan Nayak +1

Polyp segmentation in colonoscopy images is crucial for early detection and diagnosis of colorectal cancer. However, this task remains a significant challenge due to the substantia…

cs.CV2025

Just Dance with π! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection

Snehashis Majhi, Giacomo D'Amicantonio, Antitza Dantcheva +5

Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-worl…

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