activity
20192026
most citedAdversarial Bipartite Graph Learning for Video Domain Adaptation

38 citations · 65 across the 15 of their papers we have counts for

collaborators

18 papers

cs.CV2026

Foveated Probes Recover Localized Binding Information in Vision Foundation Models

Mateusz Michalkiewicz, Mahsa Baktashmotlagh, Guha Balakrishnan

Frozen vision foundation models are commonly evaluated through a single global image embedding, but this interface can conflate missing information with information lost at readout…

cs.CL2026

T5-CSBoost: Adversarial Perturbation Resistant LLM Fingerprinting

Gayan K. Kulatilleke, Mahsa Baktashmotlagh, Siamak Layeghy +1

While many AI-generated text (AIGT) detectors achieve strong performance on clean inputs, their accuracy degrades significantly under light paraphrasing, word substitutions, charac…

cs.CV2026

Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging

Tan Pan, Shuhao Mei, Yixuan Sun +8

Self-supervised pre-training methods in medical imaging typically treat each individual as an isolated instance, learning representations through augmentation-based objectives or m…

cs.CV2025

WisWheat: A Three-Tiered Vision-Language Dataset for Wheat Management

Bowen Yuan, Selena Song, Javier Fernandez +3

Wheat management strategies play a critical role in determining yield. Traditional management decisions often rely on labour-intensive expert inspections, which are expensive, subj…

cs.CV2024

Source-Free Domain-Invariant Performance Prediction

Ekaterina Khramtsova, Mahsa Baktashmotlagh, Guido Zuccon +2

Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most exist…

cs.IR20241 cited

Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection

Ekaterina Khramtsova, Teerapong Leelanupab, Shengyao Zhuang +2

In this demo we present a web-based application for selecting an effective pre-trained dense retriever to use on a private collection. Our system, DenseQuest, provides unsupervised…