activity
20222025
most citedSegment Anything Meets Point Tracking

38 citations · 43 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2025

BulletTime: Decoupled Control of Time and Camera Pose for Video Generation

Yiming Wang, Qihang Zhang, Shengqu Cai +7

Emerging video diffusion models achieve high visual fidelity but fundamentally couple scene dynamics with camera motion, limiting their ability to provide precise spatial and tempo…

cs.CV2025

Multi-View 3D Point Tracking

Frano Rajič, Haofei Xu, Marko Mihajlovic +7

We introduce the first data-driven multi-view 3D point tracker, designed to track arbitrary points in dynamic scenes using multiple camera views. Unlike existing monocular trackers…

cs.LG2023★ 2 cited

Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations

Ahmad Rahimi, Po-Chien Luan, Yuejiang Liu +2

Modeling spatial-temporal interactions among neighboring agents is at the heart of multi-agent problems such as motion forecasting and crowd navigation. Despite notable progress, i…

cs.CV2023★ 38 cited

Segment Anything Meets Point Tracking

Frano Rajič, Lei Ke, Yu-Wing Tai +3

The Segment Anything Model (SAM) has established itself as a powerful zero-shot image segmentation model, enabled by efficient point-centric annotation and prompt-based models. Whi…

cs.CL2022★ 3 cited

Using Focal Loss to Fight Shallow Heuristics: An Empirical Analysis of Modulated Cross-Entropy in Natural Language Inference

Frano Rajič, Ivan Stresec, Axel Marmet +1

There is no such thing as a perfect dataset. In some datasets, deep neural networks discover underlying heuristics that allow them to take shortcuts in the learning process, result…

cs.CL2022

Language Model Decoding as Likelihood-Utility Alignment

Martin Josifoski, Maxime Peyrard, Frano Rajic +8

A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm re…