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Igor Vasiljevic

12 accepted papers

2025

Language models scale reliably with over-training and on downstream tasks

ICLR 2025poster

Scaling laws are useful guides for derisking expensive training runs, as they predict performance of large models using cheaper, small-scale experiments. However, there remain gaps between current scaling studies and how language models are ultimately trained and evaluated. For instance, scaling is…

2025

Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry

IROS 2025

Monocular visual odometry is a key technology in various autonomous systems. Traditional feature-based methods suffer from failures due to poor lighting, insufficient texture, and large motions. In contrast, recent learning-based dense SLAM methods exploit iterative dense bundle adjustment to addres

Cited by 1SourcecodeScholar
2025

Should VLMs be Pre-trained with Image Data?

ICLR 2025poster

Pre-trained LLMs that are further trained with image data perform well on vision-language tasks. While adding images during a second training phase effectively unlocks this capability, it is unclear how much of a gain or loss this two-step pipeline gives over VLMs which integrate images earlier int…

Cited by 0SourcePDFScholar
2025

Understanding Complexity in VideoQA via Visual Program Generation

ICML 2025poster

We propose a data-driven approach to analyzing query complexity in Video Question Answering (VideoQA). Previous efforts in benchmark design have relied on human expertise to design challenging questions, yet we experimentally show that humans struggle to predict which questions are difficult for mac…

Cited by 0SourcePDFScholar
2024

DataComp-LM: In search of the next generation of training sets for language models

NeurIPS 2024poster

We introduce DataComp for Language Models, a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardized corpus of 240T tokens extracted from Common Crawl, effective pretraining recipes based on the OpenLM framework, and a broad s…

Cited by 64SourcePDFScholar
2024

Transcrib3D: 3D Referring Expression Resolution through Large Language Models

IROS 2024poster

If robots are to work effectively alongside people, they must be able to interpret natural language references to objects in their 3D environment. Understanding 3D referring expressions is challenging—it requires the ability to both parse the 3D structure of the scene and correctly ground free-form…

Cited by 5SourcecodeScholar
2023

DeLiRa: Self-Supervised Depth, Light, and Radiance Fields

ICCV 2023poster

Differentiable volumetric rendering is a powerful paradigm for 3D reconstruction and novel view synthesis. However, standard volume rendering approaches struggle with degenerate geometries in the case of limited viewpoint diversity, a common scenario in robotics applications. In this work, we propos…

Cited by 4PDFScholar
2023

Robust Self-Supervised Extrinsic Self-Calibration

IROS 2023poster

Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predi…

Cited by 6SourceScholar
2023

Towards Zero-Shot Scale-Aware Monocular Depth Estimation

ICCV 2023poster

Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to produce metric predictions. Even so, the resulting models will be geometry-specific, with learned scales that cannot be directly transferred across domains. Because of that, recent works focus instead on relative d…

Cited by 145PDFcodeScholar
2022

Depth Field Networks for Generalizable Multi-View Scene Representation

ECCV 2022poster

"Modern 3D computer vision leverages learning to boost geometric reasoning, mapping image data to classical structures such as cost volumes or epipolar constraints to improve matching. These architectures are specialized according to the particular problem, and thus require significant task-specific…

Cited by 16SourcePDFScholar
2022

Self-Supervised Camera Self-Calibration from Video

ICRA 2022poster

Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams. In practice, calibration is a laborious procedure requiring specialized data collection and careful tuning. This process must be repeated whenever…

Cited by 31SourceScholar