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20212026
most citedLearning Open-World Object Proposals without Learning to Classify

7 citations · 7 across the 9 of their papers we have counts for

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cs.CV2026

Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning

Dahun Kim, Ganesh Satish Mallya, Anelia Angelova

Multi-spectral imagery is a valuable input signal for Remote Sensing applications, such as land-use and land-cover classification and environmental monitoring. However, generalist…

cs.CV2025

Zero-Shot Multi-Spectral Learning: Reimagining a Generalist Multimodal Gemini 2.5 Model for Remote Sensing Applications

Ganesh Mallya, Yotam Gigi, Dahun Kim +4

Multi-spectral imagery plays a crucial role in diverse Remote Sensing applications including land-use classification, environmental monitoring and urban planning. These images are…

cs.CV2025

Time-Scaling State-Space Models for Dense Video Captioning

AJ Piergiovanni, Ganesh Satish Mallya, Dahun Kim +1

Dense video captioning is a challenging video understanding task which aims to simultaneously segment the video into a sequence of meaningful consecutive events and to generate det…

cs.CV2025

VideoComp: Advancing Fine-Grained Compositional and Temporal Alignment in Video-Text Models

Dahun Kim, AJ Piergiovanni, Ganesh Mallya +1

We introduce VideoComp, a benchmark and learning framework for advancing video-text compositionality understanding, aimed at improving vision-language models (VLMs) in fine-grained…

cs.CV2024

Whats in a Video: Factorized Autoregressive Decoding for Online Dense Video Captioning

AJ Piergiovanni, Dahun Kim, Michael S. Ryoo +2

Generating automatic dense captions for videos that accurately describe their contents remains a challenging area of research. Most current models require processing the entire vid…

cs.CV2024

Learning Visual Grounding from Generative Vision and Language Model

Shijie Wang, Dahun Kim, Ali Taalimi +2

Visual grounding tasks aim to localize image regions based on natural language references. In this work, we explore whether generative VLMs predominantly trained on image-text data…