17 citations · 60 across the 18 of their papers we have counts for
19 papers · 1 filter
Learning to Rank Caption Chains for Video-Text Alignment
Ansel Blume, Burak Uzkent, Shalini Chaudhuri +1
Direct preference optimization (DPO) is an effective technique to train language models to generate preferred over dispreferred responses. However, this binary "winner-takes-all" a…
Narrative Aligned Long Form Video Question Answering
Rahul Jain, Keval Doshi, Burak Uzkent +1
Recent progress in multimodal large language models (MLLMs) has led to a surge of benchmarks for long-video reasoning. However, most existing benchmarks rely on localized cues and…
CounterVid: Counterfactual Video Generation for Mitigating Action and Temporal Hallucinations in Video-Language Models
Tobia Poppi, Burak Uzkent, Amanmeet Garg +7
Video-language models (VLMs) achieve strong multimodal understanding but remain prone to hallucinations, especially when reasoning about actions and temporal order. Existing mitiga…
From Frames to Clips: Training-free Adaptive Key Clip Selection for Long-Form Video Understanding
Guangyu Sun, Archit Singhal, Burak Uzkent +3
Video Large Language Models (VLMs) have achieved strong performance on various vision-language tasks, yet their practical use is limited by the massive number of visual tokens prod…
Augment the Pairs: Semantics-Preserving Image-Caption Pair Augmentation for Grounding-Based Vision and Language Models
Jingru Yi, Burak Uzkent, Oana Ignat +4
Grounding-based vision and language models have been successfully applied to low-level vision tasks, aiming to precisely locate objects referred in captions. The effectiveness of g…
Negative Data Augmentation
Abhishek Sinha, Kumar Ayush, Jiaming Song +3
Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations,…
Efficient Conditional Pre-training for Transfer Learning
Shuvam Chakraborty, Burak Uzkent, Kumar Ayush +3
Almost all the state-of-the-art neural networks for computer vision tasks are trained by (1) pre-training on a large-scale dataset and (2) finetuning on the target dataset. This st…
Geography-Aware Self-Supervised Learning
Kumar Ayush, Burak Uzkent, Chenlin Meng +4
Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks. In this paper, we explore their application…
Predicting Livelihood Indicators from Community-Generated Street-Level Imagery
Jihyeon Lee, Dylan Grosz, Burak Uzkent +4
Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thu…
Efficient Poverty Mapping using Deep Reinforcement Learning
Kumar Ayush, Burak Uzkent, Kumar Tanmay +3
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure meas…
Learning When and Where to Zoom with Deep Reinforcement Learning
Burak Uzkent, Stefano Ermon
While high resolution images contain semantically more useful information than their lower resolution counterparts, processing them is computationally more expensive, and in some a…
Generating Interpretable Poverty Maps using Object Detection in Satellite Images
Kumar Ayush, Burak Uzkent, Marshall Burke +2
Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scar…
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
Vishnu Sarukkai, Anirudh Jain, Burak Uzkent +1
Satellite images hold great promise for continuous environmental monitoring and earth observation. Occlusions cast by clouds, however, can severely limit coverage, making ground in…
Efficient Object Detection in Large Images using Deep Reinforcement Learning
Burak Uzkent, Christopher Yeh, Stefano Ermon
Traditionally, an object detector is applied to every part of the scene of interest, and its accuracy and computational cost increases with higher resolution images. However, in so…
Learning to Interpret Satellite Images in Global Scale Using Wikipedia
Burak Uzkent, Evan Sheehan, Chenlin Meng +4
Despite recent progress in computer vision, finegrained interpretation of satellite images remains challenging because of a lack of labeled training data. To overcome this limitati…
Learning to Interpret Satellite Images Using Wikipedia
Evan Sheehan, Burak Uzkent, Chenlin Meng +4
Despite recent progress in computer vision, fine-grained interpretation of satellite images remains challenging because of a lack of labeled training data. To overcome this limitat…
EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object Tracking
Burak Uzkent, YoungWoo Seo
Computer vision technologies are very attractive for practical applications running on embedded systems. For such an application, it is desirable for the deployed algorithms to run…
Tracking in Aerial Hyperspectral Videos using Deep Kernelized Correlation Filters
Burak Uzkent, Aneesh Rangnekar, Matthew J. Hoffman
Hyperspectral imaging holds enormous potential to improve the state-of-the-art in aerial vehicle tracking with low spatial and temporal resolutions. Recently, adaptive multi-modal…
Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps
Burak Uzkent, Aneesh Rangnekar, M. J. Hoffman
Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks. In this paper, we propose a nov…