20 citations · 56 across the 19 of their papers we have counts for
18 papers · 1 filter
HorizonComposer: Spatiotemporally Consistent Driving Video Editing with Enriched Traffic Semantics
Mauricio Soroco, Yuqiu Liu, Zaid Tasneem +5
Instruction-guided video generative models offer a scalable solution for stress-testing autonomous driving systems by simulating diverse environmental conditions. However, driving…
CAViAR: A Causal Video Dataset for Fine-Grained Accident Reasoning in Real-World Scenarios
Sparsh Garg, Yi-Wen Chen, Vijay Kumar B G +1
While modern autonomous driving systems excel at perception tasks such as object detection and trajectory prediction, they lack the high-level causal reasoning required to interpre…
Driving Video Retrieval for Complex Queries with Structured Grounding
Manyi Yao, Sparsh Garg, Christian Shelton +2
Video retrieval at scale is central to data curation and safety validation in autonomous driving, where users want to find not only scenes but also dynamic events such as cut-ins a…
What to Test Next: Interpretable Coverage Gap Discovery in Driving VLMs
Abhishek Aich, Sparsh Garg, Vijay Kumar BG +2
Driving vision-language models (VLMs) must accurately understand scenes across diverse conditions defined by Operational Design Domains (ODDs), yet verification remains sparse: man…
HorizonWeaver: Generalizable Multi-Level Semantic Editing for Driving Scenes
Mauricio Soroco, Francesco Pittaluga, Zaid Tasneem +5
Ensuring safety in autonomous driving requires scalable generation of realistic, controllable driving scenes beyond what real-world testing provides. Yet existing instruction guide…
iFinder: Structured Zero-Shot Vision-Based LLM Grounding for Dash-Cam Video Reasoning
Manyi Yao, Bingbing Zhuang, Sparsh Garg +4
Grounding large language models (LLMs) in domain-specific tasks like post-hoc dash-cam driving video analysis is challenging due to their general-purpose training and lack of struc…
Mapillary Vistas Validation for Fine-Grained Traffic Signs: A Benchmark Revealing Vision-Language Model Limitations
Sparsh Garg, Abhishek Aich
Obtaining high-quality fine-grained annotations for traffic signs is critical for accurate and safe decision-making in autonomous driving. Widely used datasets, such as Mapillary,…
Resolving Inconsistent Semantics in Multi-Dataset Image Segmentation
Qilong Zhangli, Di Liu, Abhishek Aich +2
Leveraging multiple training datasets to scale up image segmentation models is beneficial for increasing robustness and semantic understanding. Individual datasets have well-define…
Image-Specific Adaptation of Transformer Encoders for Compute-Efficient Segmentation
Manyi Yao, Abhishek Aich, Yumin Suh +3
Vision transformer based models bring significant improvements for image segmentation tasks. Although these architectures offer powerful capabilities irrespective of specific segme…
Progressive Token Length Scaling in Transformer Encoders for Efficient Universal Segmentation
Abhishek Aich, Yumin Suh, Samuel Schulter +1
A powerful architecture for universal segmentation relies on transformers that encode multi-scale image features and decode object queries into mask predictions. With efficiency be…
Efficient Controllable Multi-Task Architectures
Abhishek Aich, Samuel Schulter, Amit K. Roy-Chowdhury +2
We aim to train a multi-task model such that users can adjust the desired compute budget and relative importance of task performances after deployment, without retraining. This ena…
Cross-Domain Video Anomaly Detection without Target Domain Adaptation
Abhishek Aich, Kuan-Chuan Peng, Amit K. Roy-Chowdhury
Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume that at least few task-relevant target domain training data are available for adaptation from the source t…
GAMA: Generative Adversarial Multi-Object Scene Attacks
Abhishek Aich, Calvin-Khang Ta, Akash Gupta +4
The majority of methods for crafting adversarial attacks have focused on scenes with a single dominant object (e.g., images from ImageNet). On the other hand, natural scenes includ…
Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks
Abhishek Aich, Shasha Li, Chengyu Song +3
State-of-the-art generative model-based attacks against image classifiers overwhelmingly focus on single-object (i.e., single dominant object) images. Different from such settings,…
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
Shasha Li, Abhishek Aich, Shitong Zhu +4
When compared to the image classification models, black-box adversarial attacks against video classification models have been largely understudied. This could be possible because,…
Spatio-Temporal Representation Factorization for Video-based Person Re-Identification
Abhishek Aich, Meng Zheng, Srikrishna Karanam +3
Despite much recent progress in video-based person re-identification (re-ID), the current state-of-the-art still suffers from common real-world challenges such as appearance simila…
Elastic Weight Consolidation (EWC): Nuts and Bolts
Abhishek Aich
In this report, we present a theoretical support of the continual learning method \textbf{Elastic Weight Consolidation}, introduced in paper titled `Overcoming catastrophic forgett…
ALANET: Adaptive Latent Attention Network forJoint Video Deblurring and Interpolation
Akash Gupta, Abhishek Aich, Amit K. Roy-Chowdhury
Existing works address the problem of generating high frame-rate sharp videos by separately learning the frame deblurring and frame interpolation modules. Most of these approaches…