160 citations · 163 across the 13 of their papers we have counts for
14 papers
VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale
Parth Parag Kulkarni, Rohit Gupta, Prakash Chandra Chhipa +1
The task of video geolocalization aims to determine the precise GPS coordinates of a video's origin and map its trajectory; with applications in forensics, social media, and explor…
ViLL-E: Video LLM Embeddings for Retrieval
Rohit Gupta, Jayakrishnan Unnikrishnan, Fan Fei +3
Video Large Language Models (VideoLLMs) excel at video understanding tasks where outputs are textual, such as Video Question Answering and Video Captioning. However, they underperf…
StretchySnake: Flexible SSM Training Unlocks Action Recognition Across Spatio-Temporal Scales
Nyle Siddiqui, Rohit Gupta, Sirnam Swetha +1
State space models (SSMs) have emerged as a competitive alternative to transformers in various tasks. Their linear complexity and hidden-state recurrence make them particularly att…
Cross-View Open-Vocabulary Object Detection in Aerial Imagery
Jyoti Kini, Rohit Gupta, Mubarak Shah
Traditional object detection models are typically trained on a fixed set of classes, limiting their flexibility and making it costly to incorporate new categories. Open-vocabulary…
The Telephone Game: Evaluating Semantic Drift in Unified Models
Sabbir Mollah, Rohit Gupta, Sirnam Swetha +3
Unified models (UMs) combine visual understanding (I2T) and generation (T2I) in a single framework. We focus on T2I and I2T, where cross-consistency---what a model understands, it…
VRR-QA: Visual Relational Reasoning in Videos Beyond Explicit Cues
Sirnam Swetha, Rohit Gupta, Parth Parag Kulkarni +5
Video Question Answering (VideoQA) has made significant strides by leveraging multimodal learning to align visual and textual modalities. However, current benchmarks overwhelmingly…
GAEA: A Geolocation Aware Conversational Assistant
Ron Campos, Ashmal Vayani, Parth Parag Kulkarni +4
Image geolocalization, in which an AI model traditionally predicts the precise GPS coordinates of an image, is a challenging task with many downstream applications. However, the us…
BBQ-V: Benchmarking Visual Stereotype Bias in Large Multimodal Models
Vishal Narnaware, Ashmal Vayani, Rohit Gupta +2
Stereotype biases in Large Multimodal Models (LMMs) perpetuate harmful societal prejudices, undermining the fairness and equity of AI applications. As LMMs grow increasingly influe…
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana +66
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cul…
Open Vocabulary Multi-Label Video Classification
Rohit Gupta, Mamshad Nayeem Rizve, Jayakrishnan Unnikrishnan +5
Pre-trained vision-language models (VLMs) have enabled significant progress in open vocabulary computer vision tasks such as image classification, object detection and image segmen…
Query Efficient Cross-Dataset Transferable Black-Box Attack on Action Recognition
Rohit Gupta, Naveed Akhtar, Gaurav Kumar Nayak +2
Black-box adversarial attacks present a realistic threat to action recognition systems. Existing black-box attacks follow either a query-based approach where an attack is optimized…
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility
Rohit Gupta, Naveed Akhtar, Ajmal Mian +1
Contrastive self-supervised learning (CSL) has managed to match or surpass the performance of supervised learning in image and video classification. However, it is still largely un…
"Knights": First Place Submission for VIPriors21 Action Recognition Challenge at ICCV 2021
Ishan Dave, Naman Biyani, Brandon Clark +3
This technical report presents our approach "Knights" to solve the action recognition task on a small subset of Kinetics-400 i.e. Kinetics400ViPriors without using any extra-data.…
TCLR: Temporal Contrastive Learning for Video Representation
Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve +1
Contrastive learning has nearly closed the gap between supervised and self-supervised learning of image representations, and has also been explored for videos. However, prior work…