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Kanishk Jain

Doctorat - UdeM
Superviseur⋅e principal⋅e
Aishwarya Agrawal
Sujets de recherche
Apprentissage multimodal
Site web
Google Scholar
GitHub

Publications

Discovering Failure Modes in Vision-Language Models using RL
Kanishk Jain
Qian Yang
Shravan Nayak
Parisa Kordjamshidi
Nishanth Anand
Aishwarya Agrawal
Vision-language Models (VLMs), despite achieving strong performance on multimodal benchmarks, often misinterpret straightforward visual conc… (voir plus)epts that humans identify effortlessly, such as counting, spatial reasoning, and viewpoint understanding. Previous studies manually identified these weaknesses and found that they often stem from deficits in specific skills. However, such manual efforts are costly, unscalable, and subject to human bias, which often overlooks subtle details in favor of salient objects, resulting in an incomplete understanding of a model's vulnerabilities. To address these limitations, we propose a Reinforcement Learning (RL)-based framework to automatically discover the failure modes or blind spots of any candidate VLM on a given data distribution without human intervention. Our framework trains a questioner agent that adaptively generates queries based on the candidate VLM's responses to elicit incorrect answers. Our approach increases question complexity by focusing on fine-grained visual details and distinct skill compositions as training progresses, consequently identifying 36 novel failure modes in which VLMs struggle. We demonstrate the broad applicability of our framework by showcasing its generalizability across various model combinations.
2026-04-05
arXiv (prépublication)
doi.org
arxiv.org
Learning What Matters: Prioritized Concept Learning via Relative Error-driven Sample Selection
Shivam Chandhok
Qian Yang
Oscar Mañas
Kanishk Jain
Leonid Sigal
Aishwarya Agrawal
Instruction tuning has been central to the success of recent vision-language models (VLMs), but it remains expensive-requiring large-scale d… (voir plus)atasets, high-quality annotations, and large compute budgets. We propose PRioritized cOncept learninG via Relative Error-driven Sample Selection (PROGRESS), a data- and compute-efficient framework that enables VLMs to dynamically select what to learn next based on their evolving needs during training. At each stage, the model tracks its learning progress across skills and selects the most informative samples-those it has not already mastered and that are not too difficult to learn at the current stage of training. This strategy effectively controls skill acquisition and the order in which skills are learned. Specifically, we sample from skills showing the highest learning progress, prioritizing those with the most rapid improvement. Unlike prior methods, PROGRESS requires no upfront answer annotations, queries answers only on a need basis, avoids reliance on additional supervision from auxiliary VLMs, and does not require compute-heavy gradient computations for data selection. Experiments across multiple instruction-tuning datasets of varying scales demonstrate that PROGRESS consistently outperforms state-of-the-art baselines with much less data and supervision. Additionally, we show strong cross-architecture generalization and transferability to larger models, validating PROGRESS as a scalable solution for efficient learning.
2025-06-10
ES-FoMo @ International Conference on Machine Learning (publié)
doi.org
openreview.net
Benchmarking Vision Language Models for Cultural Understanding
Shravan Nayak
Kanishk Jain
Rabiul Awal
Siva Reddy
Sjoerd van Steenkiste
Lisa Anne Hendricks
Karolina Stanczak
Aishwarya Agrawal
Foundation models and vision-language pre-training have notably advanced Vision Language Models (VLMs), enabling multimodal processing of vi… (voir plus)sual and linguistic data. However, their performance has been typically assessed on general scene understanding - recognizing objects, attributes, and actions - rather than cultural comprehension. This study introduces CulturalVQA, a visual question-answering benchmark aimed at assessing VLM's geo-diverse cultural understanding. We curate a collection of 2,378 image-question pairs with 1-5 answers per question representing cultures from 11 countries across 5 continents. The questions probe understanding of various facets of culture such as clothing, food, drinks, rituals, and traditions. Benchmarking VLMs on CulturalVQA, including GPT-4V and Gemini, reveals disparity in their level of cultural understanding across regions, with strong cultural understanding capabilities for North America while significantly lower performance for Africa. We observe disparity in their performance across cultural facets too, with clothing, rituals, and traditions seeing higher performances than food and drink. These disparities help us identify areas where VLMs lack cultural understanding and demonstrate the potential of CulturalVQA as a comprehensive evaluation set for gauging VLM progress in understanding diverse cultures.
2023-12-31
EMNLP (publié)
doi.org
arxiv.org