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Hanoona Abdul Rasheed

5 accepted papers

2026

VideoMathQA: Benchmarking Mathematical Reasoning via Multimodal Understanding in Video

Hanoona Abdul Rasheed, Abdelrahman M Shaker, Anqi Tang, Muhammad Maaz, Ming-Hsuan Yang, Salman Khan +1
ICLR 2026poster

Mathematical reasoning in real-world video presents a fundamentally different challenge than static images or text. It requires interpreting fine-grained visual information, accurately reading handwritten or digital text, and integrating spoken cues, often dispersed non-linearly over time. In such m…

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2025

A Culturally-diverse Multilingual Multimodal Video Benchmark & Model

Bhuiyan Sanjid Shafique, Ashmal Vayani, Muhammad Maaz, Hanoona Abdul Rasheed, Dinura Dissanayake, Mohammed Irfan Kurpath +23
EMNLP 2025

Large multimodal models (LMMs) have recently gained attention due to their effectiveness to understand and generate descriptions of visual content. Most existing LMMs are in English language. While few recent works explore multilingual image LMMs, to the best of our knowledge, moving beyond the Engl

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2025

Perception Encoder: The best visual embeddings are not at the output of the network

Daniel Bolya, Po-Yao Huang, Peize Sun, Jang Hyun Cho, Andrea Madotto, Chen Wei +12
NeurIPS 2025oral

We introduce Perception Encoder (PE), a family of state-of-the-art vision encoders for image and video understanding. Traditionally, vision encoders have relied on a variety of pretraining objectives, each excelling at different downstream tasks. Surprisingly, after scaling a carefully tuned image p…

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2025

PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding

Jang Hyun Cho, Andrea Madotto, Effrosyni Mavroudi, Triantafyllos Afouras, Tushar Nagarajan, Muhammad Maaz +23
NeurIPS 2025spotlight

Vision-language models are integral to computer vision research, yet many high-performing models remain closed-source, obscuring their data, design and training recipe. The research community has responded by using distillation from black-box models to label training data, achieving strong benchmark…

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2022

Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection

Hanoona Abdul Rasheed, Muhammad Maaz, Muhammd Uzair Khattak, Salman Khan, Fahad Khan
NeurIPS 2022accept

Existing open-vocabulary object detectors typically enlarge their vocabulary sizes by leveraging different forms of weak supervision. This helps generalize to novel objects at inference. Two popular forms of weak-supervision used in open-vocabulary detection (OVD) include pretrained CLIP model and i…

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