Santhosh K. Ramakrishnan

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dblp:199/1913 · also Santhosh Kumar Ramakrishnan · DBLP profile ↗

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20ranked-venue papers

9as first author

17since 2021 · last 2025

0000-0003-2833-7038ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 20 · 9 first-author · 17 since 2021Graphics, computer vision, multimedia, augmented reality and games · 9 · 5 first-author · 6 since 2021

Year	Publication	Venue	Position
2025	Does Spatial Cognition Emerge in Frontier Models? abstract Not yet. We present SPACE, a benchmark that systematically evaluates spatial cognition in frontier models. Our benchmark builds on decades of research in cognitive science. It evaluates large-scale mapping abilities that are brought to bear when an organism traverses physical environments, smaller-scale reasoning about object shapes and layouts, and cognitive infrastructure such as spatial attention and memory. For many tasks, we instantiate parallel presentations via text and images, allowing us to benchmark both large language models and large multimodal models. Results suggest that contemporary frontier models fall short of the spatial intelligence of animals, performing near chance level on a number of classic tests of animal cognition. Santhosh K. Ramakrishnan, Erik Wijmans, Philipp Krähenbühl, Vladlen Koltun	ICLR	1
2025	Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives abstract We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts, yielding long-form captures from 1 to 42 minutes each and 1,286 hours of video combined. The multimodal nature of the dataset is unprecedented: the video is accompanied by multichannel audio, eye gaze, 3D point clouds, camera poses, IMU, and multiple paired language descriptions—including a novel “expert commentary” done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity, we also present a suite of benchmark tasks and their annotations, including fine-grained activity understanding, proficiency estimation, cross-view translation, and 3D hand/body pose. All resources are open sourced to fuel new research in the community. https://ego-exo4d-data.org/ Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Makoto Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zachary Chavis, Joya Chen, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong 0002, María Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang 0020, Md Mohaiminul Islam, Suyog Dutt Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J. Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh K. Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina González, Prince Gupta, Jiabo Hu, Yifei Huang 0002, Yiming Huang 0011, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu 0007, Mi Luo, Zhengyi Luo 0002, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Kiran K. Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao 0002, Ziwei Zhao 0003, Zhifan Zhu 0001, Jeff Zhuo, Pablo Andrés Arbeláez, Gedas Bertasius, David Crandall, Dima Damen, Jakob J. Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C. V. Jawahar, Richard A. Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato 0001, Manolis Savva, Jianbo Shi, Mike Zheng Shout, Michael Wray	Int. J. Comput. Vis.	36
2025	Ego4D: Around the World in 3,600 Hours of Egocentric Video abstract We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards, with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Kristen Grauman, Andrew Westbury, Eugene Byrne, Vincent Cartillier, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang 0007, Devansh Kukreja, Miao Liu 0007, Xingyu Liu 0001, Tushar Nagarajan, Ilija Radosavovic, Santhosh K. Ramakrishnan, Fiona Ryan, Jayant Sharma 0002, Michael Wray, Mengmeng Xu 0006, Eric Zhongcong Xu, Chen Zhao 0002, Siddhant Bansal, Dhruv Batra, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina González, James Hillis, Xuhua Huang, Yifei Huang 0002, Wenqi Jia 0001, Weslie Khoo, Jáchym Kolár, Satwik Kottur, Anurag Kumar 0003, Federico Landini, Yanghao Li, Zhenqiang Li 0002, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran K. Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Xindi Wu, Takuma Yagi, Ziwei Zhao 0003, Yunyi Zhu, Pablo Andrés Arbeláez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fügen, Bernard Ghanem, Vamsi K. Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Makoto Kitani, Haizhou Li 0001, Richard A. Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato 0001, Jianbo Shi, Zheng Shou 0001, Antonio Torralba 0001, Lorenzo Torresani, Mingfei Yan, Jitendra Malik	IEEE Trans. Pattern Anal. Mach. Intell.	16
2024	Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives abstract We present Ego-Exo4D, a diverse, large-scale multi-modal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured ego-centric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts, yielding long-form captures from 1 to 42 minutes each and 1,286 hours of video combined. The multimodal nature of the dataset is un-precedented: the video is accompanied by multichannel audio, eye gaze, 3D point clouds, camera poses, IMU, and multiple paired language descriptions-including a novel “expert commentary” done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity, we also present a suite of benchmark tasks and their annotations, including fine-grained activity understanding, proficiency estimation, cross-view translation, and 3D hand/body pose. All resources are open sourced to fuel new research in the community. Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Makoto Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zachary Chavis, Joya Chen, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong 0002, María Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang 0020, Md Mohaiminul Islam, Suyog Dutt Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J. Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh K. Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina González, Prince Gupta, Jiabo Hu, Yifei Huang 0002, Yiming Huang 0011, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu 0007, Mi Luo, Zhengyi Luo 0002, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Kiran K. Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao 0002, Ziwei Zhao 0003, Zhifan Zhu 0001, Jeff Zhuo, Pablo Andrés Arbeláez, Gedas Bertasius, Dima Damen, Jakob J. Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C. V. Jawahar, Richard A. Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato 0001, Manolis Savva, Jianbo Shi, Mike Zheng Shout, Michael Wray	CVPR	36
2023	NaQ: Leveraging Narrations as Queries to Supervise Episodic Memory abstract Searching long egocentric videos with natural language queries (NLQ) has compelling applications in augmented reality and robotics, where a fluid index into everything that a person (agent) has seen before could augment human memory and surface relevant information on demand. However, the structured nature of the learning problem (free-form text query inputs, localized video temporal window outputs) and its needle-in-a-haystack nature makes it both technically challenging and expensive to supervise. We introduce Narrations-as-Queries (NaQ), a data augmentation strategy that transforms standard video-text narrations into training data for a video query localization model. Validating our idea on the Ego4D benchmark, we find it has tremendous impact in practice. NaQ improves multiple top models by substantial margins (even doubling their accuracy), and yields the very best results to date on the Ego4D NLQ challenge, soundly outperforming all challenge winners in the CVPR and ECCV 2022 competitions and topping the current public leaderboard. Beyond achieving the state-of-the-art for NLQ, we also demonstrate unique properties of our approach such as the ability to perform zero-shot and fewshot NLQ, and improved performance on queries about long-tail object categories. Code and models: http://vision.cs.utexas.edu/projects/naq. Santhosh K. Ramakrishnan, Ziad Al-Halah, Kristen Grauman	CVPR	1
2023	Habitat-Matterport 3D Semantics Dataset abstract We present the Habitat-Matterport 3D Semantics (HM3DSem) dataset. HM3DSem is the largest dataset of 3D real-world spaces with densely annotated semantics that is currently available to the academic community. It consists of 142,646 object instance annotations across 216 3D spaces and 3,100 rooms within those spaces. The scale, quality, and diversity of object annotations far exceed those of prior datasets. A key difference setting apart HM3DSem from other datasets is the use of texture information to annotate pixel-accurate object boundaries. We demonstrate the effectiveness of HM3DSem dataset for the Object Goal Navigation task using different methods. Policies trained using HM3DSem perform outperform those trained on prior datasets. Introduction of HM3DSem in the Habitat ObjectNav Challenge lead to an increase in participation from 400 submissions in 2021 to 1022 submissions in 2022. Project page: https://aihabitat.org/datasets/hm3d-semantics/ Karmesh Yadav, Ram Ramrakhya, Santhosh K. Ramakrishnan, Théophile Gervet, John M. Turner, Aaron Gokaslan, Noah Maestre, Angel X. Chang, Dhruv Batra, Manolis Savva, Alexander Clegg, Devendra Singh Chaplot	CVPR	3
2023	SpotEM: Efficient Video Search for Episodic Memory abstract The goal in episodic memory (EM) is to search a long egocentric video to answer a natural language query (e.g., “where did I leave my purse?”). Existing EM methods exhaustively extract expensive fixed-length clip features to look everywhere in the video for the answer, which is infeasible for long wearable-camera videos that span hours or even days. We propose SpotEM, an approach to achieve efficiency for a given EM method while maintaining good accuracy. SpotEM consists of three key ideas: 1) a novel clip selector that learns to identify promising video regions to search conditioned on the language query; 2) a set of low-cost semantic indexing features that capture the context of rooms, objects, and interactions that suggest where to look; and 3) distillation losses that address the optimization issues arising from end-to-end joint training of the clip selector and EM model. Our experiments on 200+ hours of video from the Ego4D EM Natural Language Queries benchmark and three different EM models demonstrate the effectiveness of our approach: computing only 10% – 25% of the clip features, we preserve 84% – 97% of the original EM model’s accuracy. Project page: https://vision.cs.utexas.edu/projects/spotem Santhosh K. Ramakrishnan, Ziad Al-Halah, Kristen Grauman	ICML	1
2023	Video-Mined Task Graphs for Keystep Recognition in Instructional Videos abstract Procedural activity understanding requires perceiving human actions in terms of a broader task, where multiple keysteps are performed in sequence across a long video to reach a final goal state---such as the steps of a recipe or the steps of a DIY fix-it task. Prior work largely treats keystep recognition in isolation of this broader structure, or else rigidly confines keysteps to align with a particular sequential script. We propose discovering a task graph automatically from how-to videos to represent probabilistically how people tend to execute keysteps, then leverage this graph to regularize keystep recognition in novel videos. On multiple datasets of real-world instructional video, we show the impact: more reliable zero-shot keystep localization and improved video representation learning, exceeding the state of the art. Kumar Ashutosh, Santhosh K. Ramakrishnan, Triantafyllos Afouras, Kristen Grauman	NeurIPS	2
2023	Single-Stage Visual Query Localization in Egocentric Videos abstract Visual Query Localization on long-form egocentric videos requires spatio-temporal search and localization of visually specified objects and is vital to build episodic memory systems. Prior work develops complex multi-stage pipelines that leverage well-established object detection and tracking methods to perform VQL. However, each stage is independently trained and the complexity of the pipeline results in slow inference speeds. We propose VQLoC, a novel single-stage VQL framework that is end-to-end trainable. Our key idea is to first build a holistic understanding of the query-video relationship and then perform spatio-temporal localization in a single shot manner. Specifically, we establish the query-video relationship by jointly considering query-to-frame correspondences between the query and each video frame and frame-to-frame correspondences between nearby video frames. Our experiments demonstrate that our approach outperforms prior VQL methods by $20$% accuracy while obtaining a $10\times$ improvement in inference speed. VQLoC is also the top entry on the Ego4D VQ2D challenge leaderboard. Hanwen Jiang, Santhosh K. Ramakrishnan, Kristen Grauman	NeurIPS	2
2023	EgoEnv: Human-centric environment representations from egocentric video abstract First-person video highlights a camera-wearer's activities in the context of their persistent environment. However, current video understanding approaches reason over visual features from short video clips that are detached from the underlying physical space and capture only what is immediately visible. To facilitate human-centric environment understanding, we present an approach that links egocentric video and the environment by learning representations that are predictive of the camera-wearer's (potentially unseen) local surroundings. We train such models using videos from agents in simulated 3D environments where the environment is fully observable, and test them on human-captured real-world videos from unseen environments. On two human-centric video tasks, we show that models equipped with our environment-aware features consistently outperform their counterparts with traditional clip features. Moreover, despite being trained exclusively on simulated videos, our approach successfully handles real-world videos from HouseTours and Ego4D, and achieves state-of-the-art results on the Ego4D NLQ challenge. Tushar Nagarajan, Santhosh K. Ramakrishnan, Ruta Desai, James Hillis, Kristen Grauman	NeurIPS	2
2023	A domain-agnostic approach for characterization of lifelong learning systems Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Benjamin, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary A. Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Dimitri Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh K. Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone 0001, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha	Neural Networks	34
2022	Zero Experience Required: Plug & Play Modular Transfer Learning for Semantic Visual Navigation abstract In reinforcement learning for visual navigation, it is common to develop a model for each new task, and train that model from scratch with task-specific interactions in 3D environments. However, this process is expensive; mas-sive amounts of interactions are needed for the model to generalize well. Moreover, this process is repeated when-ever there is a change in the task type or the goal modality. We present a unified approach to visual navigation using a novel modular transfer learning model. Our model can ef-fectively leverage its experience from one source task and apply it to multiple target tasks (e.g., ObjectNav, Room-Nav, Vi ewNav) with various goal modalities (e.g., image, sketch, audio, label). Furthermore, our model enables zero-shot experience learning, whereby it can solve the target tasks without receiving any task-specific interactive training. Our experiments on multiple photorealistic datasets and challenging tasks show that our approach learns faster, generalizes better, and outperforms SoTA models by a sig-nificant margin. Project page: https://vision.cs.utexas.edu/projects/zsel/ Ziad Al-Halah, Santhosh K. Ramakrishnan, Kristen Grauman	CVPR	2
2022	Ego4D: Around the World in 3, 000 Hours of Egocentric Video abstract We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of dailylife activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards, with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/ Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang 0007, Miao Liu 0007, Xingyu Liu 0001, Tushar Nagarajan, Ilija Radosavovic, Santhosh K. Ramakrishnan, Fiona Ryan, Jayant Sharma 0002, Michael Wray, Mengmeng Xu 0006, Eric Zhongcong Xu, Chen Zhao 0002, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina González, James Hillis, Xuhua Huang, Yifei Huang 0002, Wenqi Jia 0001, Weslie Khoo, Jáchym Kolár, Satwik Kottur, Anurag Kumar 0003, Federico Landini, Yanghao Li, Zhenqiang Li 0002, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran K. Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Xindi Wu, Takuma Yagi, Ziwei Zhao 0003, Yunyi Zhu, Pablo Andrés Arbeláez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fügen, Bernard Ghanem, Vamsi K. Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Makoto Kitani, Haizhou Li 0001, Richard A. Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato 0001, Jianbo Shi, Zheng Shou 0001, Antonio Torralba 0001, Lorenzo Torresani, Mingfei Yan, Jitendra Malik	CVPR	14
2022	PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learning abstract State-of-the-art approaches to ObjectGoal navigation (ObjectNav) rely on reinforcement learning and typically require significant computational resources and time for learning. We propose Potential functions for ObjectGoal Navigation with Interaction-free learning (PONI), a modular approach that disentangles the skills of 'where to look?’ for an object and 'how to navigate to$(x,\ y)$?’. Our key insight is that 'where to look?’ can be treated purely as a perception problem, and learned without environment interactions. To address this, we propose a network that predicts two complementary potential functions conditioned on a semantic map and uses them to decide where to look for an unseen object. We train the potential function network using supervised learning on a passive dataset of top-down semantic maps, and integrate it into a modular framework to perform ObjectNav. Experiments on Gibson and Matterport3D demonstrate that our method achieves the stateof-the-art for ObjectNav while incurring up to$1,600\times less$computational cost for training. Code and pre-trained models are available.11Website: https://vision.cs.utexas.edu/projects/poni/ Santhosh K. Ramakrishnan, Devendra Singh Chaplot, Ziad Al-Halah, Jitendra Malik, Kristen Grauman	CVPR	1
2022	Environment Predictive Coding for Visual Navigation Santhosh K. Ramakrishnan, Tushar Nagarajan, Ziad Al-Halah, Kristen Grauman	ICLR	1
2021	Learning to Set Waypoints for Audio-Visual Navigation Changan Chen, Sagnik Majumder, Ziad Al-Halah, Ruohan Gao, Santhosh K. Ramakrishnan, Kristen Grauman	ICLR	5
2021	An Exploration of Embodied Visual Exploration Santhosh K. Ramakrishnan, Dinesh Jayaraman, Kristen Grauman	Int. J. Comput. Vis.	1
2020	Occupancy Anticipation for Efficient Exploration and Navigation Santhosh K. Ramakrishnan, Ziad Al-Halah, Kristen Grauman	ECCV (5)	1
2018	Sidekick Policy Learning for Active Visual Exploration Santhosh K. Ramakrishnan, Kristen Grauman	ECCV (12)	1
2017	An Empirical Evaluation of Visual Question Answering for Novel Objects abstract We study the problem of answering questions about images in the harder setting, where the test questions and corresponding images contain novel objects, which were not queried about in the training data. Such setting is inevitable in real world–owing to the heavy tailed distribution of the visual categories, there would be some objects which would not be annotated in the train set. We show that the performance of two popular existing methods drop significantly (21–28%) when evaluated on novel objects cf. known objects. We propose methods which use large existing external corpora of (i) unlabeled text, i.e. books, and (ii) images tagged with classes, to achieve novel object based visual question answering. We systematically study both, an oracle case where the novel objects are known textually, as well as a fully automatic case without any explicit knowledge of the novel objects, but with the minimal assumption that the novel objects are semantically related to the existing objects in training. The proposed methods for novel object based visual question answering are modular and can potentially be used with many visual question answering architectures. We show consistent improvements with the two popular architectures and give qualitative analysis of the cases where the model does well and of those where it fails to bring improvements. Santhosh K. Ramakrishnan, Ambar Pal, Anurag Mittal	CVPR	1