Kristen Grauman

NeurIPS Conference 2025 Conference Paper

Learning Skill-Attributes for Transferable Assessment in Video

• Kumar Ashutosh
• Kristen Grauman

Skill assessment from video entails rating the quality of a person’s physical performance and explaining what could be done better. Today’s models specialize for an individual sport, and suffer from the high cost and scarcity of expert-level supervision across the long tail of sports. Towards closing that gap, we explore transferable video representations for skill assessment. Our CrossTrainer approach discovers skill-attributes—such as balance, control, and hand positioning—whose meaning transcends the boundaries of any given sport, then trains a multimodal language model to generate actionable feedback for a novel video, e. g. , “lift hands more to generate more power” as well as its proficiency level, e. g. , early expert. We validate the new model on multiple datasets for both cross-sport (transfer) and intra-sport (in-domain) settings, where it achieves gains up to 60% relative to the state of the art. By abstracting out the shared behaviors indicative of human skill, the proposed video representation generalizes substantially better than an array of existing techniques, enriching today’s multimodal large language models.

NeurIPS Conference 2025 Conference Paper

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

• Jang Hyun Cho
• Andrea Madotto
• Effrosyni Mavroudi
• Triantafyllos Afouras
• Tushar Nagarajan
• Muhammad Maaz
• Yale Song
• Tengyu Ma

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 results, at the cost of measurable scientific progress. However, without knowing the details of the teacher model and its data sources, scientific progress remains difficult to measure. In this paper, we study building a Perception Language Model (PLM) in a fully open and reproducible framework for transparent research in image and video understanding. We analyze standard training pipelines without distillation from proprietary models and explore large-scale synthetic data to identify critical data gaps, particularly in detailed video understanding. To bridge these gaps, we release 2. 8M human-labeled instances of fine-grained video question-answer pairs and spatio-temporally grounded video captions. Additionally, we introduce PLM–VideoBench, a suite for evaluating challenging video understanding tasks focusing on the ability to reason about ''what'', ''where'', ''when'', and ''how'' of a video. We make our work fully reproducible by providing data, training recipes, code & models.

NeurIPS Conference 2025 Conference Paper

Seeing the Arrow of Time in Large Multimodal Models

• Zihui (Sherry) Xue
• Romy Luo
• Kristen Grauman

The Arrow of Time (AoT)—time's irreversible flow shaping physical events—is fundamental to video comprehension, yet remains a significant challenge for modern large multimodal models (LMMs). Current LMMs struggle to perceive and utilize temporal directionality in video when responding to language queries, obstructing deeper temporal understanding. We tackle this deficiency by first providing a critical analysis of existing benchmarks and models. We then introduce ArrowRL, a reinforcement learning (RL)-based training strategy with an innovative reverse reward that instills AoT awareness by encouraging divergent video interpretations between forward and reversed visual frames. For rigorous evaluation, we additionally develop AoTBench, a new multi-faceted benchmark probing temporally challenging questions. Experiments show ArrowRL greatly advances temporal perception: it not only achieves substantial improvements on our challenging AoTBench but also demonstrably boosts performance on standard video question answering (VQA) benchmarks (with peak accuracy gains reaching over 20% and 10% respectively). This validates ArrowRL's effectiveness and highlights the critical need for dedicated AoT understanding in LMMs.

NeurIPS Conference 2025 Conference Paper

When Thinking Drifts: Evidential Grounding for Robust Video Reasoning

• Romy Luo
• Zihui (Sherry) Xue
• Alex Dimakis
• Kristen Grauman

Video reasoning, the task of enabling machines to infer from dynamic visual content through multi-step logic, is crucial for advanced AI. While the Chain-of-Thought (CoT) mechanism has enhanced reasoning in text-based tasks, its application to video understanding remains underexplored. This paper presents a systematic analysis revealing that CoT often degrades performance in video reasoning, generating verbose but misleading internal monologues, and leading to hallucinated visual details and overridden correct intuitions—a phenomenon we term "visual thinking drift. " We explain this drift through a Bayesian lens, positing that CoT traces often diverge from actual visual evidence, instead amplifying internal biases or language priors, causing models to storytell rather than engage in grounded reasoning. To counteract this, we introduce Visual Evidence Reward (VER), a novel reinforcement learning framework that explicitly rewards the generation of reasoning traces that are verifiably grounded in visual evidence. Comprehensive evaluation across 10 diverse video understanding benchmarks demonstrates that our Video-VER model consistently achieves top performance. Our work sheds light on the distinct challenges of video-centric reasoning and encourages the development of AI that robustly grounds its inferences in visual evidence---for large multimodal models that not only "think before answering", but also "see while thinking".

IROS Conference 2024 Conference Paper

ActiveRIR: Active Audio-Visual Exploration for Acoustic Environment Modeling

• Arjun Somayazulu
• Sagnik Majumder
• Changan Chen
• Kristen Grauman

An environment acoustic model represents how sound is transformed by the physical characteristics of an indoor environment, for any given source/receiver location. Traditional methods for constructing acoustic models involve expensive and time-consuming collection of large quantities of acoustic data at dense spatial locations in the space, or rely on privileged knowledge of scene geometry to intelligently select acoustic data sampling locations. We propose active acoustic sampling, a new task for efficiently building an environment acoustic model of an unmapped environment in which a mobile agent equipped with visual and acoustic sensors jointly constructs the environment acoustic model and the occupancy map on-the-fly. We introduce ActiveRIR, a reinforcement learning (RL) policy that leverages information from audio-visual sensor streams to guide agent navigation and determine optimal acoustic data sampling positions, yielding a high quality acoustic model of the environment from a minimal set of acoustic samples. We train our policy with a novel RL reward based on information gain in the environment acoustic model. Evaluating on diverse unseen indoor environments from a state-of-the-art acoustic simulation platform, ActiveRIR outperforms an array of methods—both traditional navigation agents based on spatial novelty and visual exploration as well as existing state-of-the-art methods.

NeurIPS Conference 2024 Conference Paper

HOI-Swap: Swapping Objects in Videos with Hand-Object Interaction Awareness

• Zihui Xue
• Mi Luo
• Changan Chen
• Kristen Grauman

We study the problem of precisely swapping objects in videos, with a focus on those interacted with by hands, given one user-provided reference object image. Despite the great advancements that diffusion models have made in video editing recently, these models often fall short in handling the intricacies of hand-object interactions (HOI), failing to produce realistic edits---especially when object swapping results in object shape or functionality changes. To bridge this gap, we present HOI-Swap, a novel diffusion-based video editing framework trained in a self-supervised manner. Designed in two stages, the first stage focuses on object swapping in a single frame with HOI awareness; the model learns to adjust the interaction patterns, such as the hand grasp, based on changes in the object's properties. The second stage extends the single-frame edit across the entire sequence; we achieve controllable motion alignment with the original video by: (1) warping a new sequence from the stage-I edited frame based on sampled motion points and (2) conditioning video generation on the warped sequence. Comprehensive qualitative and quantitative evaluations demonstrate that HOI-Swap significantly outperforms existing methods, delivering high-quality video edits with realistic HOIs.

IROS Conference 2024 Conference Paper

Sim2Real Transfer for Audio-Visual Navigation with Frequency-Adaptive Acoustic Field Prediction

• Changan Chen
• Jordi Ramos
• Anshul Tomar
• Kristen Grauman

Sim2real transfer has received increasing attention lately due to its success in transferring robotic policies learned in simulation to the real world. While significant progress has been made in transferring vision-based navigation policies, the current sim2real strategy for audio-visual navigation remains limited to basic data augmentation. Sound differs from light in that it spans across much wider frequencies and thus requires a different solution for sim2real. To understand how the acoustic sim2real gap varies with frequencies, we first define a novel acoustic field prediction (AFP) task that predicts the local sound pressure field. We then train frequency-specific AFP models in simulation and measure the prediction errors on collected real data. We propose a frequency-adaptive strategy that intelligently selects the best frequency band for prediction based on both the measured prior and the energy distribution of the received audio, which improves the generalization on real data. Coupled with waypoint navigation, we show the navigation policy not only improves navigation performance in simulation but also transfers successfully to real robots. This work demonstrates the potential of building autonomous agents that can see, hear, and act entirely from simulation, and transferring them to the real world.

NeurIPS Conference 2023 Conference Paper

EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understanding

• Shuhan Tan
• Tushar Nagarajan
• Kristen Grauman

Recent advances in egocentric video understanding models are promising, but their heavy computational expense is a barrier for many real-world applications. To address this challenge, we propose EgoDistill, a distillation-based approach that learns to reconstruct heavy ego-centric video clip features by combining the semantics from a sparse set of video frames with head motion from lightweight IMU readings. We further devise a novel IMU-based self-supervised pretraining strategy. Our method leads to significant improvements in efficiency, requiring 200× fewer GFLOPs than equivalent video models. We demonstrate its effectiveness on the Ego4D and EPIC- Kitchens datasets, where our method outperforms state-of-the-art efficient video understanding methods.

NeurIPS Conference 2023 Conference Paper

EgoEnv: Human-centric environment representations from egocentric video

• Tushar Nagarajan
• Santhosh Kumar Ramakrishnan
• Ruta Desai
• James Hillis
• Kristen Grauman

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.

NeurIPS Conference 2023 Conference Paper

EgoTracks: A Long-term Egocentric Visual Object Tracking Dataset

• Hao Tang
• Kevin J Liang
• Kristen Grauman
• Matt Feiszli
• Weiyao Wang

Visual object tracking is a key component to many egocentric vision problems. However, the full spectrum of challenges of egocentric tracking faced by an embodied AI is underrepresented in many existing datasets; these tend to focus on relatively short, third-person videos. Egocentric video has several distinguishing characteristics from those commonly found in past datasets: frequent large camera motions and hand interactions with objects commonly lead to occlusions or objects exiting the frame, and object appearance can change rapidly due to widely different points of view, scale, or object states. Embodied tracking is also naturally long-term, and being able to consistently (re-)associate objects to their appearances and disappearances over as long as a lifetime is critical. Previous datasets under-emphasize this re-detection problem, and their "framed" nature has led to adoption of various spatiotemporal priors that we find do not necessarily generalize to egocentric video. We thus introduce EgoTracks, a new dataset for long-term egocentric visual object tracking. Sourced from the Ego4D dataset, this new dataset presents a significant challenge to recent state-of-the-art single-object tracking models, which we find score poorly on traditional tracking metrics for our new dataset, compared to popular benchmarks. We further show improvements that can be made to a STARK tracker to significantly increase its performance on egocentric data, resulting in a baseline model we call EgoSTARK. We publicly release our annotations and benchmark, hoping our dataset leads to further advancements in tracking.

NeurIPS Conference 2023 Conference Paper

Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment

• Zihui (Sherry) Xue
• Kristen Grauman

The egocentric and exocentric viewpoints of a human activity look dramatically different, yet invariant representations to link them are essential for many potential applications in robotics and augmented reality. Prior work is limited to learning view-invariant features from paired synchronized viewpoints. We relax that strong data assumption and propose to learn fine-grained action features that are invariant to the viewpoints by aligning egocentric and exocentric videos in time, even when not captured simultaneously or in the same environment. To this end, we propose AE2, a self-supervised embedding approach with two key designs: (1) an object-centric encoder that explicitly focuses on regions corresponding to hands and active objects; (2) a contrastive-based alignment objective that leverages temporally reversed frames as negative samples. For evaluation, we establish a benchmark for fine-grained video understanding in the ego-exo context, comprising four datasets---including an ego tennis forehand dataset we collected, along with dense per-frame labels we annotated for each dataset. On the four datasets, our AE2 method strongly outperforms prior work in a variety of fine-grained downstream tasks, both in regular and cross-view settings.

IROS Conference 2023 Conference Paper

Learning to Map Efficiently by Active Echolocation

• Xixi Hu 0001
• Senthil Purushwalkam
• David Harwath
• Kristen Grauman

Using visual SLAM to map new environments requires time-consuming visits to all regions for data collection. We propose an approach to estimate maps of areas beyond the visible regions using a cheap and readily available modality of data-sound. We introduce the idea of an active audio-visual mapping agent. Besides collecting visual data, the proposed agent emits sounds during navigation, captures the echoes, and uses them to accurately map unknown areas. We propose a reinforcement learning based method that simultaneously trains models to 1) estimate a map from the visual data, 2) output navigation actions, 3) output the decision to emit a sound and 4) refine estimated mans using the cantured audio. Our agent is trained and tested on 85 real-world homes from the Matterport3D dataset using the Habitat and SoundSpaces simulators for visual and audio data. Our method, unlike visual-data reliant approaches, yields more accurate maps with broader environmental coverage. In addition, compared to an agent that continually emits sounds, we observe that intelligently choosing when to emit sounds leads to accurate maps obatined with greater efficiency.

NeurIPS Conference 2023 Conference Paper

Self-Supervised Visual Acoustic Matching

• Arjun Somayazulu
• Changan Chen
• Kristen Grauman

Acoustic matching aims to re-synthesize an audio clip to sound as if it were recorded in a target acoustic environment. Existing methods assume access to paired training data, where the audio is observed in both source and target environments, but this limits the diversity of training data or requires the use of simulated data or heuristics to create paired samples. We propose a self-supervised approach to visual acoustic matching where training samples include only the target scene image and audio---without acoustically mismatched source audio for reference. Our approach jointly learns to disentangle room acoustics and re-synthesize audio into the target environment, via a conditional GAN framework and a novel metric that quantifies the level of residual acoustic information in the de-biased audio. Training with either in-the-wild web data or simulated data, we demonstrate it outperforms the state-of-the-art on multiple challenging datasets and a wide variety of real-world audio and environments.

NeurIPS Conference 2023 Conference Paper

Single-Stage Visual Query Localization in Egocentric Videos

• Hanwen Jiang
• Santhosh Kumar Ramakrishnan
• Kristen Grauman

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.

ICML Conference 2023 Conference Paper

SpotEM: Efficient Video Search for Episodic Memory

• Santhosh Kumar Ramakrishnan
• Ziad Al-Halah
• Kristen Grauman

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

NeurIPS Conference 2023 Conference Paper

Video-Mined Task Graphs for Keystep Recognition in Instructional Videos

• Kumar Ashutosh
• Santhosh Kumar Ramakrishnan
• Triantafyllos Afouras
• Kristen Grauman

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.

ICLR Conference 2022 Conference Paper

Environment Predictive Coding for Visual Navigation

• Santhosh Kumar Ramakrishnan
• Tushar Nagarajan
• Ziad Al-Halah
• Kristen Grauman

We introduce environment predictive coding, a self-supervised approach to learn environment-level representations for embodied agents. In contrast to prior work on self-supervised learning for individual images, we aim to encode a 3D environment using a series of images observed by an agent moving in it. We learn these representations via a masked-zone prediction task, which segments an agent’s trajectory into zones and then predicts features of randomly masked zones, conditioned on the agent’s camera poses. This explicit spatial conditioning encourages learning representations that capture the geometric and semantic regularities of 3D environments. We learn such representations on a collection of video walkthroughs and demonstrate successful transfer to multiple downstream navigation tasks. Our experiments on the real-world scanned 3D environments of Gibson and Matterport3D show that our method obtains 2 - 6× higher sample-efficiency and up to 57% higher performance over standard image-representation learning.

NeurIPS Conference 2022 Conference Paper

Few-Shot Audio-Visual Learning of Environment Acoustics

• Sagnik Majumder
• Changan Chen
• Ziad Al-Halah
• Kristen Grauman

Room impulse response (RIR) functions capture how the surrounding physical environment transforms the sounds heard by a listener, with implications for various applications in AR, VR, and robotics. Whereas traditional methods to estimate RIRs assume dense geometry and/or sound measurements throughout the environment, we explore how to infer RIRs based on a sparse set of images and echoes observed in the space. Towards that goal, we introduce a transformer-based method that uses self-attention to build a rich acoustic context, then predicts RIRs of arbitrary query source-receiver locations through cross-attention. Additionally, we design a novel training objective that improves the match in the acoustic signature between the RIR predictions and the targets. In experiments using a state-of-the-art audio-visual simulator for 3D environments, we demonstrate that our method successfully generates arbitrary RIRs, outperforming state-of-the-art methods and---in a major departure from traditional methods---generalizing to novel environments in a few-shot manner. Project: http: //vision. cs. utexas. edu/projects/fs_rir

NeurIPS Conference 2022 Conference Paper

SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning

• Changan Chen
• Carl Schissler
• Sanchit Garg
• Philip Kobernik
• Alexander Clegg
• Paul Calamia
• Dhruv Batra
• Philip Robinson

We introduce SoundSpaces 2. 0, a platform for on-the-fly geometry-based audio rendering for 3D environments. Given a 3D mesh of a real-world environment, SoundSpaces can generate highly realistic acoustics for arbitrary sounds captured from arbitrary microphone locations. Together with existing 3D visual assets, it supports an array of audio-visual research tasks, such as audio-visual navigation, mapping, source localization and separation, and acoustic matching. Compared to existing resources, SoundSpaces 2. 0 has the advantages of allowing continuous spatial sampling, generalization to novel environments, and configurable microphone and material properties. To our knowledge, this is the first geometry-based acoustic simulation that offers high fidelity and realism while also being fast enough to use for embodied learning. We showcase the simulator's properties and benchmark its performance against real-world audio measurements. In addition, we demonstrate two downstream tasks---embodied navigation and far-field automatic speech recognition---and highlight sim2real performance for the latter. SoundSpaces 2. 0 is publicly available to facilitate wider research for perceptual systems that can both see and hear.

ICRA Conference 2021 Conference Paper

Learning Dexterous Grasping with Object-Centric Visual Affordances

• Priyanka Mandikal
• Kristen Grauman

Dexterous robotic hands are appealing for their agility and human-like morphology, yet their high degree of freedom makes learning to manipulate challenging. We introduce an approach for learning dexterous grasping. Our key idea is to embed an object-centric visual affordance model within a deep reinforcement learning loop to learn grasping policies that favor the same object regions favored by people. Unlike traditional approaches that learn from human demonstration trajectories (e. g. , hand joint sequences captured with a glove), the proposed prior is object-centric and image-based, allowing the agent to anticipate useful affordance regions for objects unseen during policy learning. We demonstrate our idea with a 30-DoF five-fingered robotic hand simulator on 40 objects from two datasets, where it successfully and efficiently learns policies for stable functional grasps. Our affordance-guided policies are significantly more effective, generalize better to novel objects, train 3× faster than the baselines, and are more robust to noisy sensor readings and actuation. Our work offers a step towards manipulation agents that learn by watching how people use objects, without requiring state and action information about the human body. Project website with videos: http://vision.cs.utexas.edu/projects/graff-dexterous-affordance-grasp.

ICLR Conference 2021 Conference Paper

Learning to Set Waypoints for Audio-Visual Navigation

• Changan Chen
• Sagnik Majumder
• Ziad Al-Halah
• Ruohan Gao
• Santhosh Kumar Ramakrishnan
• Kristen Grauman

In audio-visual navigation, an agent intelligently travels through a complex, unmapped 3D environment using both sights and sounds to find a sound source (e.g., a phone ringing in another room). Existing models learn to act at a fixed granularity of agent motion and rely on simple recurrent aggregations of the audio observations. We introduce a reinforcement learning approach to audio-visual navigation with two key novel elements: 1) waypoints that are dynamically set and learned end-to-end within the navigation policy, and 2) an acoustic memory that provides a structured, spatially grounded record of what the agent has heard as it moves. Both new ideas capitalize on the synergy of audio and visual data for revealing the geometry of an unmapped space. We demonstrate our approach on two challenging datasets of real-world 3D scenes, Replica and Matterport3D. Our model improves the state of the art by a substantial margin, and our experiments reveal that learning the links between sights, sounds, and space is essential for audio-visual navigation.

NeurIPS Conference 2021 Conference Paper

Shaping embodied agent behavior with activity-context priors from egocentric video

• Tushar Nagarajan
• Kristen Grauman

Complex physical tasks entail a sequence of object interactions, each with its own preconditions -- which can be difficult for robotic agents to learn efficiently solely through their own experience. We introduce an approach to discover activity-context priors from in-the-wild egocentric video captured with human worn cameras. For a given object, an activity-context prior represents the set of other compatible objects that are required for activities to succeed (e. g. , a knife and cutting board brought together with a tomato are conducive to cutting). We encode our video-based prior as an auxiliary reward function that encourages an agent to bring compatible objects together before attempting an interaction. In this way, our model translates everyday human experience into embodied agent skills. We demonstrate our idea using egocentric EPIC-Kitchens video of people performing unscripted kitchen activities to benefit virtual household robotic agents performing various complex tasks in AI2-iTHOR, significantly accelerating agent learning.

NeurIPS Conference 2020 Conference Paper

Learning Affordance Landscapes for Interaction Exploration in 3D Environments

• Tushar Nagarajan
• Kristen Grauman

Embodied agents operating in human spaces must be able to master how their environment works: what objects can the agent use, and how can it use them? We introduce a reinforcement learning approach for exploration for interaction, whereby an embodied agent autonomously discovers the affordance landscape of a new unmapped 3D environment (such as an unfamiliar kitchen). Given an egocentric RGB-D camera and a high-level action space, the agent is rewarded for maximizing successful interactions while simultaneously training an image-based affordance segmentation model. The former yields a policy for acting efficiently in new environments to prepare for downstream interaction tasks, while the latter yields a convolutional neural network that maps image regions to the likelihood they permit each action, densifying the rewards for exploration. We demonstrate our idea with AI2-iTHOR. The results show agents can learn how to use new home environments intelligently and that it prepares them to rapidly address various downstream tasks like "find a knife and put it in the drawer. " Project page: http: //vision. cs. utexas. edu/projects/interaction-exploration/

NeurIPS Conference 2017 Conference Paper

Learning Spherical Convolution for Fast Features from 360° Imagery

• Yu-Chuan Su
• Kristen Grauman

While 360° cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make core feature extraction non-trivial. Convolutional neural networks (CNNs) trained on images from perspective cameras yield “flat" filters, yet 360° images cannot be projected to a single plane without significant distortion. A naive solution that repeatedly projects the viewing sphere to all tangent planes is accurate, but much too computationally intensive for real problems. We propose to learn a spherical convolutional network that translates a planar CNN to process 360° imagery directly in its equirectangular projection. Our approach learns to reproduce the flat filter outputs on 360° data, sensitive to the varying distortion effects across the viewing sphere. The key benefits are 1) efficient feature extraction for 360° images and video, and 2) the ability to leverage powerful pre-trained networks researchers have carefully honed (together with massive labeled image training sets) for perspective images. We validate our approach compared to several alternative methods in terms of both raw CNN output accuracy as well as applying a state-of-the-art “flat" object detector to 360° data. Our method yields the most accurate results while saving orders of magnitude in computation versus the existing exact reprojection solution.

UAI Conference 2015 Conference Paper

Large-Margin Determinantal Point Processes

• Wei-Lun Chao
• Boqing Gong
• Kristen Grauman
• Fei Sha

Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset from a ground set of items. We study the problem of learning the parameters (i. e. , the kernel matrix) of a DPP from labeled training data. In this paper, we develop a novel parameter estimation technique particularly tailored for DPPs based on the principle of large margin separation. In contrast to the state-of-the-art method of maximum likelihood estimation of the DPP parameters, our large-margin loss function explicitly models errors in selecting the target subsets, and it can be customized to trade off different types of errors (precision vs. recall). Extensive empirical studies validate our contributions, including applications on challenging document and video summarization, where flexibility in balancing different errors while training the summarization models is indispensable.

NeurIPS Conference 2014 Conference Paper

Diverse Sequential Subset Selection for Supervised Video Summarization

• Boqing Gong
• Wei-Lun Chao
• Kristen Grauman
• Fei Sha

Video summarization is a challenging problem with great application potential. Whereas prior approaches, largely unsupervised in nature, focus on sampling useful frames and assembling them as summaries, we consider video summarization as a supervised subset selection problem. Our idea is to teach the system to learn from human-created summaries how to select informative and diverse subsets, so as to best meet evaluation metrics derived from human-perceived quality. To this end, we propose the sequential determinantal point process (seqDPP), a probabilistic model for diverse sequential subset selection. Our novel seqDPP heeds the inherent sequential structures in video data, thus overcoming the deficiency of the standard DPP, which treats video frames as randomly permutable items. Meanwhile, seqDPP retains the power of modeling diverse subsets, essential for summarization. Our extensive results of summarizing videos from 3 datasets demonstrate the superior performance of our method, compared to not only existing unsupervised methods but also naive applications of the standard DPP model.

NeurIPS Conference 2014 Conference Paper

Predicting Useful Neighborhoods for Lazy Local Learning

• Aron Yu
• Kristen Grauman

Lazy local learning methods train a classifier on the fly" at test time, using only a subset of the training instances that are most relevant to the novel test example. The goal is to tailor the classifier to the properties of the data surrounding the test example. Existing methods assume that the instances most useful for building the local model are strictly those closest to the test example. However, this fails to account for the fact that the success of the resulting classifier depends on the full distribution of selected training instances. Rather than simply gather the test example's nearest neighbors, we propose to predict the subset of training data that is jointly relevant to training its local model. We develop an approach to discover patterns between queries and their "good" neighborhoods using large-scale multi-label classification with compressed sensing. Given a novel test point, we estimate both the composition and size of the training subset likely to yield an accurate local model. We demonstrate the approach on image classification tasks on SUN and aPascal and show it outperforms traditional global and local approaches. "

NeurIPS Conference 2014 Conference Paper

Zero-shot recognition with unreliable attributes

• Dinesh Jayaraman
• Kristen Grauman

In principle, zero-shot learning makes it possible to train an object recognition model simply by specifying the category's attributes. For example, with classifiers for generic attributes like striped and four-legged, one can construct a classifier for the zebra category by enumerating which properties it possesses --- even without providing zebra training images. In practice, however, the standard zero-shot paradigm suffers because attribute predictions in novel images are hard to get right. We propose a novel random forest approach to train zero-shot models that explicitly accounts for the unreliability of attribute predictions. By leveraging statistics about each attribute’s error tendencies, our method obtains more robust discriminative models for the unseen classes. We further devise extensions to handle the few-shot scenario and unreliable attribute descriptions. On three datasets, we demonstrate the benefit for visual category learning with zero or few training examples, a critical domain for rare categories or categories defined on the fly.

ICML Conference 2013 Conference Paper

Analogy-preserving Semantic Embedding for Visual Object Categorization

• Sung Ju Hwang
• Kristen Grauman
• Fei Sha

In multi-class categorization tasks, knowledge about the classes’ semantic relationships can provide valuable information beyond the class labels themselves. However, existing techniques focus on preserving the semantic distances between classes (e. g. , according to a given object taxonomy for visual recognition), limiting the influence to pairwise structures. We propose to model \emphanalogies that reflect the relationships between multiple pairs of classes simultaneously, in the form “p is to q, as r is to s"". We translate semantic analogies into higher-order geometric constraints called \emphanalogical parallelograms, and use them in a novel convex regularizer for a discriminatively learned label embedding. Furthermore, we show how to discover analogies from attribute-based class descriptions, and how to prioritize those likely to reduce inter-class confusion. Evaluating our Analogy-preserving Semantic Embedding (ASE) on two visual recognition datasets, we demonstrate clear improvements over existing approaches, both in terms of recognition accuracy and analogy completion.

ICML Conference 2013 Conference Paper

Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation

• Boqing Gong
• Kristen Grauman
• Fei Sha

Learning domain-invariant features is of vital importance to unsupervised domain adaptation, where classifiers trained on the source domain need to be adapted to a different target domain for which no labeled examples are available. In this paper, we propose a novel approach for learning such features. The central idea is to exploit the existence of landmarks, which are a subset of labeled data instances in the source domain that are distributed most similarly to the target domain. Our approach automatically discovers the landmarks and use them to bridge the source to the target by constructing provably easier auxiliary domain adaptation tasks. The solutions of those auxiliary tasks form the basis to compose invariant features for the original task. We show how this composition can be optimized discriminatively without requiring labels from the target domain. We validate the method on standard benchmark datasets for visual object recognition and sentiment analysis of text. Empirical results show the proposed method outperforms the state-of-the-art significantly.

NeurIPS Conference 2013 Conference Paper

Reshaping Visual Datasets for Domain Adaptation

• Boqing Gong
• Kristen Grauman
• Fei Sha

In visual recognition problems, the common data distribution mismatches between training and testing make domain adaptation essential. However, image data is difficult to manually divide into the discrete domains required by adaptation algorithms, and the standard practice of equating datasets with domains is a weak proxy for all the real conditions that alter the statistics in complex ways (lighting, pose, background, resolution, etc. ) We propose an approach to automatically discover latent domains in image or video datasets. Our formulation imposes two key properties on domains: maximum distinctiveness and maximum learnability. By maximum distinctiveness, we require the underlying distributions of the identified domains to be different from each other; by maximum learnability, we ensure that a strong discriminative model can be learned from the domain. We devise a nonparametric representation and efficient optimization procedure for distinctiveness, which, when coupled with our learnability constraint, can successfully discover domains among both training and test data. We extensively evaluate our approach on object recognition and human activity recognition tasks.

AAAI Conference 2012 Conference Paper

Relative Attributes for Enhanced Human-Machine Communication

• Devi Parikh
• Adriana Kovashka
• Amar Parkash
• Kristen Grauman

We propose to model relative attributes1 that capture the relationships between images and objects in terms of human-nameable visual properties. For example, the models can capture that animal A is ‘furrier’ than animal B, or image X is ‘brighter’ than image B. Given training data stating how object/scene categories relate according to different attributes, we learn a ranking function per attribute. The learned ranking functions predict the relative strength of each property in novel images. We show how these relative attribute predictions enable a variety of novel applications, including zero-shot learning from relative comparisons, automatic image description, image search with interactive feedback, and active learning of discriminative classifiers. We overview results demonstrating these applications with images of faces and natural scenes. Overall, we find that relative attributes enhance the precision of communication between humans and computer vision algorithms, providing the richer language needed to fluidly “teach” a system about visual concepts.

NeurIPS Conference 2012 Conference Paper

Semantic Kernel Forests from Multiple Taxonomies

• Sung Hwang
• Kristen Grauman
• Fei Sha

When learning features for complex visual recognition problems, labeled image exemplars alone can be insufficient. While an \emph{object taxonomy} specifying the categories' semantic relationships could bolster the learning process, not all relationships are relevant to a given visual classification task, nor does a single taxonomy capture all ties that \emph{are} relevant. In light of these issues, we propose a discriminative feature learning approach that leverages \emph{multiple} hierarchical taxonomies representing different semantic views of the object categories (e. g. , for animal classes, one taxonomy could reflect their phylogenic ties, while another could reflect their habitats). For each taxonomy, we first learn a tree of semantic kernels, where each node has a Mahalanobis kernel optimized to distinguish between the classes in its children nodes. Then, using the resulting \emph{semantic kernel forest}, we learn class-specific kernel combinations to select only those relationships relevant to recognize each object class. To learn the weights, we introduce a novel hierarchical regularization term that further exploits the taxonomies' structure. We demonstrate our method on challenging object recognition datasets, and show that interleaving multiple taxonomic views yields significant accuracy improvements.

NeurIPS Conference 2011 Conference Paper

Learning a Tree of Metrics with Disjoint Visual Features

• Kristen Grauman
• Fei Sha
• Sung Hwang

We introduce an approach to learn discriminative visual representations while exploiting external semantic knowledge about object category relationships. Given a hierarchical taxonomy that captures semantic similarity between the objects, we learn a corresponding tree of metrics (ToM). In this tree, we have one metric for each non-leaf node of the object hierarchy, and each metric is responsible for discriminating among its immediate subcategory children. Specifically, a Mahalanobis metric learned for a given node must satisfy the appropriate (dis)similarity constraints generated only among its subtree members' training instances. To further exploit the semantics, we introduce a novel regularizer coupling the metrics that prefers a sparse disjoint set of features to be selected for each metric relative to its ancestor supercategory nodes' metrics. Intuitively, this reflects that visual cues most useful to distinguish the generic classes (e. g. , feline vs. canine) should be different than those cues most useful to distinguish their component fine-grained classes (e. g. , Persian cat vs. Siamese cat). We validate our approach with multiple image datasets using the WordNet taxonomy, show its advantages over alternative metric learning approaches, and analyze the meaning of attribute features selected by our algorithm.

ICML Conference 2011 Conference Paper

Learning with Whom to Share in Multi-task Feature Learning

• Zhuoliang Kang
• Kristen Grauman
• Fei Sha

NeurIPS Conference 2010 Conference Paper

Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning

• Prateek Jain
• Sudheendra Vijayanarasimhan
• Kristen Grauman

We consider the problem of retrieving the database points nearest to a given {\em hyperplane} query without exhaustively scanning the database. We propose two hashing-based solutions. Our first approach maps the data to two-bit binary keys that are locality-sensitive for the angle between the hyperplane normal and a database point. Our second approach embeds the data into a vector space where the Euclidean norm reflects the desired distance between the original points and hyperplane query. Both use hashing to retrieve near points in sub-linear time. Our first method's preprocessing stage is more efficient, while the second has stronger accuracy guarantees. We apply both to pool-based active learning: taking the current hyperplane classifier as a query, our algorithm identifies those points (approximately) satisfying the well-known minimal distance-to-hyperplane selection criterion. We empirically demonstrate our methods' tradeoffs, and show that they make it practical to perform active selection with millions of unlabeled points.

NeurIPS Conference 2008 Conference Paper

Multi-Level Active Prediction of Useful Image Annotations for Recognition

• Sudheendra Vijayanarasimhan
• Kristen Grauman

We introduce a framework for actively learning visual categories from a mixture of weakly and strongly labeled image examples. We propose to allow the category-learner to strategically choose what annotations it receives---based on both the expected reduction in uncertainty as well as the relative costs of obtaining each annotation. We construct a multiple-instance discriminative classifier based on the initial training data. Then all remaining unlabeled and weakly labeled examples are surveyed to actively determine which annotation ought to be requested next. After each request, the current classifier is incrementally updated. Unlike previous work, our approach accounts for the fact that the optimal use of manual annotation may call for a combination of labels at multiple levels of granularity (e. g. , a full segmentation on some images and a present/absent flag on others). As a result, it is possible to learn more accurate category models with a lower total expenditure of manual annotation effort.

NeurIPS Conference 2008 Conference Paper

Online Metric Learning and Fast Similarity Search

• Prateek Jain
• Brian Kulis
• Inderjit Dhillon
• Kristen Grauman

Metric learning algorithms can provide useful distance functions for a variety of domains, and recent work has shown good accuracy for problems where the learner can access all distance constraints at once. However, in many real applications, constraints are only available incrementally, thus necessitating methods that can perform online updates to the learned metric. Existing online algorithms offer bounds on worst-case performance, but typically do not perform well in practice as compared to their offline counterparts. We present a new online metric learning algorithm that updates a learned Mahalanobis metric based on LogDet regularization and gradient descent. We prove theoretical worst-case performance bounds, and empirically compare the proposed method against existing online metric learning algorithms. To further boost the practicality of our approach, we develop an online locality-sensitive hashing scheme which leads to efficient updates for approximate similarity search data structures. We demonstrate our algorithm on multiple datasets and show that it outperforms relevant baselines.

JMLR Journal 2007 Journal Article

The Pyramid Match Kernel: Efficient Learning with Sets of Features

• Kristen Grauman
• Trevor Darrell

In numerous domains it is useful to represent a single example by the set of the local features or parts that comprise it. However, this representation poses a challenge to many conventional machine learning techniques, since sets may vary in cardinality and elements lack a meaningful ordering. Kernel methods can learn complex functions, but a kernel over unordered set inputs must somehow solve for correspondences---generally a computationally expensive task that becomes impractical for large set sizes. We present a new fast kernel function called the pyramid match that measures partial match similarity in time linear in the number of features. The pyramid match maps unordered feature sets to multi-resolution histograms and computes a weighted histogram intersection in order to find implicit correspondences based on the finest resolution histogram cell where a matched pair first appears. We show the pyramid match yields a Mercer kernel, and we prove bounds on its error relative to the optimal partial matching cost. We demonstrate our algorithm on both classification and regression tasks, including object recognition, 3-D human pose inference, and time of publication estimation for documents, and we show that the proposed method is accurate and significantly more efficient than current approaches. [abs] [ pdf ][ bib ] &copy JMLR 2007. ( edit, beta )

AAAI Conference 2007 Conference Paper

The Pyramid Match: Efficient Learning with Partial Correspondences

• Kristen Grauman

It is often useful to represent a single example by a set of the local features that comprise it. However, this representation poses a challenge to many conventional learning techniques, since sets may vary in cardinality and the elements are unordered. To compare sets of features, researchers often resort to solving for the least-cost correspondences, but this is computationally expensive and becomes impractical for large set sizes. We have developed a general approximate matching technique called the pyramid match that measures partial match similarity in time linear in the number of feature vectors per set. The matching forms a Mercer kernel, making it valid for use in many existing kernel-based learning methods. We have demonstrated the approach for various learning tasks in vision and text processing, and find that it is accurate and significantly more efficient than previous approaches.

NeurIPS Conference 2006 Conference Paper

Approximate Correspondences in High Dimensions

• Kristen Grauman
• Trevor Darrell

Pyramid intersection is an efficient method for computing an approximate partial matching between two sets of feature vectors. We introduce a novel pyramid em- bedding based on a hierarchy of non-uniformly shaped bins that takes advantage of the underlying structure of the feature space and remains accurate even for sets with high-dimensional feature vectors. The matching similarity is computed in linear time and forms a Mercer kernel. Whereas previous matching approxima- tion algorithms suffer from distortion factors that increase linearly with the fea- ture dimension, we demonstrate that our approach can maintain constant accuracy even as the feature dimension increases. When used as a kernel in a discrimina- tive classifier, our approach achieves improved object recognition results over a state-of-the-art set kernel.