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Predicting Motivations of Actions by Leveraging Text

https://doi.org/10.1109/cvpr.2016.327

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Microsoft COCO: Common Objects in Context

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Reasoning about Object Affordances in a Knowledge Base Representation

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Diverse M-Best Solutions in Markov Random Fields

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Activity Forecasting

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Cutting-plane training of structural SVMs

inferredcites →https://doi.org/10.1016/0010-0277(83)90004-5openalex-snapshot

Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children's understanding of deception

inferredcites →https://doi.org/10.1016/j.cognition.2009.07.005openalex-snapshot

Action understanding as inverse planning

inferredcites →https://doi.org/10.1016/j.imavis.2009.11.014openalex-snapshot

A survey on vision-based human action recognition

inferredcites →https://doi.org/10.1016/j.robot.2014.01.003openalex-snapshot

Learning intentions for improved human motion prediction

inferredcites →https://doi.org/10.1016/s1053-8119(03)00230-1openalex-snapshot

People thinking about thinking peopleThe role of the temporo-parietal junction in “theory of mind”

inferredcites →https://doi.org/10.1109/cvpr.2011.5995466openalex-snapshot

Baby talk: Understanding and generating simple image descriptions

inferredcites →https://doi.org/10.1109/cvpr.2012.6248012openalex-snapshot

Max-margin early event detectors

inferredcites →https://doi.org/10.1109/cvpr.2013.387openalex-snapshot

Bringing Semantics into Focus Using Visual Abstraction

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Predicting Object Dynamics in Scenes

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Visual Persuasion: Inferring Communicative Intents of Images

inferredcites →https://doi.org/10.1109/cvpr.2014.412openalex-snapshot

Learning Everything about Anything: Webly-Supervised Visual Concept Learning

inferredcites →https://doi.org/10.1109/cvpr.2014.416openalex-snapshot

Patch to the Future: Unsupervised Visual Prediction

inferredcites →https://doi.org/10.1109/cvpr.2015.7298932openalex-snapshot

Deep visual-semantic alignments for generating image descriptions

inferredcites →https://doi.org/10.1109/cvpr.2015.7298935openalex-snapshot

Show and tell: A neural image caption generator

inferredcites →https://doi.org/10.1109/cvpr.2016.501openalex-snapshot

MovieQA: Understanding Stories in Movies through Question-Answering

inferredcites →https://doi.org/10.1109/cvprw.2014.131openalex-snapshot

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

inferredcites →https://doi.org/10.1109/iccv.2013.178openalex-snapshot

NEIL: Extracting Visual Knowledge from Web Data

inferredcites →https://doi.org/10.1109/iccv.2013.211openalex-snapshot

Learning the Visual Interpretation of Sentences

inferredcites →https://doi.org/10.1109/iccv.2013.277openalex-snapshot

Inferring "Dark Matter" and "Dark Energy" from Videos

inferredcites →https://doi.org/10.1109/iccv.2015.279openalex-snapshot

VQA: Visual Question Answering

inferredcites →https://doi.org/10.1109/icra.2013.6630785openalex-snapshot

Predicting human intention in visual observations of hand/object interactions

inferredcites →https://doi.org/10.1109/icra.2013.6631291openalex-snapshot

Language for learning complex human-object interactions

inferredcites →https://doi.org/10.1109/tpami.2011.27openalex-snapshot

C^4: Exploring Multiple Solutions in Graphical Models by Cluster Sampling

inferredcites →https://doi.org/10.1145/1922649.1922653openalex-snapshot

Human activity analysis

inferredcites →https://doi.org/10.1145/2157689.2157740openalex-snapshot

Deep networks for predicting human intent with respect to objects

inferredcites →https://doi.org/10.1145/2461466.2461504openalex-snapshot

Exploiting language models to recognize unseen actions

inferredcites →https://doi.org/10.1287/mnsc.18.7.401openalex-snapshot

A Procedure for Computing the K Best Solutions to Discrete Optimization Problems and Its Application to the Shortest Path Problem

inferredcites →https://doi.org/10.15607/rss.2013.ix.006openalex-snapshot

Anticipating Human Activities using Object Affordances for Reactive Robotic Response

inferredcites →https://doi.org/10.2514/6.2012-2445openalex-snapshot

Human Intent Prediction Using Markov Decision Processes

inferredcites →https://doi.org/10.48550/arxiv.1312.1743openalex-snapshot

Dual coordinate solvers for large-scale structural SVMs

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1506.06726openalex-snapshot

Skip-Thought Vectors

inferredcites →https://doi.org/10.5244/c.23.2openalex-snapshot

Learning Models for Object Recognition from Natural Language Descriptions

inferredcites →https://doi.org/10.63317/3uyduafd26myopenalex-snapshot

N-gram Counts and Language Models from the Common Crawl

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Generating and evaluating explanations of attended and error‐inducing input regions for VQA models

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MCIC: Multimodal Conversational Intent Classification for E-commerce Customer Service

inferred← citeshttps://doi.org/10.1007/978-3-031-73247-8_9openalex-snapshot

Synergy of Sight and Semantics: Visual Intention Understanding with CLIP

inferred← citeshttps://doi.org/10.1007/978-3-319-90403-0_15openalex-snapshot

Group Cognition and Collaborative AI

inferred← citeshttps://doi.org/10.1007/s11263-020-01404-0openalex-snapshot

Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent

inferred← citeshttps://doi.org/10.1016/j.neucom.2025.131117openalex-snapshot

UCFN: Uncertainty-aware cross-granularity fusion network for visual intention understanding

inferred← citeshttps://doi.org/10.1109/cvpr.2017.599openalex-snapshot

Asynchronous Temporal Fields for Action Recognition

inferred← citeshttps://doi.org/10.1109/cvpr.2018.00711openalex-snapshot

Where and Why are They Looking? Jointly Inferring Human Attention and Intentions in Complex Tasks

inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00965openalex-snapshot

NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions

inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.01279openalex-snapshot

Intentonomy: a Dataset and Study towards Human Intent Understanding

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3MASSIV: Multilingual, Multimodal and Multi-Aspect dataset of Social Media Short Videos

inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.02059openalex-snapshot

From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering

inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.01324openalex-snapshot

Affordances from Human Videos as a Versatile Representation for Robotics

inferred← citeshttps://doi.org/10.1109/cvprw56347.2022.00384openalex-snapshot

Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos

inferred← citeshttps://doi.org/10.1109/iccv.2017.15openalex-snapshot

Anticipating Daily Intention Using On-wrist Motion Triggered Sensing

inferred← citeshttps://doi.org/10.1109/icdl-epirob48136.2020.9278057openalex-snapshot

Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset

inferred← citeshttps://doi.org/10.1109/ijcnn64981.2025.11229224openalex-snapshot

Through the Theory of Mind’s Eye: Reading Minds with Multimodal Video Large Language Models

inferred← citeshttps://doi.org/10.1109/taffc.2023.3247876openalex-snapshot

Learnable Hierarchical Label Embedding and Grouping for Visual Intention Understanding

inferred← citeshttps://doi.org/10.1109/tip.2023.3261743openalex-snapshot

Cross-Modality Pyramid Alignment for Visual Intention Understanding

inferred← citeshttps://doi.org/10.1109/tip.2024.3380250openalex-snapshot

Label-Aware Calibration and Relation-Preserving in Visual Intention Understanding

inferred← citeshttps://doi.org/10.1109/tmm.2023.3234817openalex-snapshot

Prototype-Based Intent Perception

inferred← citeshttps://doi.org/10.1145/3474085.3475638openalex-snapshot

Hybrid Reasoning Network for Video-based Commonsense Captioning

inferred← citeshttps://doi.org/10.1145/3503161.3548257openalex-snapshot

Visual Knowledge Graph for Human Action Reasoning in Videos

inferred← citeshttps://doi.org/10.1145/3572914openalex-snapshot

Dual Scene Graph Convolutional Network for Motivation Prediction

inferred← citeshttps://doi.org/10.1145/3627508.3638347openalex-snapshot

Human and Large Language Model Intent Detection in Image-Based Self-Expression of People with Intellectual Disability

inferred← citeshttps://doi.org/10.14569/ijacsa.2022.0130535openalex-snapshot

Rule-based Text Extraction for Multimodal Knowledge Graph

inferred← citeshttps://doi.org/10.18653/v1/2020.emnlp-main.61openalex-snapshot

Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning

inferred← citeshttps://doi.org/10.18653/v1/2020.findings-emnlp.248openalex-snapshot

Be Different to Be Better! A Benchmark to Leverage the Complementarity of Language and Vision

inferred← citeshttps://doi.org/10.18653/v1/2020.findings-emnlp.253openalex-snapshot

Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs

inferred← citeshttps://doi.org/10.21203/rs.3.rs-10542927/v1openalex-snapshot

SymCLIP: Symmetric Prompting with Adaptive Semantic Labeling for Multi-Intent Recognition

inferred← citeshttps://doi.org/10.24963/ijcai.2017/230openalex-snapshot

Object Detection Meets Knowledge Graphs

inferred← citeshttps://doi.org/10.48550/arxiv.1607.02769openalex-snapshot

Annotation Methodologies for Vision and Language Dataset Creation

inferred← citeshttps://doi.org/10.48550/arxiv.2003.05162openalex-snapshot

Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning

inferred← citeshttps://doi.org/10.53846/goediss-7632openalex-snapshot

Action-oriented Scene Understanding

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Hybrid approaches for context recognition in Ambient Assisted Living systems : application to emotion recognition and human activity recognition and anticipation

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