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Works37 from public data
- Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities432
The first multi-view action dataset, with si-multaneous static and egocentric recordings, and a novel task of detecting mistakes is proposed, to investigate generalization to new toys, cross-view transfer, long-tailed distributions, and pose vs. appearance.
- Temporal Aggregate Representations for Long-Range Video Understanding185
This work addresses questions of temporal extent, scaling, and level of semantic abstraction with a flexible multi-granular temporal aggregation framework and shows that it is possible to achieve state of the art in both next action and dense anticipation with simple techniques such as max-pooling and attention.
- Temporal Action Segmentation: An Analysis of Modern Techniques165
This survey analyzes and summarizes the most significant contributions and trends in temporal action segmentation in videos, and systematically investigates two essential techniques of this topic, i.e., frame representation and temporal modeling.
- Unsupervised Learning of Action Classes With Continuous Temporal Embedding144
This work uses a continuous temporal embedding of framewise features to benefit from the sequential nature of activities and identifies clusters of temporal segments across all videos that correspond to semantic meaningful action classes.
- Unsupervised Learning and Segmentation of Complex Activities from Video128
This paper proposes an iterative discriminative-generative approach which alternates between discriminatively learning the appearance of sub-activities from the videos' visual features to sub-activity labels and generatively modelling the temporal structure of sub theactivities using a Generalized Mallows Model.
- DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions86
This work introduces DiffH2O, a new diffusion-based framework for synthesizing realistic, dexterous hand-object interactions from natural language, and proposes grasp guidance, which directs the diffusion model towards a target grasp, seamlessly connecting the grasping and interaction stages through a motion imputation mechanism.
- AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation85
It is shown that having higher-quality hand poses directly improves the ability to recognize actions, and a novel action classification task is designed to evaluate predicted 3D hand poses.
- Zero-Shot Anticipation for Instructional Activities75
A hierarchical model is presented that generalizes instructional knowledge from large-scale text-corpora and transfers the knowledge to the visual domain and predicts coherent and plausible actions multiple steps into the future, all in rich natural language.
- Two-person interaction recognition via spatial multiple instance embedding38
Experimental results on two benchmark datasets validate that using two-person visual descriptors together with spatial multiple instance learning offers an effective way for inferring the type of the interaction.
- On Recognizing Actions in Still Images via Multiple Features36
The results show that using multiple object hypotheses within multiple instance learning is effective for human action recognition in still images and such an object representation is suitable for using in conjunction with other visual features.
- Every Mistake Counts in Assembly26
A system that can detect ordering mistakes by utilizing a learned knowledge base that constructs a knowledge base with spatial and temporal beliefs based on observed mistakes, and demonstrates the superior performance of the belief inference algorithm in detecting ordering mistakes on the Assembly101 dataset.
- DRAW23
A young boy's capability to recognize an illustrator's style in a totally different context is motivated and a deep networks method exploiting deep networks for categorizing illustrators over-performed the traditional methods by more than 10%.
- Transferring Knowledge From Text to Video: Zero-Shot Anticipation for Procedural Actions21
A hierarchical model that generalizes instructional knowledge from large-scale text corpora and transfers the knowledge to video recognizes and predicts coherent and plausible actions multiple steps into the future, all in rich natural language.
- Context-Enhanced Memory-Refined Transformer for Online Action Detection18
A Context-enhanced Memory-Refined Transformer (CMeRT) is proposed, which introduces a context-enhanced encoder to improve frame representations using additional near-past context and features a memory-refined decoder to leverage near-future generation to enhance performance.
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- Learning Style Compatibility for Furniture13
This paper investigates how Siamese networks can be used efficiently for assessing the style compatibility between images of furniture items and shows that the middle layers of pretrained CNNs can capture essential information about furniture style, which allows for efficient applications of such networks for this task.
- X-MIC: Cross-Modal Instance Conditioning for Egocentric Action Generalization10
This work proposes a simple yet effective cross-modal adaptation framework for vision-language models to image and third-person video classification called X-MIC, which retains and improves generalization of the pre-trained VLMs by disentangling learnable temporal modeling and frozen visual en-coder.
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- Don't Pause! Every prediction matters in a streaming video4
AsynKV, a training-free streaming adaptation of offline models, that retains their event perception while improving their streaming behavior, serves as a strong baseline on SPOT-Bench, outperforming existing streaming models, and achieves state-of-the-art on retrospective benchmarks.
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- Transformed ROIs for capturing visual transformations in videos4
TROI, a plug-and-play module for CNNs to reason between mid-level feature representations that are otherwise separated in space and time, achieves state-of-the-art action recognition results on the large-scale datasets Something-Something-V2 and EPIC-Kitchens-100.
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- Identification of Illustrators4
Inspired by the human's ability to identify illustrators, this study proposes a method that can automatically learn to distinguish illustrations of different illustrators using computer vision techniques.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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