Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Suggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works161 from public data
- 3,070
- 2,188
- Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words1,938
A novel unsupervised learning method for human action categories that can recognize and localize multiple actions in long and complex video sequences containing multiple motions.
- Dense-Captioning Events in Videos1,676
This work proposes a new model that is able to identify all events in a single pass of the video while simultaneously describing the detected events with natural language, and introduces a new captioning module that uses contextual information from past and future events to jointly describe all events.
- Modeling Temporal Structure of Decomposable Motion Segments for Activity Classification824
A framework for modeling motion by exploiting the temporal structure of the human activities, which represents activities as temporal compositions of motion segments, and shows that the algorithm performs better than other state of the art methods.
- 508
- Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs455
This work introduces Action Genome, a representation that decomposes actions into spatio-temporal scene graphs and demonstrates the utility of a hierarchical event decomposition by enabling few-shot action recognition, achieving 42.7% mAP using as few as 10 examples.
- 447
- ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding443
ULIP is introduced to learn a unified representation of image, text, and 3D point cloud by pre-training with object triplets from the three modalities, using a pre-trained vision-language model that has already learned a common visual and textual space by training with massive image-text pairs.
- DAPs: Deep Action Proposals for Action Understanding425
Deep Action Proposals (DAPs), an effective and efficient algorithm for generating temporal action proposals from long videos, is introduced, which outperforms previous work on a large scale action benchmark, runs at 134 FPS making it practical for large-scale scenarios, and exhibits an appealing ability to generalize.
- Peeking Into the Future: Predicting Future Person Activities and Locations in Videos421
An end-to-end, multi-task learning system utilizing rich visual features about human behavioral information and interaction with their surroundings is proposed, providing the first empirical evidence that joint modeling of paths and activities benefits future path prediction.
- Learning to Decompose and Disentangle Representations for Video Prediction324
The Decompositional Disentangled Predictive Auto-Encoder (DDPAE) is proposed, a framework that combines structured probabilistic models and deep networks to automatically decompose the high-dimensional video that the authors aim to predict into components, and disentangle each component to have low-dimensional temporal dynamics that are easier to predict.
- Few-Shot Video Classification via Temporal Alignment300
This paper proposes the Ordered Temporal Alignment Module (OTAM), a novel few-shot learning framework that can learn to classify a previously unseen video and demonstrates that the model leads to significant improvement of few- shot video classification over a wide range of competitive baselines and outperforms state-of-the-art benchmarks by a large margin.
- 292
- ULIP-2: Towards Scalable Multimodal Pre-Training for 3D Understanding287
ULIP-2 sheds light on a new paradigm for scalable multimodal 3D representation learning without human annotations and shows significant improvements over existing baselines.
- Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers286
A computer vision based algorithm for recognizing single actions of earthmoving construction equipment, based on a multiple binary SVM classifier and spatio-temporal features, which outperforms previous algorithms for excavator and truck action recognition.
- Spatio-Temporal Graph for Video Captioning With Knowledge Distillation277
A novel spatio-temporal graph model for video captioning that exploits object interactions in space and time and an object-aware knowledge distillation mechanism, in which local object information is used to regularize global scene features are proposed.
- Connectionist Temporal Modeling for Weakly Supervised Action Labeling259
The Extended Connectionist Temporal Classification (ECTC) framework is introduced to efficiently evaluate all possible alignments via dynamic programming and explicitly enforce their consistency with frame-to-frame visual similarities.
- End-to-End, Single-Stream Temporal Action Detection in Untrimmed Videos253
This work introduces the new architecture for Single-Stream Temporal Action Detection (SS-TAD), which effectively integrates joint action detection with its semantic sub-tasks in a single unifying end-to-end framework.
- Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors249
Preliminary results on detection of standing workers, excavators and dump trucks with an average accuracy of 98.83%, 82.10%, and 84.88% respectively indicate the applicability of the proposed method for automated activity analysis of workers and equipment from single video cameras.
- Revisiting the “Video” in Video-Language Understanding247
It is found that understanding of event temporality is often not necessary to achieve strong or state-of-the-art performance, even compared with recent large-scale video-language models and in contexts intended to benchmark deeper video-level understanding.
- Align and Prompt: Video-and-Language Pre-training with Entity Prompts238
This paper proposes Align and Prompt: a new video-and-language pre-training framework (AlPro), which operates on sparsely-sampled video frames and achieves more effective cross-modal alignment without explicit object detectors.
- 210
- Leveraging Video Descriptions to Learn Video Question Answering197
A self-paced learning procedure to iteratively identify non-perfect candidate QA pairs and mitigate their effects in training is proposed and shown to be effective and the extended SS model outperforms various baselines.
- Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction195
A framework based on graph convolution to uncover the spatiotemporal relationships in the scene for reasoning about pedestrian intent is presented and a new dataset designed specifically for autonomous-driving scenarios in areas with dense pedestrian populations is introduced: the Stanford-TRI Intent Prediction (STIP) dataset.
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 2026-10-13. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.