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Claim this profileWorks292 from public data
- Ego4D: Around the World in 3,000 Hours of Egocentric Video2,124
Ego4D, a massive-scale egocentric video dataset and benchmark suite, is introduced and a host of new benchmark challenges centered around understanding the first-person visual experience in the past, present, and future are presented.
- Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking1,056
This work shows that a basic Kalman filter can still obtain state-of-the-art tracking performance if proper care is taken to fix the noise accumulated during Occlusion, and improves robustness during occlusion and non-linear motion.
- AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting792
A stochastic multi-agent trajectory prediction model that can attend to features of any agent at any previous timestep when inferring an agent’s future position is proposed and significantly improves the state of the art on well-established pedestrian and autonomous driving datasets.
- Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives640
To push the frontier of first-person video understanding of skilled human activity, a suite of benchmark tasks and their annotations are presented, including fine-grained activity understanding, proficiency estimation, cross-view translation, and 3D hand/body pose.
- Activity Forecasting639
The unified model uses state-of-the-art semantic scene understanding combined with ideas from optimal control theory to achieve accurate activity forecasting and shows how the same techniques can improve the results of tracking algorithms by leveraging information about likely goals and trajectories.
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- No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency481
A novel model that leverages self-consistency as a source of self-supervision to improve the robustness of NR-IQA models is proposed and enforce self- Consistency between the outputs of the quality assessment model for each image and its transformation to reduce the uncertainty of the model.
- DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion467
A large-scale dataset for multi-human tracking, where humans have sim-ilar appearance, diverse motion and extreme articulation, and is named “DanceTrack”, which is expected to provide a better platform to develop more MOT algorithms that rely less on visual discrimination and depend more on motion analysis.
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- PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings419
A probabilistic forecasting model of future interactions between a variable number of agents that performs both standard forecasting and the novel task of conditional forecasting, which reasons about how all agents will likely respond to the goal of a controlled agent.
- Rethinking Transformer-based Set Prediction for Object Detection389
Experimental results show that the proposed methods not only converge much faster than the original DETR, but also significantly outperform DETR and other baselines in terms of detection accuracy.
- Deep OC-Sort: Multi-Pedestrian Tracking by Adaptive Re-Identification381
A novel way to leverage objects’ appearances to adaptively integrate appearance matching into existing high-performance motion-based methods, building upon the pure motion- based method OC-SORT.
- Perpetual Humanoid Control for Real-time Simulated Avatars365
A physics-based humanoid controller is presented that achieves high-fidelity motion imitation and fault-tolerant behavior in the presence of noisy input and unexpected falls and dynamically allocates new network capacity to learn harder and harder motion sequences.
- Cross-Domain Adaptive Teacher for Object Detection329
A teacher-student framework named Adaptive Teacher (AT) which leverages domain adversarial learning and weak-strong data augmentation to address the domain gap in object detection and demonstrates superiority over existing approaches and even Oracle (fully-supervised) models by a large margin.
- Going Deeper into First-Person Activity Recognition318
By learning to recognize objects, actions and activities jointly, the performance of individual recognition tasks also increase by 30% (actions) and 14% ( objects) and the results of extensive ablative analysis are included to highlight the importance of network design decisions.
- DLow: Diversifying Latent Flows for Diverse Human Motion Prediction315
A novel sampling method, Diversifying Latent Flows (DLow), to produce a diverse set of samples from a pretrained deep generative model that outperforms state-of-the-art baseline methods in terms of sample diversity and accuracy.
- Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation311
This work presents Human to Humanoid (H2O), a reinforcement learning (RL) based framework that enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB camera, and achieves teleoperation of dynamic whole-body motions in real-world scenarios.
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- Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud296
This work aims at bridging the performance gap between 3D sensing and 2D sensing for 3D object detection by enhancing LiDAR-based algorithms to work with single image input by enhancing pseudo-LiDAR end-to-end methods.
- NavCog288
This work proposes a smartphone-based system that provides turn-by-turn navigation assistance based on accurate real-time localization over large spaces and shows that the system is capable of guiding visually impaired users in complex and unfamiliar environments.
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- Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection260
The theoretical and empirical analysis demonstrates that the optimal temporal difference between views varies significantly for different pixels and depths, making it necessary to fuse many timesteps over long-term history to generate a cost volume from a long history of image observations, compensating for the coarse but efficient matching resolution with a more optimal multi-view matching setup.
- GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning259
This work proposes two techniques to improve the discriminative feature learning for MOT by introducing a novel feature interaction mechanism by introducing the Graph Neural Network and proposes a novel joint feature extractor to learn appearance and motion features from 2D and 3D space simultaneously.
- r2p2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting259
A method to forecast a vehicle’s ego-motion as a distribution over spatiotemporal paths, conditioned on features embedded in an overhead map, and obtains expressions for the cross-entropy metrics that can be efficiently evaluated and differentiated, enabling stochastic-gradient optimization.
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 2026-10-14. 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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