no code implementations • 2 May 2024 • Srikanth Malla, Joon Hee Choi, Chiho Choi
Adapting pre-trained large language models to different domains in natural language processing requires two key considerations: high computational demands and model's inability to continual adaptation.
1 code implementation • 28 Mar 2024 • Sangjae Bae, David Isele, Alireza Nakhaei, Peng Xu, Alexandre Miranda Anon, Chiho Choi, Kikuo Fujimura, Scott Moura
This paper presents an online smooth-path lane-change control framework.
1 code implementation • 31 Oct 2023 • Ce Zhang, Changcheng Fu, Shijie Wang, Nakul Agarwal, Kwonjoon Lee, Chiho Choi, Chen Sun
To recognize and predict human-object interactions, we use a Transformer-based neural architecture which allows the "retrieval" of relevant objects for action anticipation at various time scales.
Ranked #2 on Long Term Action Anticipation on Ego4D
1 code implementation • 12 Sep 2023 • Enna Sachdeva, Nakul Agarwal, Suhas Chundi, Sean Roelofs, Jiachen Li, Mykel Kochenderfer, Chiho Choi, Behzad Dariush
The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) may largely depend on their acceptance by society, for which their perceived trustworthiness and interpretability to riders are crucial.
1 code implementation • CVPR 2023 • Yi Xu, Armin Bazarjani, Hyung-gun Chi, Chiho Choi, Yun Fu
As far as we know, this is the first work to address the lack of benchmarks and techniques for trajectory imputation and prediction in a unified manner.
no code implementations • CVPR 2023 • Hyung-gun Chi, Kwonjoon Lee, Nakul Agarwal, Yi Xu, Karthik Ramani, Chiho Choi
SALF is challenging because it requires understanding the underlying physics of video observations to predict future action locations accurately.
no code implementations • CVPR 2023 • Harshayu Girase, Nakul Agarwal, Chiho Choi, Karttikeya Mangalam
We present RAFTformer, a real-time action forecasting transformer for latency aware real-world action forecasting applications.
no code implementations • 22 Sep 2022 • Srikanth Malla, Chiho Choi, Isht Dwivedi, Joon Hee Choi, Jiachen Li
We make this data available to the community for further research.
no code implementations • 22 Aug 2022 • Enna Sachdeva, Chiho Choi
DIDER discovers an interpretable sequence of inter-agent interactions by disentangling the task of latent interaction prediction into sub-interaction prediction and duration estimation.
no code implementations • 28 Mar 2022 • Lingfeng Sun, Chen Tang, Yaru Niu, Enna Sachdeva, Chiho Choi, Teruhisa Misu, Masayoshi Tomizuka, Wei Zhan
To address these issues, we propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels.
no code implementations • CVPR 2022 • Reza Ghoddoosian, Isht Dwivedi, Nakul Agarwal, Chiho Choi, Behzad Dariush
Experimental results show efficacy of the proposed methods both qualitatively and quantitatively in two domains of cooking and assembly.
no code implementations • 5 Mar 2022 • Jiachen Li, Haiming Gang, Hengbo Ma, Masayoshi Tomizuka, Chiho Choi
We propose a novel approach for important object identification in egocentric driving scenarios with relational reasoning on the objects in the scene.
no code implementations • CVPR 2022 • Hengbo Ma, Jiachen Li, Ramtin Hosseini, Masayoshi Tomizuka, Chiho Choi
Obtaining accurate and diverse human motion prediction is essential to many industrial applications, especially robotics and autonomous driving.
no code implementations • ICCV 2021 • Harshayu Girase, Haiming Gang, Srikanth Malla, Jiachen Li, Akira Kanehara, Karttikeya Mangalam, Chiho Choi
We also propose a model that jointly performs trajectory and intention prediction, showing that recurrently reasoning about intention can assist with trajectory prediction.
no code implementations • ICCV 2021 • Jiachen Li, Fan Yang, Hengbo Ma, Srikanth Malla, Masayoshi Tomizuka, Chiho Choi
Motion forecasting plays a significant role in various domains (e. g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of historical observations.
no code implementations • CVPR 2021 • Chiho Choi, Joon Hee Choi, Jiachen Li, Srikanth Malla
At test time, a single input modality (e. g., LiDAR data) is required to generate predictions from the input perspective (i. e., in the LiDAR space), while taking advantages from the model trained with multiple sensor modalities.
no code implementations • 10 Nov 2020 • Srikanth Malla, Chiho Choi, Behzad Dariush
This paper considers the problem of multi-modal future trajectory forecast with ranking.
no code implementations • 13 Apr 2020 • Isht Dwivedi, Srikanth Malla, Behzad Dariush, Chiho Choi
Third, the semantic context of the scene are modeled and take into account the environmental constraints that potentially influence the future motion.
no code implementations • 1 Apr 2020 • Chiho Choi, Joon Hee Choi, Srikanth Malla, Jiachen Li
At test time, a single input modality (e. g., LiDAR data) is required to generate predictions from the input perspective (i. e., in the LiDAR space), while taking advantages from the model trained with multiple sensor modalities.
no code implementations • NeurIPS 2020 • Jiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choi
In this paper, we propose a generic trajectory forecasting framework (named EvolveGraph) with explicit relational structure recognition and prediction via latent interaction graphs among multiple heterogeneous, interactive agents.
Ranked #12 on Trajectory Prediction on Stanford Drone
no code implementations • CVPR 2020 • Srikanth Malla, Behzad Dariush, Chiho Choi
In an attempt to address this problem, we introduce TITAN (Trajectory Inference using Targeted Action priors Network), a new model that incorporates prior positions, actions, and context to forecast future trajectory of agents and future ego-motion.
no code implementations • 18 Nov 2019 • Shan Su, Cheng Peng, Jianbo Shi, Chiho Choi
From the generated potential fields, we further estimate future motion direction and speed, which are modeled as Gaussian distributions to account for the multi-modal nature of the problem.
no code implementations • 17 Sep 2019 • Srikanth Malla, Isht Dwivedi, Behzad Dariush, Chiho Choi
In the proposed approach, a predictive distribution of future forecast is jointly modeled with the uncertainty of predictions.
1 code implementation • 9 Sep 2019 • Sangjae Bae, Dhruv Saxena, Alireza Nakhaei, Chiho Choi, Kikuo Fujimura, Scott Moura
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers.
no code implementations • 31 Jul 2019 • Chiho Choi, Srikanth Malla, Abhishek Patil, Joon Hee Choi
We propose a Deep RObust Goal-Oriented trajectory prediction Network (DROGON) for accurate vehicle trajectory prediction by considering behavioral intentions of vehicles in traffic scenes.
no code implementations • ICCV 2019 • Chiho Choi, Behzad Dariush
Inferring relational behavior between road users as well as road users and their surrounding physical space is an important step toward effective modeling and prediction of navigation strategies adopted by participants in road scenes.
no code implementations • ICLR 2019 • Min Liu, Fupin Yao, Chiho Choi, Sinha Ayan, Karthik Ramani
The ground-breaking performance obtained by deep convolutional neural networks (CNNs) for image processing tasks is inspiring research efforts attempting to extend it for 3D geometric tasks.
2 code implementations • 19 Sep 2018 • Yu Yao, Mingze Xu, Chiho Choi, David J. Crandall, Ella M. Atkins, Behzad Dariush
Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving.
no code implementations • ICCV 2017 • Chiho Choi, Sang Ho Yoon, Chin-Ning Chen, Karthik Ramani
Our main insight is that the shape of an object causes a configuration of the hand in the form of a hand grasp.
no code implementations • ICCV 2017 • Chiho Choi, Sangpil Kim, Karthik Ramani
As an additional modality to depth data, we present a function of geometric properties on the surface of the hand described by heat diffusion.
no code implementations • CVPR 2016 • Ayan Sinha, Chiho Choi, Karthik Ramani
Our matrix completion algorithm uses these 'spatio-temporal' activation features and the corresponding known pose parameter values to to estimate the unknown pose parameters of the input feature vector.
no code implementations • ICCV 2015 • Chiho Choi, Ayan Sinha, Joon Hee Choi, Sujin Jang, Karthik Ramani
Specifically, we recast the hand pose estimation problem as the cold-start problem for a new user with unknown item ratings in a recommender system.