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Found 70 papers, 22 papers with code
Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real
Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decision making.
Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios
Modified methods based on PGM, NN and IRL are provided to generate probabilistic reaction predictions in an exemplar scenario of nudging from a highway ramp.
Weakly Supervised Scene Parsing with Point-based Distance Metric Learning
Semantic scene parsing is suffering from the fact that pixel-level annotations are hard to be collected.
Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning
In order to enable high-quality decision making and motion planning of intelligent systems such as robotics and autonomous vehicles, accurate probabilistic predictions for surrounding interactive objects is a crucial prerequisite.
Text Guided Person Image Synthesis
This paper presents a novel method to manipulate the visual appearance (pose and attribute) of a person image according to natural language descriptions.
Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling
In order to tackle the task of probabilistic prediction for multiple, interactive entities, we propose a coordination and trajectory prediction system (CTPS), which has a hierarchical structure including a macro-level coordination recognition module and a micro-level subtle pattern prediction module which solves a probabilistic generation task.
Conditional Generative Neural System for Probabilistic Trajectory Prediction
Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are critical for intelligent systems such as autonomous vehicles and wheeled mobile robotics navigating in complex scenarios to achieve safe and high-quality decision making, motion planning and control.
Ranked #14 on
Trajectory Prediction
on Stanford Drone
SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection
Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, and high inference accuracy.
Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving
The framework can incorporate an arbitrary prediction model as the implicit proposal distribution of the CMSMC method.
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
Object detection and tracking are challenging tasks for resource-constrained embedded systems.
Multi-task Batch Reinforcement Learning with Metric Learning
To perform well, the policy must infer the task identity from collected transitions by modelling its dependency on states, actions and rewards.
Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network
Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are indispensable for intelligent mobile systems (like autonomous vehicles and social robots) to achieve safe and high-quality planning when they navigate in highly interactive and crowded scenarios.
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
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
Shared Cross-Modal Trajectory Prediction for Autonomous Driving
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.
Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships
Deep reinforcement learning (DRL) provides a promising way for learning navigation in complex autonomous driving scenarios.
Shared Cross-Modal Trajectory Prediction for Autonomous Driving
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.
Minimal Geometry-Distortion Constraint for Unsupervised Image-to-Image Translation
Unsupervised image-to-image (I2I) translation, which aims to learn a domain mapping function without paired data, is very challenging because the function is highly under-constrained.
Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking
Due to the existence of frequent interactions and uncertainty in the scene evolution, it is desired for the prediction system to enable relational reasoning on different entities and provide a distribution of future trajectories for each agent.
Escaping the Big Data Paradigm with Compact Transformers
Our models are flexible in terms of model size, and can have as little as 0. 28M parameters while achieving competitive results.
Ranked #1 on
Image Classification
on Flowers-102
(using extra training data)
Fine-Grained Image Classification
Superpixel Image Classification
Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection
Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by well-designed assignment methods based on the Intersection-over-Union~(IoU) metric.
RSCA: Real-time Segmentation-based Context-Aware Scene Text Detection
Segmentation-based scene text detection methods have been widely adopted for arbitrary-shaped text detection recently, since they make accurate pixel-level predictions on curved text instances and can facilitate real-time inference without time-consuming processing on anchors.
Spectral Temporal Graph Neural Network for Trajectory Prediction
To this end, we propose a Spectral Temporal Graph Neural Network (SpecTGNN), which can capture inter-agent correlations and temporal dependency simultaneously in frequency domain in addition to time domain.
MSN: Efficient Online Mask Selection Network for Video Instance Segmentation
Our method improves the masks from segmentation and propagation branches in an online manner using the Mask Selection Network (MSN) hence limiting the noise accumulation during mask tracking.
Ranked #26 on
Video Instance Segmentation
on YouTube-VIS validation
Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks
Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years.
Orientation-Aware Planning for Parallel Task Execution of Omni-Directional Mobile Robot
Omni-directional mobile robot (OMR) systems have been very popular in academia and industry for their superb maneuverability and flexibility.
RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting
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.
LOKI: Long Term and Key Intentions for Trajectory Prediction
We also propose a model that jointly performs trajectory and intention prediction, showing that recurrently reasoning about intention can assist with trajectory prediction.
ConvMLP: Hierarchical Convolutional MLPs for Vision
MLP-based architectures, which consist of a sequence of consecutive multi-layer perceptron blocks, have recently been found to reach comparable results to convolutional and transformer-based methods.
Ranked #8 on
Image Classification
on Flowers-102
(using extra training data)
Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting
Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a long-standing challenge.
SS-MAIL: Self-Supervised Multi-Agent Imitation Learning
We show that our method (SS-MAIL) outperforms prior state-of-the-art methods on real-world prediction tasks, as well as on custom-designed synthetic experiments.
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction
Improving the sample efficiency of reinforcement learning algorithms requires effective exploration.
SeMask: Semantically Masked Transformers for Semantic Segmentation
To achieve this, we propose SeMask, a simple and effective framework that incorporates semantic information into the encoder with the help of a semantic attention operation.
Ranked #10 on
Semantic Segmentation
on Cityscapes val
Multi-Objective Diverse Human Motion Prediction With Knowledge Distillation
Obtaining accurate and diverse human motion prediction is essential to many industrial applications, especially robotics and autonomous driving.
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint
Unsupervised image-to-image (I2I) translation aims to learn a domain mapping function that can preserve the semantics of the input images without paired data.
Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identification
Assisted with the camera-aware proxies, we design two proxy-level contrastive learning losses that are, respectively, based on offline and online association results.
ConvNeXt-backbone HoVerNet for nuclei segmentation and classification
This manuscript gives a brief description of the algorithm used to participate in CoNIC Challenge 2022.
Important Object Identification with Semi-Supervised Learning for Autonomous Driving
We propose a novel approach for important object identification in egocentric driving scenarios with relational reasoning on the objects in the scene.
BCOT: A Markerless High-Precision 3D Object Tracking Benchmark
Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes.
Neighborhood Attention Transformer
We present Neighborhood Attention (NA), the first efficient and scalable sliding-window attention mechanism for vision.
Ranked #119 on
Semantic Segmentation
on ADE20K
Symbolic Expression Transformer: A Computer Vision Approach for Symbolic Regression
Symbolic Regression (SR) is a type of regression analysis to automatically find the mathematical expression that best fits the data.
Causal Balancing for Domain Generalization
While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations.
Point-to-Box Network for Accurate Object Detection via Single Point Supervision
However, the performance gap between point supervised object detection (PSOD) and bounding box supervised detection remains large.
EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction
While the modeling of pair-wise relations has been widely studied in multi-agent interacting systems, its ability to capture higher-level and larger-scale group-wise activities is limited.
Interaction Modeling with Multiplex Attention
Modeling multi-agent systems requires understanding how agents interact.
VMFormer: End-to-End Video Matting with Transformer
In this paper, we propose VMFormer: a transformer-based end-to-end method for video matting.
DRAMA: Joint Risk Localization and Captioning in Driving
We make this data available to the community for further research.
Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments
Detection and segmentation of moving obstacles, along with prediction of the future occupancy states of the local environment, are essential for autonomous vehicles to proactively make safe and informed decisions.
Learning Physical Dynamics with Subequivariant Graph Neural Networks
Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics.
OneFormer: One Transformer to Rule Universal Image Segmentation
However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance.
Ranked #1 on
Panoptic Segmentation
on COCO minival
Transformer Based Multi-Grained Features for Unsupervised Person Re-Identification
To this end, we build a dual-branch network architecture based upon a modified Vision Transformer (ViT).
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints
Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas.
Matting Anything
In this paper, we propose the Matting Anything Model (MAM), an efficient and versatile framework for estimating the alpha matte of any instance in an image with flexible and interactive visual or linguistic user prompt guidance.
Robust Driving Policy Learning with Guided Meta Reinforcement Learning
Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior policy to control social vehicles in the training environment.
Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning
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.
For A More Comprehensive Evaluation of 6DoF Object Pose Tracking
The limitations of previous scoring methods and error metrics are analyzed, based on which we introduce our improved evaluation methods.
Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments
We introduce the Scene Informer, a unified approach for predicting both observed agent trajectories and inferring occlusions in a partially observable setting.
Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning
Inspired by their success in language tasks, existing research has leveraged LLMs in embodied instruction following and task planning.
Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification
Although prompt learning has enabled a recent work named CLIP-ReID to achieve promising performance, the underlying mechanisms and the necessity of prompt learning remain unclear due to the absence of semantic labels in ReID tasks.
Ranked #1 on
Unsupervised Vehicle Re-Identification
on VeRi-776
Contrastive Learning
Unsupervised Person Re-Identification
+1
Video Instance Matting
To remedy this deficiency, we propose Video Instance Matting~(VIM), that is, estimating alpha mattes of each instance at each frame of a video sequence.
More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering
While most existing works on LLM prompting techniques focus only on how to select a better set of data samples inside one single prompt input (In-Context Learning or ICL), why can not we design and leverage multiple prompts together to further improve the LLM's performance?
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Moreover, we propose an interactivity estimation mechanism based on the difference between predicted trajectories in these two situations, which indicates the degree of influence of the ego agent on other agents.
Disentangled Neural Relational Inference for Interpretable Motion Prediction
The results show superior performance compared to existing methods in modeling spatio-temporal relations, motion prediction, and identifying time-invariant latent features.
Graph Q-Learning for Combinatorial Optimization
Graph-structured data is ubiquitous throughout natural and social sciences, and Graph Neural Networks (GNNs) have recently been shown to be effective at solving prediction and inference problems on graph data.
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation
In this paper, we propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures, and we demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games
Offline learning has become widely used due to its ability to derive effective policies from offline datasets gathered by expert demonstrators without interacting with the environment directly.
MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications.
Reward Guided Latent Consistency Distillation
By distilling a latent consistency model (LCM) from a pre-trained teacher latent diffusion model (LDM), LCD facilitates the generation of high-fidelity images within merely 2 to 4 inference steps.
CMP: Cooperative Motion Prediction with Multi-Agent Communication
Building on top of cooperative perception, this paper explores the feasibility and effectiveness of cooperative motion prediction.
