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Found 21 papers, 7 papers with code
LVLM-empowered Multi-modal Representation Learning for Visual Place Recognition
We start from a new perspective and attempt to build a discriminative global representations by fusing image data and text descriptions of the the visual scene.
Window-to-Window BEV Representation Learning for Limited FoV Cross-View Geo-localization
Subsequently, the cross-attention is performed between the matched BEV and ground windows to learn the robust BEV representation.
MCMS: Multi-Category Information and Multi-Scale Stripe Attention for Blind Motion Deblurring
As a result, the model effectively improves motion deblurring by fusing the edge information of the high-frequency component and the structural information of the low-frequency component.
Single Image Super-Resolution Based on Global-Local Information Synergy
To overcome the existing challenges, a novel super-resolution reconstruction algorithm is proposed in this paper.
Empowering Large Language Models on Robotic Manipulation with Affordance Prompting
While large language models (LLMs) are successful in completing various language processing tasks, they easily fail to interact with the physical world by generating control sequences properly.
Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction
Previous studies have tried to tackle this problem by leveraging a portion of the trajectory data from the target domain to adapt the model.
XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
In this paper, we present XuanCe, a comprehensive and unified deep reinforcement learning (DRL) library designed to be compatible with PyTorch, TensorFlow, and MindSpore.
Sparse Pedestrian Character Learning for Trajectory Prediction
Specifically, TSNet learns the negative-removed characters in the sparse character representation stream to improve the trajectory embedding obtained in the trajectory representation stream.
Robust Navigation with Cross-Modal Fusion and Knowledge Transfer
We consider the problem of improving the generalization of mobile robots and achieving sim-to-real transfer for navigation skills.
LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph Generation
Besides, representation-level unbiased strategies endow LANDMARK the advantage of compatibility with other methods.
Transformer-Guided Convolutional Neural Network for Cross-View Geolocalization
Furthermore, we design a dual-branch Transformer head network to combine image features from multi-scale windows in order to improve details of the global feature representation.
Biasing Like Human: A Cognitive Bias Framework for Scene Graph Generation
Scene graph generation is a sophisticated task because there is no specific recognition pattern (e. g., "looking at" and "near" have no conspicuous difference concerning vision, whereas "near" could occur between entities with different morphology).
Ranked #1 on
Predicate Classification
on Visual Genome
(mR@20 metric)
Multi-agent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots
In this paper, a novel hybrid multi-robot motion planner that can be applied under non-communication and local observable conditions is presented.
A coarse-to-fine approach for dynamic-to-static image translation
Using the coarse predicted image, we explicitly infer a more accurate dynamic mask to identify both dynamic objects and their shadows, so that the task could be effectively converted to an image inpainting problem.
Learning Temporally Causal Latent Processes from General Temporal Data
In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified from their nonlinear mixtures.
Learning Temporally Latent Causal Processes from General Temporal Data
Our goal is to find time-delayed latent causal variables and identify their relations from temporal measured variables.
Multi-modal Visual Place Recognition in Dynamics-Invariant Perception Space
In this letter, we for the first time explore the use of multi-modal fusion of semantic and visual modalities in dynamics-invariant space to improve place recognition in dynamic environments.
Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool
The iVQA task is to generate a question that corresponds to a given image and answer pair.
iVQA: Inverse Visual Question Answering
The iVQA task is to generate a question that corresponds to a given image and answer pair.
Semantic Regularisation for Recurrent Image Annotation
We propose a simple modification to the design pattern that makes learning more effective and efficient.
Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps
Crowd counting is an important task in computer vision, which has many applications in video surveillance.
