Search Results for author:
Found 15 papers, 6 papers with code
Vision-and-Language Navigation via Causal Learning
In the pursuit of robust and generalizable environment perception and language understanding, the ubiquitous challenge of dataset bias continues to plague vision-and-language navigation (VLN) agents, hindering their performance in unseen environments.
Causality-based Cross-Modal Representation Learning for Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) has gained significant research interest in recent years due to its potential applications in real-world scenarios.
CLIPose: Category-Level Object Pose Estimation with Pre-trained Vision-Language Knowledge
Inspired by this motivation, we propose CLIPose, a novel 6D pose framework that employs the pre-trained vision-language model to develop better learning of object category information, which can fully leverage abundant semantic knowledge in image and text modalities.
TransPose: 6D Object Pose Estimation with Geometry-Aware Transformer
To improve robustness to occlusion, we adopt Transformer to perform the exchange of global information, making each local feature contains global information.
InstructDET: Diversifying Referring Object Detection with Generalized Instructions
In order to encompass common detection expressions, we involve emerging vision-language model (VLM) and large language model (LLM) to generate instructions guided by text prompts and object bbxs, as the generalizations of foundation models are effective to produce human-like expressions (e. g., describing object property, category, and relationship).
Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation Learning
As the most essential property in a video, motion information is critical to a robust and generalized video representation.
PASTS: Progress-Aware Spatio-Temporal Transformer Speaker For Vision-and-Language Navigation
The experimental results demonstrate that PASTS outperforms all existing speaker models and successfully improves the performance of previous VLN models, achieving state-of-the-art performance on the standard Room-to-Room (R2R) dataset.
A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) is a realistic but challenging task that requires an agent to locate the target region using verbal and visual cues.
MLANet: Multi-Level Attention Network with Sub-instruction for Continuous Vision-and-Language Navigation
For a better performance in continuous VLN, we design a multi-level instruction understanding procedure and propose a novel model, Multi-Level Attention Network (MLANet).
Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation
We propose a meta-ability decoupling (MAD) paradigm, which brings together various object navigation methods in an architecture system, allowing them to mutually enhance each other and evolve together.
Search for or Navigate to? Dual Adaptive Thinking for Object Navigation
After seeing the target, we remember the target location and navigate to.
Unbiased Directed Object Attention Graph for Object Navigation
Thus, in this paper, we propose a directed object attention (DOA) graph to guide the agent in explicitly learning the attention relationships between objects, thereby reducing the object attention bias.
PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds
Recent machine learning-based multi-object tracking (MOT) frameworks are becoming popular for 3-D point clouds.
Unsupervised Learning of Depth and Deep Representation for Visual Odometry from Monocular Videos in a Metric Space
In contrast to the previous methods, our proposed method calculates the camera motion with a direct method rather than regressing the ego-motion from the pose network.
Focal Loss in 3D Object Detection
Inspired by the recent use of focal loss in image-based object detection, we extend this hard-mining improvement of binary cross entropy to point-cloud-based object detection and conduct experiments to show its performance based on two different 3D detectors: 3D-FCN and VoxelNet.
