Search Results for author:
Found 22 papers, 11 papers with code
Place Anything into Any Video
This paper introduces a novel and efficient system named Place-Anything, which facilitates the insertion of any object into any video solely based on a picture or text description of the target object or element.
Track Anything: Segment Anything Meets Videos
Therefore, in this report, we propose Track Anything Model (TAM), which achieves high-performance interactive tracking and segmentation in videos.
Resource-Efficient RGBD Aerial Tracking
However, current research on aerial perception has mainly focused on limited categories, such as pedestrian or vehicle, and most scenes are captured in urban environments from a birds-eye view.
Learning Dual-Fused Modality-Aware Representations for RGBD Tracking
However, some existing RGBD trackers use the two modalities separately and thus some particularly useful shared information between them is ignored.
Prompting for Multi-Modal Tracking
Multi-modal tracking gains attention due to its ability to be more accurate and robust in complex scenarios compared to traditional RGB-based tracking.
Ranked #8 on
Rgb-T Tracking
on LasHeR
RGBD Object Tracking: An In-depth Review
Particularly, we are the first to provide depth quality evaluation and analysis of tracking results in depth-friendly scenarios in RGBD tracking.
Multi-modal Alignment using Representation Codebook
Aligning signals from different modalities is an important step in vision-language representation learning as it affects the performance of later stages such as cross-modality fusion.
Vision-Language Pre-Training with Triple Contrastive Learning
Besides CMA, TCL introduces an intra-modal contrastive objective to provide complementary benefits in representation learning.
Ranked #3 on
Zero-Shot Cross-Modal Retrieval
on COCO 2014
Depth-only Object Tracking
There are two potential reasons for the heuristics: 1) the lack of large RGBD tracking datasets to train deep RGBD trackers and 2) the long-term evaluation protocol of VOT RGBD that benefits from heuristics such as depth-based occlusion detection.
DepthTrack : Unveiling the Power of RGBD Tracking
RGBD (RGB plus depth) object tracking is gaining momentum as RGBD sensors have become popular in many application fields such as robotics. However, the best RGBD trackers are extensions of the state-of-the-art deep RGB trackers.
TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation
With the recent exponential increase in applying Vision Transformer (ViT) to vision tasks, the capability of ViT in adapting cross-domain knowledge, however, remains unexplored in the literature.
Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation
Recent studies imply that deep neural networks are vulnerable to adversarial examples -- inputs with a slight but intentional perturbation are incorrectly classified by the network.
DepthTrack: Unveiling the Power of RGBD Tracking
This can be explained by the fact that there are no sufficiently large RGBD datasets to 1) train "deep depth trackers" and to 2) challenge RGB trackers with sequences for which the depth cue is essential.
Hierarchical Graph Capsule Network
Graph Neural Networks (GNNs) draw their strength from explicitly modeling the topological information of structured data.
RetroXpert: Decompose Retrosynthesis Prediction like a Chemist
Retrosynthesis is the process of recursively decomposing target molecules into available building blocks.
Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation
Unsupervised domain adaptation enables to alleviate the need for pixel-wise annotation in the semantic segmentation.
Ranked #26 on
Domain Adaptation
on SYNTHIA-to-Cityscapes
Context-Aware Domain Adaptation in Semantic Segmentation
To achieve this goal, we design two cross-domain attention modules to adapt context dependencies from both spatial and channel views.
Ranked #27 on
Domain Adaptation
on SYNTHIA-to-Cityscapes
Aggregation Signature for Small Object Tracking
In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift.
Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation
We investigate the posterior collapse problem of current RNN-based VAEs for molecule sequence generation.
Maximal Margin Distribution Support Vector Regression with coupled Constraints-based Convex Optimization
Recent researchers reveal that maximizing the margin distribution of whole training dataset rather than the minimal margin of a few support vectors, is prone to achieve better generalization performance.
Artificial Intelligence in Intelligent Tutoring Robots: A Systematic Review and Design Guidelines
We first analyse the environment of the ITR and propose a relationship model for describing interactions of ITR with the students, the social milieu and the curriculum.
An Unified Intelligence-Communication Model for Multi-Agent System Part-I: Overview
Motivated by Shannon's model and recent rehabilitation of self-supervised artificial intelligence having a "World Model", this paper propose an unified intelligence-communication (UIC) model for describing a single agent and any multi-agent system.
