Search Results for author:
Found 12 papers, 7 papers with code
Multi-Object Tracking in the Dark
We conducted a comprehensive analysis of our LMOT dataset and proposed LTrack.
Transformer based Pluralistic Image Completion with Reduced Information Loss
The indices of quantized pixels are used as tokens for the inputs and prediction targets of the transformer.
Infrared Small Target Detection with Scale and Location Sensitivity
In this paper, we focus on boosting detection performance with a more effective loss but a simpler model structure.
Towards More Unified In-context Visual Understanding
Thanks to this design, the model is capable of handling in-context vision understanding tasks with multimodal output in a unified pipeline. Experimental results demonstrate that our model achieves competitive performance compared with specialized models and previous ICL baselines.
Siamese-DETR for Generic Multi-Object Tracking
Different from existing GMOT methods, which train a Single Object Tracking (SOT) based detector to detect interested objects and then apply a data association based MOT tracker to get the trajectories, we leverage the inherent object queries in DETR variants.
Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal
Inspired by the various successful applications of large-scale pre-trained models (e. g, CLIP), in this paper, we explore the potential benefits of them for this task through both spatial feature representation learning and semantic information embedding aspects: 1) for spatial feature representation learning, we design a Spatially-Adaptive Residual (\textbf{SAR}) Encoder to extract degraded areas adaptively.
UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection
UPCL is designed for learning the consistency-related representation with progressive optimized pseudo annotations.
Reduce Information Loss in Transformers for Pluralistic Image Inpainting
The indices of quantized pixels are used as tokens for the inputs and prediction targets of transformer.
Ranked #6 on
Seeing Beyond the Visible
on KITTI360-EX
Real-time Online Multi-Object Tracking in Compressed Domain
Recent online Multi-Object Tracking (MOT) methods have achieved desirable tracking performance.
Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation
In addition, such practice of re-identification still can not track those highly occluded objects when they are missed by the detector.
Ranked #7 on
Multi-Object Tracking
on MOT16
(using extra training data)
Joint Face Image Restoration and Frontalization for Recognition
First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart.
