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Found 12 papers, 6 papers with code
Triplet Attention Transformer for Spatiotemporal Predictive Learning
Spatiotemporal predictive learning offers a self-supervised learning paradigm that enables models to learn both spatial and temporal patterns by predicting future sequences based on historical sequences.
Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions
This paper strives to resolve this problem by proposing a new instruct-ReID task that requires the model to retrieve images according to the given image or language instructions.
UniHCP: A Unified Model for Human-Centric Perceptions
When adapted to a specific task, UniHCP achieves new SOTAs on a wide range of human-centric tasks, e. g., 69. 8 mIoU on CIHP for human parsing, 86. 18 mA on PA-100K for attribute prediction, 90. 3 mAP on Market1501 for ReID, and 85. 8 JI on CrowdHuman for pedestrian detection, performing better than specialized models tailored for each task.
Ranked #1 on
Pose Estimation
on MS-COCO
Saliency Guided Contrastive Learning on Scene Images
Despite being feasible, recent works largely overlooked discovering the most discriminative regions for contrastive learning to object representations in scene images.
Unsupervised Prompt Tuning for Text-Driven Object Detection
In this paper, we delve into this problem and propose an Unsupervised Prompt Tuning framework for text-driven object detection, which is composed of two novel mean teaching mechanisms.
Learning Domain Adaptive Object Detection with Probabilistic Teacher
In addition, we conduct anchor adaptation in parallel with localization adaptation, since anchor can be regarded as a learnable parameter.
FocalClick: Towards Practical Interactive Image Segmentation
To make the model work with preexisting masks, we formulate a sub-task termed Interactive Mask Correction, and propose Progressive Merge as the solution.
Ranked #1 on
Interactive Segmentation
on GrabCut
(using extra training data)
Revisiting the Transferability of Supervised Pretraining: an MLP Perspective
The pretrain-finetune paradigm is a classical pipeline in visual learning.
Improving the Transferability of Supervised Pretraining with an MLP Projector
The pretrain-finetune paradigm is a classical pipeline in visual learning.
Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-Identification
To tackle the problem, we propose an online pseudo label generation by hierarchical cluster dynamics for adaptive ReID.
State-Aware Tracker for Real-Time Video Object Segmentation
For higher efficiency, SAT takes advantage of the inter-frame consistency and deals with each target object as a tracklet.
Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation
Compared with other semantic segmentation tasks, portrait segmentation requires both higher precision and faster inference speed.
