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Found 12 papers, 9 papers with code
PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition
In this paper, we further adapt the selective scanning process of Mamba to the visual domain, enhancing its ability to learn features from two-dimensional images by (i) a continuous 2D scanning process that improves spatial continuity by ensuring adjacency of tokens in the scanning sequence, and (ii) direction-aware updating which enables the model to discern the spatial relations of tokens by encoding directional information.
EgoPoseFormer: A Simple Baseline for Egocentric 3D Human Pose Estimation
We also show that our method can be seamlessly extended to monocular settings, which achieves state-of-the-art performance on the SceneEgo dataset, improving MPJPE by 25. 5mm (21% improvement) compared to the best existing method with only 60. 7% model parameters and 36. 4% FLOPs.
WidthFormer: Toward Efficient Transformer-based BEV View Transformation
In this work, we present WidthFormer, a novel transformer-based Bird's-Eye-View (BEV) 3D detection method tailored for real-time autonomous-driving applications.
GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation
In each GP Block, features are first grouped together by a fixed number of learnable group tokens; we then perform Group Propagation where global information is exchanged between the grouped features; finally, global information in the updated grouped features is returned back to the image features through a transformer decoder.
Plug and Play Active Learning for Object Detection
To overcome this challenge, we introduce Plug and Play Active Learning (PPAL), a simple and effective AL strategy for object detection.
DETRDistill: A Universal Knowledge Distillation Framework for DETR-families
Next, we propose a target-aware feature distillation to help the student model learn from the object-centric features of the teacher model.
Prediction-Guided Distillation for Dense Object Detection
Based on this, we propose Prediction-Guided Distillation (PGD), which focuses distillation on these key predictive regions of the teacher and yields considerable gains in performance over many existing KD baselines.
Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning
The goal of contrastive learning based pre-training is to leverage large quantities of unlabeled data to produce a model that can be readily adapted downstream.
Disentangle Your Dense Object Detector
Extensive experiments on MS COCO benchmark show that our approach can lead to 2. 0 mAP, 2. 4 mAP and 2. 2 mAP absolute improvements on RetinaNet, FCOS, and ATSS baselines with negligible extra overhead.
QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection
On the popular COCO dataset, the proposed method improves the detection mAP by 1. 0 and mAP-small by 2. 0, and the high-resolution inference speed is improved to 3. 0x on average.
Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation
Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data.
Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes
While visual object detection with deep learning has received much attention in the past decade, cases when heavy intra-class occlusions occur have not been studied thoroughly.
