Search Results for author:
Found 23 papers, 18 papers with code
WidthFormer: Toward Efficient Transformer-based BEV View Transformation
We present WidthFormer, a novel transformer-based module to compute Bird's-Eye-View (BEV) representations from multi-view cameras for real-time autonomous-driving applications.
EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction
Motion prediction is a crucial task in autonomous driving, and one of its major challenges lands in the multimodality of future behaviors.
Sparse4D v3: Advancing End-to-End 3D Detection and Tracking
We introduce two auxiliary training tasks (Temporal Instance Denoising and Quality Estimation) and propose decoupled attention to make structural improvements, leading to significant enhancements in detection performance.
Sparse4D v2: Recurrent Temporal Fusion with Sparse Model
Firstly, it reduces the computational complexity of temporal fusion from $O(T)$ to $O(1)$, resulting in significant improvements in inference speed and memory usage.
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.
Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion
Bird-eye-view (BEV) based methods have made great progress recently in multi-view 3D detection task.
Ranked #10 on
Robust Camera Only 3D Object Detection
on nuScenes-C
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.
Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregation
Based on deep networks, video object detection is actively studied for pushing the limits of detection speed and accuracy.
Boundary-preserving Mask R-CNN
Besides, it is not surprising to observe that BMask R-CNN obtains more obvious improvement when the evaluation criterion requires better localization (e. g., AP$_{75}$) as shown in Fig. 1.
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
By combining the new CS-NL prior with local and in-scale non-local priors in a powerful recurrent fusion cell, we can find more cross-scale feature correlations within a single low-resolution (LR) image.
Ranked #10 on
Image Super-Resolution
on Manga109 - 3x upscaling
Multi-object Tracking via End-to-end Tracklet Searching and Ranking
Recent works in multiple object tracking use sequence model to calculate the similarity score between the detections and the previous tracklets.
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration
Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency.
RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation
To reciprocate these two tasks, we design a two-stream structure to learn features on both the object level (i. e., bounding boxes) and the pixel level (i. e., instance masks) jointly.
Ranked #94 on
Instance Segmentation
on COCO test-dev
Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds
Varying density of point clouds increases the difficulty of 3D detection.
Real Time Visual Tracking using Spatial-Aware Temporal Aggregation Network
Our tracker achieves leading performance in OTB2013, OTB2015, VOT2015, VOT2016 and LaSOT, and operates at a real-time speed of 26 FPS, which indicates our method is effective and practical.
Object Detection in Video with Spatial-temporal Context Aggregation
Without any bells and whistles, our method obtains 80. 3\% mAP on the ImageNet VID dataset, which is superior over the previous state-of-the-arts.
Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation
Video object segmentation (VOS) aims at pixel-level object tracking given only the annotations in the first frame.
Ranked #1 on
Visual Object Tracking
on YouTube-VOS 2018
(Jaccard (Seen) metric)
Mask Scoring R-CNN
In this paper, we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks.
Ranked #75 on
Instance Segmentation
on COCO minival
CCNet: Criss-Cross Attention for Semantic Segmentation
Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage.
Ranked #7 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking
The separation of the task requires to define a hand-crafted training goal in affinity learning stage and a hand-crafted cost function of data association stage, which prevents the tracking goals from learning directly from the feature.
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation
Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera.
DenseBox: Unifying Landmark Localization with End to End Object Detection
How can a single fully convolutional neural network (FCN) perform on object detection?
Incorporating Structural Alternatives and Sharing into Hierarchy for Multiclass Object Recognition and Detection
This paper proposes a reconfigurable model to recognize and detect multiclass (or multiview) objects with large variation in appearance.
