Search Results for author:
Found 14 papers, 7 papers with code
WebSeg: Learning Semantic Segmentation from Web Searches
In this paper, we improve semantic segmentation by automatically learning from Flickr images associated with a particular keyword, without relying on any explicit user annotations, thus substantially alleviating the dependence on accurate annotations when compared to previous weakly supervised methods.
Three Birds One Stone: A General Architecture for Salient Object Segmentation, Edge Detection and Skeleton Extraction
Although these tasks are inherently very different, we show that our unified approach performs very well on all of them and works far better than current single-purpose state-of-the-art methods.
A Simple Pooling-Based Design for Real-Time Salient Object Detection
We further design a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down pathway.
Ranked #1 on
RGB Salient Object Detection
on SOD
EGNet:Edge Guidance Network for Salient Object Detection
In the second step, we integrate the local edge information and global location information to obtain the salient edge features.
Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton
To evaluate the performance of our proposed network on these tasks, we conduct exhaustive experiments on multiple representative datasets.
Improving Convolutional Networks With Self-Calibrated Convolutions
Recent advances on CNNs are mostly devoted to designing more complex architectures to enhance their representation learning capacity.
Centralized Information Interaction for Salient Object Detection
Our approach can cooperate with various existing U-shape-based salient object detection methods by substituting the connections between the bottom-up and top-down pathways.
Attention Mechanisms in Computer Vision: A Survey
Humans can naturally and effectively find salient regions in complex scenes.
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement
In this paper, we propose an efficient structure named Efficient Correspondence Transformer (ECO-TR) by finding correspondences in a coarse-to-fine manner, which significantly improves the efficiency of functional correspondence model.
SLAN: Self-Locator Aided Network for Cross-Modal Understanding
By aggregating cross-modal information, the region filter selects key regions and the region adaptor updates their coordinates with text guidance.
SLAN: Self-Locator Aided Network for Vision-Language Understanding
By aggregating vision-language information, the region filter selects key regions and the region adaptor updates their coordinates with text guidance.
Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes
Modern autonomous driving systems are typically divided into three main tasks: perception, prediction, and planning.
Ranked #1 on
Trajectory Planning
on nuScenes
Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation
State-of-the-art solutions adopt the DETR-like framework, and mainly develop the complex decoder, e. g., regarding pose estimation as keypoint box detection and combining with human detection in ED-Pose, hierarchically predicting with pose decoder and joint (keypoint) decoder in PETR.
