Search Results for author:
Found 7 papers, 4 papers with code
Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians
State-of-the-art pedestrian detectors have performed promisingly on non-occluded pedestrians, yet they are still confronted by heavy occlusions.
Ranked #18 on
Pedestrian Detection
on Caltech
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation
In the classification tree, as the number of parent class nodes are significantly less, their logits are less noisy and can be utilized to suppress the wrong/noisy logits existed in the fine-grained class nodes.
Ranked #5 on
Few-Shot Object Detection
on LVIS v1.0 val
Stacked Homography Transformations for Multi-View Pedestrian Detection
This task is confronted with two challenges: how to establish the 3D correspondences from views to the BEV map and how to assemble occupancy information across views.
Ranked #7 on
Multiview Detection
on MultiviewX
Track to Detect and Segment: An Online Multi-Object Tracker
Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking.
Ranked #1 on
Instance Segmentation
on nuScenes
Efficient Video Instance Segmentation via Tracklet Query and Proposal
In addition, VisTR is not fully end-to-end learnable in multiple video clips as it requires a hand-crafted data association to link instance tracklets between successive clips.
Deformable VisTR: Spatio temporal deformable attention for video instance segmentation
To improve the training efficiency, we propose Deformable VisTR, leveraging spatio-temporal deformable attention module that only attends to a small fixed set of key spatio-temporal sampling points around a reference point.
GRiT: A Generative Region-to-text Transformer for Object Understanding
Specifically, GRiT consists of a visual encoder to extract image features, a foreground object extractor to localize objects, and a text decoder to generate open-set object descriptions.
Ranked #2 on
Dense Captioning
on Visual Genome
