Search Results for author:
Found 23 papers, 18 papers with code
Model-based Deep Hand Pose Estimation
For the first time, we show that embedding such a non-linear generative process in deep learning is feasible for hand pose estimation.
Deep Kinematic Pose Regression
In this work, we propose to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation.
Ranked #307 on
3D Human Pose Estimation
on Human3.6M
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure.
2D Pose Estimation
3D Multi-Person Pose Estimation (absolute)
+4
Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan or image.
StarMap for Category-Agnostic Keypoint and Viewpoint Estimation
Existing methods define semantic keypoints separately for each category with a fixed number of semantic labels in fixed indices.
Ranked #2 on
Keypoint Detection
on Pascal3D+
Bottom-up Object Detection by Grouping Extreme and Center Points
With the advent of deep learning, object detection drifted from a bottom-up to a top-down recognition problem.
Ranked #128 on
Object Detection
on COCO minival
Objects as Points
We model an object as a single point --- the center point of its bounding box.
Tracking Objects as Points
Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection.
Ranked #4 on
Multiple Object Tracking
on KITTI Tracking test
Center-based 3D Object Detection and Tracking
Three-dimensional objects are commonly represented as 3D boxes in a point-cloud.
Ranked #1 on
Robust 3D Object Detection
on nuScenes-C
Learning a unified label space
These labels span many diverse datasets with potentially inconsistent semantic labels.
Simple multi-dataset detection
Experiments show our learned taxonomy outperforms a expert-designed taxonomy in all datasets.
Probabilistic two-stage detection
We develop a probabilistic interpretation of two-stage object detection.
Ranked #20 on
Object Detection
on COCO-O
Multimodal Virtual Point 3D Detection
For autonomous driving, this means that large objects close to the sensors are easily visible, but far-away or small objects comprise only one measurement or two.
Ranked #63 on
3D Object Detection
on nuScenes
Detecting Twenty-thousand Classes using Image-level Supervision
For the first time, we train a detector with all the twenty-one-thousand classes of the ImageNet dataset and show that it generalizes to new datasets without finetuning.
Ranked #2 on
Open Vocabulary Object Detection
on OpenImages-v4
Global Tracking Transformers
The transformer encodes object features from all frames, and uses trajectory queries to group them into trajectories.
Ranked #12 on
Multi-Object Tracking
on SportsMOT
(using extra training data)
NMS Strikes Back
Our detector that trains Deformable-DETR with traditional IoU-based label assignment achieved 50. 2 COCO mAP within 12 epochs (1x schedule) with ResNet50 backbone, outperforming all existing traditional or transformer-based detectors in this setting.
Ranked #2 on
Object Detection
on COCO-O
(using extra training data)
How can objects help action recognition?
In this paper, we investigate how we can use knowledge of objects to design better video models, namely to process fewer tokens and to improve recognition accuracy.
Does Visual Pretraining Help End-to-End Reasoning?
A positive result would refute the common belief that explicit visual abstraction (e. g. object detection) is essential for compositional generalization on visual reasoning, and confirm the feasibility of a neural network "generalist" to solve visual recognition and reasoning tasks.
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation
With ResNet50 backbone, our MaskConver achieves 53. 6% PQ on the COCO panoptic val set, outperforming the modern convolution-based model, Panoptic FCN, by 9. 3% as well as transformer-based models such as Mask2Former (+1. 7% PQ) and kMaX-DeepLab (+0. 6% PQ).
Ranked #8 on
Panoptic Segmentation
on COCO test-dev
Pixel Aligned Language Models
When taking locations as inputs, the model performs location-conditioned captioning, which generates captions for the indicated object or region.
Distilling Vision-Language Models on Millions of Videos
Our best model outperforms state-of-the-art methods on MSR-VTT zero-shot text-to-video retrieval by 6%.
Streaming Dense Video Captioning
An ideal model for dense video captioning -- predicting captions localized temporally in a video -- should be able to handle long input videos, predict rich, detailed textual descriptions, and be able to produce outputs before processing the entire video.
