Search Results for author:
Found 47 papers, 23 papers with code
Neural Reflectance Fields for Radio-Frequency Ray Tracing
We achieve this by translating the neural reflectance field from optics to RF domain by modelling both the amplitude and phase of RF signals to account for the multipath effects.
λ: A Benchmark for Data-Efficiency in Long-Horizon Indoor Mobile Manipulation Robotics
Learning to execute long-horizon mobile manipulation tasks is crucial for advancing robotics in household and workplace settings.
Evaluating Cognitive and Neuropsychological Assessments -- A Comprehensive Review
Cognitive impairments in older adults represent a significant public health concern, necessitating accurate diagnostic and monitoring strategies.
Immunogenic cell death triggered by pathogen ligands via host germ line-encoded receptors
In response to infections, host cells dictate a variety of cell death pathways, including apoptosis, pyroptosis, necrosis, and lysosomal cell death, which are essential for amplifying immune responses and controlling pathogen dissemination.
NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds
To this end, we introduce NovelGym, a flexible and adaptable ecosystem designed to simulate gridworld environments, serving as a robust platform for benchmarking reinforcement learning (RL) and hybrid planning and learning agents in open-world contexts.
Dynamic Causal Disentanglement Model for Dialogue Emotion Detection
Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables, enabling the accumulation of emotion-related information throughout the conversation.
Category Query Learning for Human-Object Interaction Classification
Unlike most previous HOI methods that focus on learning better human-object features, we propose a novel and complementary approach called category query learning.
Ranked #9 on
Human-Object Interaction Detection
on HICO-DET
SOLQ: Segmenting Objects by Learning Queries
Moreover, the joint learning of unified query representation can greatly improve the detection performance of DETR.
Ranked #5 on
Object Detection
on COCO minival
(AP75 metric)
MOTR: End-to-End Multiple-Object Tracking with Transformer
Temporal modeling of objects is a key challenge in multiple object tracking (MOT).
Ranked #1 on
Multi-Object Tracking
on MOT17
(e2e-MOT metric)
Multi-Object Tracking
Multiple Object Tracking with Transformer
+1
Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales
%We argue that such flexibility is also important for deep metric learning, because different visual concepts indeed correspond to different semantic scales.
Ranked #2 on
Metric Learning
on DyML-Animal
End-to-End Human Object Interaction Detection with HOI Transformer
We propose HOI Transformer to tackle human object interaction (HOI) detection in an end-to-end manner.
Ranked #32 on
Human-Object Interaction Detection
on HICO-DET
(using extra training data)
Prime-Aware Adaptive Distillation
With ten teacher-student combinations on six datasets, PAD promotes the performance of existing distillation methods and outperforms recent state-of-the-art methods.
Spherical Feature Transform for Deep Metric Learning
Consequently, the feature transform is performed by a rotation that respects the spherical data distributions.
Joint Multi-Dimension Pruning via Numerical Gradient Update
To tackle these three naturally different dimensions, we proposed a general framework by defining pruning as seeking the best pruning vector (i. e., the numerical value of layer-wise channel number, spacial size, depth) and construct a unique mapping from the pruning vector to the pruned network structures.
Angle-based Search Space Shrinking for Neural Architecture Search
Comprehensive experiments show that ABS can dramatically enhance existing NAS approaches by providing a promising shrunk search space.
Data Uncertainty Learning in Face Recognition
This work applies data uncertainty learning to face recognition, such that the feature (mean) and uncertainty (variance) are learnt simultaneously, for the first time.
Balanced Alignment for Face Recognition: A Joint Learning Approach
There lacks an understanding of how important face alignment is and how it should be performed, for recognition.
Circle Loss: A Unified Perspective of Pair Similarity Optimization
This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization
Therefore many modified normalization techniques have been proposed, which either fail to restore the performance of BN completely, or have to introduce additional nonlinear operations in inference procedure and increase huge consumption.
Vehicle Re-identification with Viewpoint-aware Metric Learning
This paper considers vehicle re-identification (re-ID) problem.
3D Dense Face Alignment via Graph Convolution Networks
Recently, 3D face reconstruction and face alignment tasks are gradually combined into one task: 3D dense face alignment.
Re-Identification Supervised Texture Generation
The estimation of 3D human body pose and shape from a single image has been extensively studied in recent years.
Single Path One-Shot Neural Architecture Search with Uniform Sampling
It is easy to train and fast to search.
Ranked #88 on
Neural Architecture Search
on ImageNet
(Accuracy metric)
Rethinking on Multi-Stage Networks for Human Pose Estimation
Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods.
Ranked #1 on
Pose Estimation
on COCO minival
Simple Baselines for Human Pose Estimation and Tracking
There has been significant progress on pose estimation and increasing interests on pose tracking in recent years.
Ranked #2 on
Keypoint Detection
on COCO test-dev
Towards High Performance Video Object Detection for Mobiles
In this paper, we present a light weight network architecture for video object detection on mobiles.
Learning Region Features for Object Detection
While most steps in the modern object detection methods are learnable, the region feature extraction step remains largely hand-crafted, featured by RoI pooling methods.
Pseudo Mask Augmented Object Detection
In this work, we present a novel and effective framework to facilitate object detection with the instance-level segmentation information that is only supervised by bounding box annotation.
Towards High Performance Video Object Detection
There has been significant progresses for image object detection in recent years.
Relation Networks for Object Detection
Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era.
Integral Human Pose Regression
State-of-the-art human pose estimation methods are based on heat map representation.
Ranked #23 on
Pose Estimation
on MPII Human Pose
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
Compositional Human Pose Regression
A central problem is that the structural information in the pose is not well exploited in the previous regression methods.
Ranked #36 on
Pose Estimation
on MPII Human Pose
Flow-Guided Feature Aggregation for Video Object Detection
The accuracy of detection suffers from degenerated object appearances in videos, e. g., motion blur, video defocus, rare poses, etc.
Ranked #27 on
Video Object Detection
on ImageNet VID
Deformable Convolutional Networks
Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules.
Ranked #3 on
Vessel Detection
on Vessel detection Dateset
Fully Convolutional Instance-aware Semantic Segmentation
It inherits all the merits of FCNs for semantic segmentation and instance mask proposal.
Ranked #100 on
Instance Segmentation
on COCO test-dev
Deep Feature Flow for Video Recognition
Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable.
Ranked #9 on
Video Semantic Segmentation
on Cityscapes val
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 #331 on
3D Human Pose Estimation
on Human3.6M
Attribute Recognition from Adaptive Parts
Previous part-based attribute recognition approaches perform part detection and attribute recognition in separate steps.
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.
Object Proposal by Multi-Branch Hierarchical Segmentation
Hierarchical segmentation based object proposal methods have become an important step in modern object detection paradigm.
Global Refinement of Random Forest
The global pruning is developed to reduce the model size as well as the over-fitting risk.
Cascaded Hand Pose Regression
We extends the previous 2D cascaded object pose regression work [9] in two aspects so that it works better for 3D articulated objects.
Realtime and Robust Hand Tracking from Depth
We present a realtime hand tracking system using a depth sensor.
Face Alignment at 3000 FPS via Regressing Local Binary Features
The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently.
Saliency Optimization from Robust Background Detection
However, their usage of boundary prior is very simple, fragile, and the integration with other cues is mostly heuristic.
Face alignment by explicit shape regression
We present a very efficient, highly accurate, “Explicit Shape Regression” approach for face alignment.
Ranked #36 on
Face Alignment
on WFLW
