Search Results for author:
Found 47 papers, 16 papers with code
EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation
In this paper, we have a critical insight that improving the feed-forward network (FFN) in BERT has a higher gain than improving the multi-head attention (MHA) since the computational cost of FFN is 2$\sim$3 times larger than MHA.
Voxel Transformer for 3D Object Detection
We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds.
Ranked #1 on
3D Object Detection
on waymo vehicle
(L1 mAP metric)
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
To resolve the problems, we propose a novel second-stage module, named pyramid RoI head, to adaptively learn the features from the sparse points of interest.
Ranked #1 on
3D Object Detection
on waymo vehicle
(AP metric)
Adversarial Robustness for Unsupervised Domain Adaptation
Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models.
MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving
By pre-training on SODA10M, a large-scale autonomous driving dataset, MultiSiam exceeds the ImageNet pre-trained MoCo-v2, demonstrating the potential of domain-specific pre-training.
Unbiased IoU for Spherical Image Object Detection
In this paper, we first identify that spherical rectangles are unbiased bounding boxes for objects in spherical images, and then propose an analytical method for IoU calculation without any approximations.
G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation
In this paper, we investigate the knowledge distillation (KD) strategy for object detection and propose an effective framework applicable to both homogeneous and heterogeneous student-teacher pairs.
NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models
Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks.
Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining
In this paper, we investigate a more realistic setting that aims to perform weakly-supervised multi-modal instance-level product retrieval among fine-grained product categories.
AutoBERT-Zero: Evolving BERT Backbone from Scratch
We optimize both the search algorithm and evaluation of candidate models to boost the efficiency of our proposed OP-NAS.
SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving
Aiming at facilitating a real-world, ever-evolving and scalable autonomous driving system, we present a large-scale benchmark for standardizing the evaluation of different self-supervised and semi-supervised approaches by learning from raw data, which is the first and largest benchmark to date.
One Million Scenes for Autonomous Driving: ONCE Dataset
To facilitate future research on exploiting unlabeled data for 3D detection, we additionally provide a benchmark in which we reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation
For student morphism, weight inheritance strategy is adopted, allowing the student to flexibly update its architecture while fully utilize the predecessor's weights, which considerably accelerates the search; To facilitate dynamic distillation, an elastic teacher pool is trained via integrated progressive shrinking strategy, from which teacher detectors can be sampled without additional cost in subsequent searches.
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search
While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks.
BWCP: Probabilistic Learning-to-Prune Channels for ConvNets via Batch Whitening
To address this problem, we develop a probability-based pruning algorithm, called batch whitening channel pruning (BWCP), which can stochastically discard unimportant channels by modeling the probability of a channel being activated.
Effective Sparsification of Neural Networks with Global Sparsity Constraint
Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real-world deployments.
Unsupervised Pretraining for Object Detection by Patch Reidentification
Secondly, patch Re-ID is performed in Deeply Unsupervised manner to learn multi-level representations, appealing to object detection.
SparseBERT: Rethinking the Importance Analysis in Self-attention
A surprising result is that diagonal elements in the attention map are the least important compared with other attention positions.
L2E: Learning to Exploit Your Opponent
In this work, we propose a novel Learning to Exploit (L2E) framework for implicit opponent modeling.
Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search
For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges.
DetCo: Unsupervised Contrastive Learning for Object Detection
Unlike most recent methods that focused on improving accuracy of image classification, we present a novel contrastive learning approach, named DetCo, which fully explores the contrasts between global image and local image patches to learn discriminative representations for object detection.
DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning
This paper introduces DeepReduce, a versatile framework for the compressed communication of sparse tensors, tailored for distributed deep learning.
Segmenting Transparent Object in the Wild with Transformer
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection
Here we present a novel self-supervised 3D Object detection framework that seamlessly integrates the geometry-aware contrast and clustering harmonization to lift the unsupervised 3D representation learning, named GCC-3D.
TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture Search
While existing NAS methods mostly design architectures on one single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks.
C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing
The semi-supervised semantic segmentation methods utilize the unlabeled data to increase the feature discriminative ability to alleviate the burden of the annotated data.
NASOA: Towards Faster Task-oriented Online Fine-tuning
The resulting model zoo is more training efficient than SOTA NAS models, e. g. 6x faster than RegNetY-16GF, and 1. 7x faster than EfficientNetB3.
OpenHoldem: An Open Toolkit for Large-Scale Imperfect-Information Game Research
Owning to the unremitting efforts by a few institutes, significant progress has recently been made in designing superhuman AIs in No-limit Texas Hold'em (NLTH), the primary testbed for large-scale imperfect-information game research.
Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation
Panoptic segmentation that unifies instance segmentation and semantic segmentation has recently attracted increasing attention.
Ranked #12 on
Panoptic Segmentation
on COCO test-dev
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation
In this work, we propose an efficient, cooperative and highly automated framework to simultaneously search for all main components including backbone, segmentation branches, and feature fusion module in a unified panoptic segmentation pipeline based on the prevailing one-shot Network Architecture Search (NAS) paradigm.
Driver Anomaly Detection: A Dataset and Contrastive Learning Approach
For this task, we introduce a new video-based benchmark, the Driver Anomaly Detection (DAD) dataset, which contains normal driving videos together with a set of anomalous actions in its training set.
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
In this paper, we propose a novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information while unifying both architecture search and post-processing on curve lane predictions via point blending.
Ranked #12 on
Lane Detection
on CULane
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search
In spite of its remarkable progress, many algorithms are restricted to particular search spaces.
Ranked #3 on
Neural Architecture Search
on NAS-Bench-201, ImageNet-16-120
(Accuracy (val) metric)
AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling
By carefully analyzing the existing bounding box patterns on the feature hierarchy, we design a flexible and tight hyper-parameter space for anchor configurations.
JGR-P2O: Joint Graph Reasoning based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image
The key ideas are two-fold: a) explicitly modeling the dependencies among joints and the relations between the pixels and the joints for better local feature representation learning; b) unifying the dense pixel-wise offset predictions and direct joint regression for end-to-end training.
ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection
Is a hand-crafted detection network tailored for natural image undoubtedly good enough over a discrepant medical lesion domain?
Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN
Finally, an InterDomain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts globally.
EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement
In this paper, we study the hybrid-supervised object detection problem, aiming to train a high quality detector with only a limited amount of fullyannotated data and fully exploiting cheap data with imagelevel labels.
SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection
In this paper, we present a two-stage coarse-to-fine searching strategy named Structural-to-Modular NAS (SM-NAS) for searching a GPU-friendly design of both an efficient combination of modules and better modular-level architecture for object detection.
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS
In this work, we propose BONAS (Bayesian Optimized Neural Architecture Search), a sample-based NAS framework which is accelerated using weight-sharing to evaluate multiple related architectures simultaneously.
Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification
Abstract Neural architecture search (NAS) has shown great potential in automating the manual process of designing a good CNN architecture for image classification.
MANAS: Multi-Agent Neural Architecture Search
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective.
Spatial-Aware Graph Relation Network for Large-Scale Object Detection
How to proper encode high-order object relation in the detection system without any external knowledge?
Reasoning-RCNN: Unifying Adaptive Global Reasoning Into Large-Scale Object Detection
In this paper, we address the large-scale object detection problem with thousands of categories, which poses severe challenges due to long-tail data distributions, heavy occlusions, and class ambiguities.
Hybrid Knowledge Routed Modules for Large-scale Object Detection
The dominant object detection approaches treat the recognition of each region separately and overlook crucial semantic correlations between objects in one scene.