Search Results for author:
Found 51 papers, 32 papers with code
VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection
In recent years, transformer-based detectors have demonstrated remarkable performance in 2D visual perception tasks.
Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning
As a pioneering work, PointContrast conducts unsupervised 3D representation learning via leveraging contrastive learning over raw RGB-D frames and proves its effectiveness on various downstream tasks.
Ranked #2 on
Semantic Segmentation
on ScanNet
(using extra training data)
Detecting Everything in the Open World: Towards Universal Object Detection
In this paper, we formally address universal object detection, which aims to detect every scene and predict every category.
Influencer Backdoor Attack on Semantic Segmentation
In this work, we explore backdoor attacks on segmentation models to misclassify all pixels of a victim class by injecting a specific trigger on non-victim pixels during inferences, which is dubbed Influencer Backdoor Attack (IBA).
Open-vocabulary Panoptic Segmentation with Embedding Modulation
Open-vocabulary image segmentation is attracting increasing attention due to its critical applications in the real world.
ScribbleSeg: Scribble-based Interactive Image Segmentation
We hope this could serve as a more powerful and general solution for interactive segmentation.
GeoSpark: Sparking up Point Cloud Segmentation with Geometry Clue
For feature aggregation, it improves feature modeling by allowing the network to learn from both local points and neighboring geometry partitions, resulting in an enlarged data-tailored receptive field.
Semantics-Aware Dynamic Localization and Refinement for Referring Image Segmentation
Referring image segmentation segments an image from a language expression.
PhysFormer++: Facial Video-based Physiological Measurement with SlowFast Temporal Difference Transformer
As key modules in PhysFormer, the temporal difference transformers first enhance the quasi-periodic rPPG features with temporal difference guided global attention, and then refine the local spatio-temporal representation against interference.
Mod-Squad: Designing Mixtures of Experts As Modular Multi-Task Learners
To address the MTL challenge, we propose Mod-Squad, a new model that is Modularized into groups of experts (a 'Squad').
Mod-Squad: Designing Mixture of Experts As Modular Multi-Task Learners
To address the MTL challenge, we propose Mod-Squad, a new model that is Modularized into groups of experts (a 'Squad').
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments
In this paper, we use Deep Generative Networks (DGNs) with a novel training mechanism to eliminate the distribution gap.
$BT^2$: Backward-compatible Training with Basis Transformation
In this way, the new representation can be directly compared with the old representation, in principle avoiding the need for any backfilling.
Point Transformer V2: Grouped Vector Attention and Partition-based Pooling
In this work, we analyze the limitations of the Point Transformer and propose our powerful and efficient Point Transformer V2 model with novel designs that overcome the limitations of previous work.
Ranked #2 on
Semantic Segmentation
on S3DIS Area5
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness
Since SegPGD can create more effective adversarial examples, the adversarial training with our SegPGD can boost the robustness of segmentation models.
DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation
Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations.
Universal Adaptive Data Augmentation
Different from existing methods, UADA would adaptively update DA's parameters according to the target model's gradient information during training: given a pre-defined set of DA operations, we randomly decide types and magnitudes of DA operations for every data batch during training, and adaptively update DA's parameters along the gradient direction of the loss concerning DA's parameters.
FocalClick: Towards Practical Interactive Image Segmentation
To make the model work with preexisting masks, we formulate a sub-task termed Interactive Mask Correction, and propose Progressive Merge as the solution.
Ranked #1 on
Interactive Segmentation
on GrabCut
(using extra training data)
Stratified Transformer for 3D Point Cloud Segmentation
In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance.
Ranked #5 on
Semantic Segmentation
on ScanNet
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation
Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image.
Ranked #3 on
Referring Expression Segmentation
on RefCOCOg-test
PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer
Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications (e. g., remote healthcare and affective computing).
Adversarial Examples on Segmentation Models Can be Easy to Transfer
The high transferability achieved by our method shows that, in contrast to the observations in previous work, adversarial examples on a segmentation model can be easy to transfer to other segmentation models.
Hierarchical interaction network for video object segmentation from referring expressions
In this paper, we investigate the problem of video object segmentation from referring expressions (VOSRE).
Ranked #1 on
Referring Expression Segmentation
on J-HMDB
(Precision@0.9 metric)
Optical Flow Estimation
Referring Expression Segmentation
+3
Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision
In particular, Panoptic FCN encodes each object instance or stuff category with the proposed kernel generator and produces the prediction by convolving the high-resolution feature directly.
Panoptic Segmentation
Weakly-supervised panoptic segmentation
Open-World Entity Segmentation
By removing the need of class label prediction, the models trained for such task can focus more on improving segmentation quality.
Do Different Tracking Tasks Require Different Appearance Models?
We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks considered.
Ranked #2 on
Video Object Segmentation
on DAVIS 2017
(mIoU metric)
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+10
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency
Semantic segmentation has made tremendous progress in recent years.
Dual-Cross Central Difference Network for Face Anti-Spoofing
In this paper, we propose two Cross Central Difference Convolutions (C-CDC), which exploit the difference of the center and surround sparse local features from the horizontal/vertical and diagonal directions, respectively.
Distilling Knowledge via Knowledge Review
Knowledge distillation transfers knowledge from the teacher network to the student one, with the goal of greatly improving the performance of the student network.
Ranked #9 on
Knowledge Distillation
on CIFAR-100
Bidirectional Projection Network for Cross Dimension Scene Understanding
Via the \emph{BPM}, complementary 2D and 3D information can interact with each other in multiple architectural levels, such that advantages in these two visual domains can be combined for better scene recognition.
Ranked #7 on
Semantic Segmentation
on ScanNet
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds
The key of PAConv is to construct the convolution kernel by dynamically assembling basic weight matrices stored in Weight Bank, where the coefficients of these weight matrices are self-adaptively learned from point positions through ScoreNet.
Ranked #2 on
Point Cloud Segmentation
on PointCloud-C
General Adversarial Defense via Pixel Level and Feature Level Distribution Alignment
Specifically, compared with previous methods, we propose a more efficient pixel-level training constraint to weaken the hardness of aligning adversarial samples to clean samples, which can thus obviously enhance the robustness on adversarial samples.
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
In this paper, we aim to provide an alternative perspective by treating semantic segmentation as a sequence-to-sequence prediction task.
Ranked #1 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
Point Transformer
For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.
Ranked #3 on
3D Semantic Segmentation
on STPLS3D
Fully Convolutional Networks for Panoptic Segmentation
In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN.
Ranked #1 on
Panoptic Segmentation
on Mapillary val
(PQth metric)
Generalized Few-shot Semantic Segmentation
Then, since context is essential for semantic segmentation, we propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by 1) leveraging the co-occurrence prior knowledge from support samples, and 2) dynamically enriching contextual information to the classifier, conditioned on the content of each query image.
Generalized Few-Shot Semantic Segmentation
Semantic Segmentation
Prior Guided Feature Enrichment Network for Few-Shot Segmentation
It consists of novel designs of (1) a training-free prior mask generation method that not only retains generalization power but also improves model performance and (2) Feature Enrichment Module (FEM) that overcomes spatial inconsistency by adaptively enriching query features with support features and prior masks.
Ranked #54 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Exploring Self-attention for Image Recognition
Recent work has shown that self-attention can serve as a basic building block for image recognition models.
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
Instance segmentation is an important task for scene understanding.
Ranked #5 on
3D Instance Segmentation
on STPLS3D
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation
Adversarial training is promising for improving robustness of deep neural networks towards adversarial perturbations, especially on the classification task.
GridMask Data Augmentation
Then we show limitation of existing information dropping algorithms and propose our structured method, which is simple and yet very effective.
Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
To incorporate point features in the edge branch, we establish a hierarchical graph framework, where the graph is initialized from a coarse layer and gradually enriched along the point decoding process.
Ranked #25 on
Semantic Segmentation
on S3DIS Area5
Region Refinement Network for Salient Object Detection
Albeit intensively studied, false prediction and unclear boundaries are still major issues of salient object detection.
PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing
Unlike previous work, we densely connect each point with every other in a local neighborhood, aiming to specify feature of each point based on the local region characteristics for better representing the region.
Ranked #17 on
Semantic Segmentation
on S3DIS Area5
(oAcc metric)
UPSNet: A Unified Panoptic Segmentation Network
More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification.
Ranked #3 on
Panoptic Segmentation
on KITTI Panoptic Segmentation
PSANet: Point-wise Spatial Attention Network for Scene Parsing
We notice information flow in convolutional neural networks is restricted inside local neighborhood regions due to the physical design of convolutional filters, which limits the overall understanding of complex scenes.
Ranked #45 on
Semantic Segmentation
on Cityscapes test
Compositing-aware Image Search
We present a new image search technique that, given a background image, returns compatible foreground objects for image compositing tasks.
SegStereo: Exploiting Semantic Information for Disparity Estimation
Disparity estimation for binocular stereo images finds a wide range of applications.
Ranked #5 on
Semantic Segmentation
on KITTI Semantic Segmentation
Automatic Real-time Background Cut for Portrait Videos
A spatial-temporal refinement network is developed to further refine the segmentation errors in each frame and ensure temporal coherence in the segmentation map.
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
We focus on the challenging task of real-time semantic segmentation in this paper.
Ranked #10 on
Dichotomous Image Segmentation
on DIS-VD
Dichotomous Image Segmentation
Real-Time Semantic Segmentation
+2
Pyramid Scene Parsing Network
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes.
