Search Results for author:
Found 36 papers, 25 papers with code
OV-VG: A Benchmark for Open-Vocabulary Visual Grounding
While current foundational models excel at various visual language tasks, there's a noticeable absence of models specifically tailored for open-vocabulary visual grounding.
Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision
The proposed framework is evaluated on five regular VG datasets and two newly constructed robust VG datasets.
Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review
We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and learning frameworks in the methodology section.
Transformer-Based Visual Segmentation: A Survey
Recently, transformers, a type of neural network based on self-attention originally designed for natural language processing, have considerably surpassed previous convolutional or recurrent approaches in various vision processing tasks.
Tube-Link: A Flexible Cross Tube Framework for Universal Video Segmentation
Our framework is a near-online approach that takes a short subclip as input and outputs the corresponding spatial-temporal tube masks.
Ranked #3 on
Video Semantic Segmentation
on VSPW
Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search
For local information exchange, a graph convolutional network (GCN) guided module is seamlessly integrated as a communication deliver between cells.
PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation
Third, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross-attention scheme to boost part segmentation qualities further.
Betrayed by Captions: Joint Caption Grounding and Generation for Open Vocabulary Instance Segmentation
Experiments on the COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS) and Open Set Panoptic Segmentation (OSPS) demonstrate the superiority of the CGG.
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation
To solve this problem, we propose a Multi-Modality Ovarian Tumor Ultrasound (MMOTU) image dataset containing 1469 2d ultrasound images and 170 contrast enhanced ultrasonography (CEUS) images with pixel-wise and global-wise annotations.
SFNet: Faster, Accurate, and Domain Agnostic Semantic Segmentation via Semantic Flow
In this paper, we focus on exploring effective methods for faster, accurate, and domain agnostic semantic segmentation.
Multi-level Domain Adaptation for Lane Detection
We focus on bridging domain discrepancy in lane detection among different scenarios to greatly reduce extra annotation and re-training costs for autonomous driving.
Reconstruct from BEV: A 3D Lane Detection Approach based on Geometry Structure Prior
Inspired by previous methods, we first analyze the geometry heuristic between the 3D lane and its 2D representation on the ground and propose to impose explicit supervision based on the structure prior, which makes it achievable to build inter-lane and intra-lane relationships to facilitate the reconstruction of 3D lanes from local to global.
Reconstruct from Top View: A 3D Lane Detection Approach based on Geometry Structure Prior
Inspired by previous methods, we first analyze the geometry heuristic between the 3D lane and its 2D representation on the ground and propose to impose explicit supervision based on the structure prior, which makes it achievable to build inter-lane and intra-lane relationships to facilitate the reconstruction of 3D lanes from local to global.
Ranked #6 on
3D Lane Detection
on Apollo Synthetic 3D Lane
Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation
We hope this simple, yet effective method can serve as a new, flexible baseline in unified video segmentation design.
Ranked #1 on
Video Panoptic Segmentation
on KITTI-STEP
(using extra training data)
Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition
This focus on joint human fashion segmentation and attribute recognition.
Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation
To the best of our knowledge, we are the first to solve the PPS problem via \textit{a unified and end-to-end transformer model.
TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers
Detection Transformer (DETR) and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous complex hand-crafted detectors.
Ranked #4 on
Video Object Detection
on ImageNet VID
(using extra training data)
PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation
The Depth-aware Video Panoptic Segmentation (DVPS) is a new challenging vision problem that aims to predict panoptic segmentation and depth in a video simultaneously.
PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation
Extensive experiments demonstrate that our method can soundly boost the performance on both cross-domain object detection and segmentation for state-of-the-art techniques.
Context-Aware Mixup for Domain Adaptive Semantic Segmentation
The generated contextual mask is critical in this work and will guide the context-aware domain mixup on three different levels.
Ranked #5 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
Global Aggregation then Local Distribution for Scene Parsing
Modelling long-range contextual relationships is critical for pixel-wise prediction tasks such as semantic segmentation.
Improving Video Instance Segmentation via Temporal Pyramid Routing
To incorporate both temporal and scale information, we propose a Temporal Pyramid Routing (TPR) strategy to conditionally align and conduct pixel-level aggregation from a feature pyramid pair of two adjacent frames.
BoundarySqueeze: Image Segmentation as Boundary Squeezing
This module is used to squeeze the object boundary from both inner and outer directions, which contributes to precise mask representation.
End-to-End Video Object Detection with Spatial-Temporal Transformers
Recently, DETR and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous complex hand-crafted detectors.
Enhanced Boundary Learning for Glass-like Object Segmentation
We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours.
Ranked #20 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods.
Embedded Knowledge Distillation in Depth-Level Dynamic Neural Network
In real applications, different computation-resource devices need different-depth networks (e. g., ResNet-18/34/50) with high-accuracy.
Towards Efficient Scene Understanding via Squeeze Reasoning
Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector and perform reasoning within the single vector where the computation cost can be significantly reduced.
Improving Semantic Segmentation via Decoupled Body and Edge Supervision
Our insight is that appealing performance of semantic segmentation requires \textit{explicitly} modeling the object \textit{body} and \textit{edge}, which correspond to the high and low frequency of the image.
Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization
Learning to find an optimal mixed precision model that can preserve accuracy and satisfy the specific constraints on model size and computation is extremely challenge due to the difficult in training a mixed precision model and the huge space of all possible bit quantizations.
Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation
Guided by this mask, we propose a ClassOut strategy to realize effective regional consistency in a fine-grained manner.
DMT: Dynamic Mutual Training for Semi-Supervised Learning
Instead, leveraging inter-model disagreement between different models is a key to locate pseudo label errors.
Graph-guided Architecture Search for Real-time Semantic Segmentation
Unlike previous works that use a simplified search space and stack a repeatable cell to form a network, we introduce a novel search mechanism with new search space where a lightweight model can be effectively explored through the cell-level diversity and latencyoriented constraint.
GDRQ: Group-based Distribution Reshaping for Quantization
Low-bit quantization is challenging to maintain high performance with limited model capacity (e. g., 4-bit for both weights and activations).
OVSNet : Towards One-Pass Real-Time Video Object Segmentation
Video object segmentation aims at accurately segmenting the target object regions across consecutive frames.
Accurate Urban Road Centerline Extraction from VHR Imagery via Multiscale Segmentation and Tensor Voting
Finally, to overcome the ineffectiveness of current methods in the road intersection, a fitting based road centerline connection algorithm is proposed.
