Search Results for author:
Found 15 papers, 14 papers with code
Point Cloud Mamba: Point Cloud Learning via State Space Model
To enable more effective processing of 3-D point cloud data by Mamba, we propose a novel Consistent Traverse Serialization to convert point clouds into 1-D point sequences while ensuring that neighboring points in the sequence are also spatially adjacent.
OMG-Seg: Is One Model Good Enough For All Segmentation?
In this work, we address various segmentation tasks, each traditionally tackled by distinct or partially unified models.
RAP-SAM: Towards Real-Time All-Purpose Segment Anything
Segment Anything Model (SAM) is one remarkable model that can achieve generalized segmentation.
Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively
The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs).
Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants
Beyond the normal case, long-tail class incremental learning and few-shot class incremental learning are also proposed to consider the data imbalance and data scarcity, respectively, which are common in real-world implementations and further exacerbate the well-known problem of catastrophic forgetting.
Towards Open Vocabulary Learning: A Survey
To our knowledge, this is the first comprehensive literature review of open vocabulary learning.
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
Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning
In this paper, we deal with this misalignment dilemma in FSCIL inspired by the recently discovered phenomenon named neural collapse, which reveals that the last-layer features of the same class will collapse into a vertex, and the vertices of all classes are aligned with the classifier prototypes, which are formed as a simplex equiangular tight frame (ETF).
Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning
In this paper, we deal with this misalignment dilemma in FSCIL inspired by the recently discovered phenomenon named neural collapse, which reveals that the last-layer features of the same class will collapse into a vertex, and the vertices of all classes are aligned with the classifier prototypes, which are formed as a simplex equiangular tight frame (ETF).
Ranked #3 on
Few-Shot Class-Incremental Learning
on CUB-200-2011
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.
Towards Theoretically Inspired Neural Initialization Optimization
With NIO, we improve the classification performance of a variety of neural architectures on CIFAR-10, CIFAR-100, and ImageNet.
Multi-Task Learning with Multi-Query Transformer for Dense Prediction
We first model each task with a task-relevant query.
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.
Monocular Road Planar Parallax Estimation
It implies planar parallax and can be combined with the road plane serving as a reference to estimate the 3D structure by warping the consecutive frames.
