Search Results for author:
Found 14 papers, 10 papers with code
GSVA: Generalized Segmentation via Multimodal Large Language Models
Generalized Referring Expression Segmentation (GRES) extends the scope of classic RES to refer to multiple objects in one expression or identify the empty targets absent in the image.
Generalized Referring Expression Segmentation
Referring Expression
+1
DAT++: Spatially Dynamic Vision Transformer with Deformable Attention
On the one hand, using dense attention in ViT leads to excessive memory and computational cost, and features can be influenced by irrelevant parts that are beyond the region of interests.
Ranked #4 on
Object Detection
on COCO 2017
FLatten Transformer: Vision Transformer using Focused Linear Attention
The quadratic computation complexity of self-attention has been a persistent challenge when applying Transformer models to vision tasks.
Dynamic Perceiver for Efficient Visual Recognition
Early exits are placed exclusively within the classification branch, thus eliminating the need for linear separability in low-level features.
Slide-Transformer: Hierarchical Vision Transformer with Local Self-Attention
Self-attention mechanism has been a key factor in the recent progress of Vision Transformer (ViT), which enables adaptive feature extraction from global contexts.
Joint Representation Learning for Text and 3D Point Cloud
During the pre-training stage, we establish the correspondence of images and point clouds based on the readily available RGB-D data and use contrastive learning to align the image and point cloud representations.
Contrastive Language-Image Pre-Training with Knowledge Graphs
Recent years have witnessed the fast development of large-scale pre-training frameworks that can extract multi-modal representations in a unified form and achieve promising performances when transferred to downstream tasks.
ActiveNeRF: Learning where to See with Uncertainty Estimation
In this paper, we present a novel learning framework, ActiveNeRF, aiming to model a 3D scene with a constrained input budget.
Vision Transformer with Deformable Attention
On the one hand, using dense attention e. g., in ViT, leads to excessive memory and computational cost, and features can be influenced by irrelevant parts which are beyond the region of interests.
Ranked #107 on
Object Detection
on COCO test-dev
On the Integration of Self-Attention and Convolution
In this paper, we show that there exists a strong underlying relation between them, in the sense that the bulk of computations of these two paradigms are in fact done with the same operation.
A Unified Framework for Convolution-based Graph Neural Networks
In this paper, we take a step forward to establish a unified framework for convolution-based graph neural networks, by formulating the basic graph convolution operation as an optimization problem in the graph Fourier space.
3D Object Detection with Pointformer
In this paper, we propose Pointformer, a Transformer backbone designed for 3D point clouds to learn features effectively.
Regularizing Deep Networks with Semantic Data Augmentation
The proposed method is inspired by the intriguing property that deep networks are effective in learning linearized features, i. e., certain directions in the deep feature space correspond to meaningful semantic transformations, e. g., changing the background or view angle of an object.
Implicit Semantic Data Augmentation for Deep Networks
Our work is motivated by the intriguing property that deep networks are surprisingly good at linearizing features, such that certain directions in the deep feature space correspond to meaningful semantic transformations, e. g., adding sunglasses or changing backgrounds.
