Search Results for author:
Found 15 papers, 6 papers with code
VRP-SAM: SAM with Visual Reference Prompt
In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model.
Semantic-Aware Autoregressive Image Modeling for Visual Representation Learning
In this study, inspired by human beings' way of grasping an image, i. e., focusing on the main object first, we present a semantic-aware autoregressive image modeling (SemAIM) method to tackle this challenge.
Multi-Mode Online Knowledge Distillation for Self-Supervised Visual Representation Learning
Different from existing SSL-KD methods that transfer knowledge from a static pre-trained teacher to a student, in MOKD, two different models learn collaboratively in a self-supervised manner.
Teacher-Student Knowledge Distillation for Radar Perception on Embedded Accelerators
Many radar signal processing methodologies are being developed for critical road safety perception tasks.
s-Adaptive Decoupled Prototype for Few-Shot Object Detection
To provide precise information for the query image, the prototype is decoupled into task-specific ones, which provide tailored guidance for 'where to look' and 'what to look for', respectively.
Semantics-Consistent Feature Search for Self-Supervised Visual Representation Learning
In contrastive self-supervised learning, the common way to learn discriminative representation is to pull different augmented "views" of the same image closer while pushing all other images further apart, which has been proven to be effective.
Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining
The training process consists of self-supervised pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the detector on Object365, and finally finetuning it on COCO.
Ranked #8 on
Object Detection
on COCO test-dev
Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection
To address these drawbacks, we propose a Time-rEversed diffusioN tEnsor Transformer (TENET), which i) forms high-order tensor representations that capture multi-way feature occurrences that are highly discriminative, and ii) uses a transformer that dynamically extracts correlations between the query image and the entire support set, instead of a single average-pooled support embedding.
Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment
Detection transformer (DETR) relies on one-to-one assignment, assigning one ground-truth object to one prediction, for end-to-end detection without NMS post-processing.
A Neural Network Based Method with Transfer Learning for Genetic Data Analysis
Transfer learning has emerged as a powerful technique in many application problems, such as computer vision and natural language processing.
CATrans: Context and Affinity Transformer for Few-Shot Segmentation
In this work, we effectively integrate the context and affinity information via the proposed novel Context and Affinity Transformer (CATrans) in a hierarchical architecture.
Distributed Estimation in Large Scale Wireless Sensor Networks via a Two Step Group-based Approach
To achieve greater sensor transmission and estimation efficiency, we propose a two step group-based collaborative distributed estimation scheme, where in the first step, sensors form dependence driven groups such that sensors in the same group are highly dependent, while sensors from different groups are independent, and perform a copula-based maximum a posteriori probability (MAP) estimation via intragroup collaboration.
Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network
Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis.
Kernelized Few-Shot Object Detection With Efficient Integral Aggregation
We design a Kernelized Few-shot Object Detector by leveraging kernelized matrices computed over multiple proposal regions, which yield expressive non-linear representations whose model complexity is learned on the fly.
Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory
Such arguments, however, fail to acknowledge that the overall decision-making system is composed of two entities: the learned model and a human who fuses together model outputs with his or her own information.
