Search Results for author:
Found 19 papers, 12 papers with code
The Mitochondrial Genome of Cathaya argyrophylla Reaches 18.99 Mb: Analysis of Super-Large Mitochondrial Genomes in Pinaceae
In this study, we sequenced and analyzed the mitochondrial genome of Cathaya argyrophylla, an endangered and endemic Pinaceae species, uncovering a genome size of 18. 99 Mb, meaning the largest mitochondrial genome reported to date.
P4Q: Learning to Prompt for Quantization in Visual-language Models
We propose a method that balances fine-tuning and quantization named ``Prompt for Quantization'' (P4Q), in which we design a lightweight architecture to leverage contrastive loss supervision to enhance the recognition performance of a PTQ model.
Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective
In this paper, we re-examine the SID problem and identify two prevalent biases in current training paradigms, i. e., weakened artifact features and overfitted artifact features.
A Sanity Check for AI-generated Image Detection
This effectively enables the model to discern AI-generated images based on semantics or contextual information; Secondly, we select the highest frequency patches and the lowest frequency patches in the image, and compute the low-level patchwise features, aiming to detect AI-generated images by low-level artifacts, for example, noise pattern, anti-aliasing, etc.
Mining Open Semantics from CLIP: A Relation Transition Perspective for Few-Shot Learning
Specifically, we adopt a transformer module which takes the visual feature as "Query", the text features of the anchors as "Key" and the similarity matrix between the text features of anchor and target classes as "Value".
RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model
RSBuilding is designed to enhance cross-scene generalization and task universality.
Controllable Mind Visual Diffusion Model
Brain signal visualization has emerged as an active research area, serving as a critical interface between the human visual system and computer vision models.
PiClick: Picking the desired mask from multiple candidates in click-based interactive segmentation
Click-based interactive segmentation aims to generate target masks via human clicking, which facilitates efficient pixel-level annotation and image editing.
MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation
CLIP (Contrastive Language-Image Pretraining) is well-developed for open-vocabulary zero-shot image-level recognition, while its applications in pixel-level tasks are less investigated, where most efforts directly adopt CLIP features without deliberative adaptations.
Ranked #5 on
Zero-Shot Semantic Segmentation
on COCO-Stuff
Towards Open-Vocabulary Video Instance Segmentation
Video Instance Segmentation (VIS) aims at segmenting and categorizing objects in videos from a closed set of training categories, lacking the generalization ability to handle novel categories in real-world videos.
Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Space
Despite its fruitful applications in remote sensing, image super-resolution is troublesome to train and deploy as it handles different resolution magnifications with separate models.
OvarNet: Towards Open-vocabulary Object Attribute Recognition
In this paper, we consider the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at the training stage, resembling an open-vocabulary scenario.
Ranked #1 on
Open Vocabulary Attribute Detection
on OVAD benchmark
(using extra training data)
SwiftNet: Real-time Video Object Segmentation
In this work we present SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77. 8% J &F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance.
Horizontal-to-Vertical Video Conversion
Alongside the prevalence of mobile videos, the general public leans towards consuming vertical videos on hand-held devices.
Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking
In this work, we present an end-to-end framework to settle data association in online Multiple-Object Tracking (MOT).
Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks
In this paper, we propose a trellis encoder-decoder network (TEDnet) for crowd counting, which focuses on generating high-quality density estimation maps.
Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network
In this paper, we propose a trellis encoder-decoder network (TEDnet) for crowd counting, which focuses on generating high-quality density estimation maps.
Model-free Tracking with Deep Appearance and Motion Features Integration
To overcome the object-centric information scarcity, both appearance and motion features are deeply integrated by the proposed AMNet, which is an end-to-end offline trained two-stream network.
