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Found 27 papers, 13 papers with code
YNetr: Dual-Encoder architecture on Plain Scan Liver Tumors (PSLT)
Concurrently, we utilized Dice coefficient as the metric for assessing the segmentation outcomes produced by YNetr, having advantage of capturing different frequency information.
Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction
The in-context learning (ICL) for relational triple extraction (RTE) has achieved promising performance, but still encounters two key challenges: (1) how to design effective prompts and (2) how to select proper demonstrations.
Content-Conditioned Generation of Stylized Free hand Sketches
We explore the performance of the model, including using styles randomly sampled from a prior normal distribution to generate images with various free-hand sketching styles, disentangling the painters' styles from known free-hand sketches to generate images with specific styles, and generating images of unknown classes that are not in the training set.
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process
We propose a model-agnostic solution called SegRefiner, which offers a novel perspective on this problem by interpreting segmentation refinement as a data generation process.
GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation
Vision Transformers (ViTs) have revolutionized the field of computer vision, yet their deployments on resource-constrained devices remain challenging due to high computational demands.
Ranked #185 on
Image Classification
on ImageNet
Understanding the Effects of Projectors in Knowledge Distillation
Interestingly, we discovered that even if the student and the teacher have the same feature dimensions, adding a projector still helps to improve the distillation performance.
No Token Left Behind: Efficient Vision Transformer via Dynamic Token Idling
By allowing the idle tokens to be re-selected in the following layers, IdleViT mitigates the negative impact of improper pruning in the early stages.
Plug n' Play: Channel Shuffle Module for Enhancing Tiny Vision Transformers
Inspired by the channel shuffle design in ShuffleNetV2 \cite{ma2018shufflenet}, our module expands the feature channels of a tiny ViT and partitions the channels into two groups: the \textit{Attended} and \textit{Idle} groups.
Real-time Multi-modal Object Detection and Tracking on Edge for Regulatory Compliance Monitoring
Regulatory compliance auditing across diverse industrial domains requires heightened quality assurance and traceability.
Object Detection Difficulty: Suppressing Over-aggregation for Faster and Better Video Object Detection
The ODD score enhances the VOD system in two ways: 1) it enables the VOD system to select superior global reference frames, thereby improving overall accuracy; and 2) it serves as an indicator in the newly designed ODD Scheduler to eliminate the aggregation of frames that are easy to detect, thus accelerating the VOD process.
OCHID-Fi: Occlusion-Robust Hand Pose Estimation in 3D via RF-Vision
To overcome the challenge in labeling RF imaging given its human incomprehensible nature, OCHID-Fi employs a cross-modality and cross-domain training process.
Dynamic Token Pruning in Plain Vision Transformers for Semantic Segmentation
Experiments suggest that the proposed DToP architecture reduces on average $20\% - 35\%$ of computational cost for current semantic segmentation methods based on plain vision transformers without accuracy degradation.
Decoupled Graph Neural Networks for Large Dynamic Graphs
The experimental results demonstrate that our algorithm achieves state-of-the-art performance in both kinds of dynamic graphs.
Global Knowledge Calibration for Fast Open-Vocabulary Segmentation
Recent advancements in pre-trained vision-language models, such as CLIP, have enabled the segmentation of arbitrary concepts solely from textual inputs, a process commonly referred to as open-vocabulary semantic segmentation (OVS).
Knowledge Distillation
Open Vocabulary Semantic Segmentation
+4
Point-Syn2Real: Semi-Supervised Synthetic-to-Real Cross-Domain Learning for Object Classification in 3D Point Clouds
Object classification using LiDAR 3D point cloud data is critical for modern applications such as autonomous driving.
Improved Feature Distillation via Projector Ensemble
Motivated by the positive effect of the projector in feature distillation, we propose an ensemble of projectors to further improve the quality of student features.
STAR-GNN: Spatial-Temporal Video Representation for Content-based Retrieval
It models a video with a lattice feature graph in which the nodes represent regions of different granularity, with weighted edges that represent the spatial and temporal links.
InvisibiliTee: Angle-agnostic Cloaking from Person-Tracking Systems with a Tee
After a survey for person-tracking system-induced privacy concerns, we propose a black-box adversarial attack method on state-of-the-art human detection models called InvisibiliTee.
A Real-time Edge-AI System for Reef Surveys
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COTS populations to ecologically sustainable levels.
Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data
However, the multimodal animal behavior classification algorithm based on posterior probability fusion is preferable to the one based on feature concatenation as it delivers better classification accuracy, has less computational and memory complexity, is more robust to sensor data failure, and enjoys better modularity.
Instant Graph Neural Networks for Dynamic Graphs
With the development of numerous GNN variants, recent years have witnessed groundbreaking results in improving the scalability of GNNs to work on static graphs with millions of nodes.
The CSIRO Crown-of-Thorn Starfish Detection Dataset
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are underway in an attempt to manage COTS populations to ecologically sustainable levels.
A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition
In addition, extended learning period is a general challenge for deep RL which can impact the speed of learning for SER.
Unsupervised Adversarial Attacks on Deep Feature-based Retrieval with GAN
Such tendency indicates that the model indeed learned how to toy with both image retrieval systems and human eyes.
RUM: network Representation learning throUgh Multi-level structural information preservation
We propose a novel network representations learning model framework called RUM (network Representation learning throUgh Multi-level structural information preservation).
Learning in High-Dimensional Multimedia Data: The State of the Art
Given the high dimensionality and the high complexity of multimedia data, it is important to investigate new machine learning algorithms to facilitate multimedia data analysis.
Temporal Embedding in Convolutional Neural Networks for Robust Learning of Abstract Snippets
The prediction of periodical time-series remains challenging due to various types of data distortions and misalignments.
