Search Results for author:
Found 23 papers, 13 papers with code
COSEE: Consistency-Oriented Signal-Based Early Exiting via Calibrated Sample Weighting Mechanism
Early exiting is an effective paradigm for improving the inference efficiency of pre-trained language models (PLMs) by dynamically adjusting the number of executed layers for each sample.
NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstruction
We contend that the key to addressing these challenges lies in accurately decoding both high-level semantics and low-level perception flows, as perceived by the brain in response to video stimuli.
FedMinds: Privacy-Preserving Personalized Brain Visual Decoding
Exploring the mysteries of the human brain is a long-term research topic in neuroscience.
Granular-Balls based Fuzzy Twin Support Vector Machine for Classification
Additionally, we develop the granular-ball fuzzy twin support vector machine (GBFTSVM) classifier by incorporating GBC with the fuzzy twin support vector machine (FTSVM) classifier.
MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization
Additionally, we introduce a token merging method guided by comprehensive semantics from the frequency and spatial domains.
Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding
Additionally, Wills Aligner leverages the semantic relation of visual stimuli to guide the learning of inter-subject commonality, enabling visual decoding for each subject to draw insights from other subjects' data.
MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correction
Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks.
DE$^3$-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks
DE$^3$-BERT implements a hybrid exiting strategy that supplements classic entropy-based local information with distance-based global information to enhance the estimation of prediction correctness for more reliable early exiting decisions.
Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast
In real-world scenarios, multimodal federated learning often faces the practical challenge of intricate modality missing, which poses constraints on building federated frameworks and significantly degrades model inference accuracy.
Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector
Multimodal content, such as mixing text with images, presents significant challenges to rumor detection in social media.
Lite-Mind: Towards Efficient and Robust Brain Representation Network
The limited data availability and the low signal-to-noise ratio of fMRI signals lead to the challenging task of fMRI-to-image retrieval.
Hyneter: Hybrid Network Transformer for Object Detection
In this paper, we point out that the essential differences between CNN-based and Transformer-based detectors, which cause the worse performance of small objects in Transformer-based methods, are the gap between local information and global dependencies in feature extraction and propagation.
Learning Domain Invariant Prompt for Vision-Language Models
Prompt learning is one of the most effective and trending ways to adapt powerful vision-language foundation models like CLIP to downstream datasets by tuning learnable prompt vectors with very few samples.
Ranked #4 on
Prompt Engineering
on Caltech-101
Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification
Instead of learning fixed triggers for the target classes from the training set, DT-IBA can dynamically generate new triggers for any unknown identities.
Context-Aware Feature Learning for Noise Robust Person Search
For human-introduced noise, we propose a noise-discovery and noise-suppression training process for mislabeling robust person search.
Rethinking the Zigzag Flattening for Image Reading
Sequence ordering of word vector matters a lot to text reading, which has been proven in natural language processing (NLP).
Salience-Guided Iterative Asymmetric Mutual Hashing for Fast Person Re-identification
Moreover, a novel iterative asymmetric mutual training strategy (IAMT) is proposed to alleviate drawbacks of common mutual learning, which can continuously refine the discriminative regions for SSB and extract regularized dark knowledge for two models as well.
Improved Instance Discrimination and Feature Compactness for End-to-End Person Search
Our method achieves comparable performance on two benchmarks, CUHK-SYSU and PRW, and achieves 91. 96% of mAP and 93. 34% of rank1 accuracy on CUHK-SYSU.
Control Distance IoU and Control Distance IoU Loss Function for Better Bounding Box Regression
In this paper, we first present an evaluation-feedback module, which is proposed to consist of evaluation system and feedback mechanism.
Sequential End-to-end Network for Efficient Person Search
Person search aims at jointly solving Person Detection and Person Re-identification (re-ID).
Ranked #7 on
Person Search
on PRW
Granular conditional entropy-based attribute reduction for partially labeled data with proxy labels
Attribute reduction is one of the most important research topics in the theory of rough sets, and many rough sets-based attribute reduction methods have thus been presented.
Deep Fusion Feature Representation Learning with Hard Mining Center-Triplet Loss for Person Re-identification
The extraction of robust feature representations from pedestrian images through CNNs with a single deterministic pooling operation is problematic as the features in real pedestrian images are complex and diverse.
Hierarchical Attention Generative Adversarial Networks for Cross-domain Sentiment Classification
Cross-domain sentiment classification (CDSC) is an importance task in domain adaptation and sentiment classification.
