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Found 31 papers, 6 papers with code
fMRI-3D: A Comprehensive Dataset for Enhancing fMRI-based 3D Reconstruction
To advance this task, we present the fMRI-3D dataset, which includes data from 15 participants and showcases a total of 4768 3D objects.
Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-Training
In this paper, we present a novel paradigm Diffusion-ReID to efficiently augment and generate diverse images based on known identities without requiring any cost of data collection and annotation.
Hyper-Transformer for Amodal Completion
The multi-scale features from the image branch guide the hyper transformer in learning shape priors and in generating the weights for dynamic convolution tailored to each instance.
Distribution Aligned Semantics Adaption for Lifelong Person Re-Identification
Therefore, the adaptation of Re-ID models to new domains while preserving previously acquired knowledge is crucial, known as Lifelong person Re-IDentification (LReID).
Auto-selected Knowledge Adapters for Lifelong Person Re-identification
On the one hand, the appropriate adapters are selected for the inputs to process ReID, and on the other hand, the knowledge interaction and fusion between adapters are enhanced to improve the generalization ability of the model.
Image-Text-Image Knowledge Transferring for Lifelong Person Re-Identification with Hybrid Clothing States
To tackle the challenges of knowledge granularity mismatch and knowledge presentation mismatch that occurred in LReID-Hybrid, we take advantage of the consistency and generalization of the text space, and propose a novel framework, dubbed $Teata$, to effectively align, transfer and accumulate knowledge in an "image-text-image" closed loop.
Content and Salient Semantics Collaboration for Cloth-Changing Person Re-Identification
Specifically, we propose the Content and Salient Semantics Collaboration (CSSC) framework, facilitating cross-parallel semantics interaction and refinement.
Ranked #1 on
Person Re-Identification
on PRCC
(Rank-1 metric)
NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation
Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific conditions of pre-trained diffusion models.
Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability
In this paper, we extend auto-regressive models to 3D domains, and seek a stronger ability of 3D shape generation by improving auto-regressive models at capacity and scalability simultaneously.
MinD-3D: Reconstruct High-quality 3D objects in Human Brain
In this paper, we introduce Recon3DMind, an innovative task aimed at reconstructing 3D visuals from Functional Magnetic Resonance Imaging (fMRI) signals, marking a significant advancement in the fields of cognitive neuroscience and computer vision.
fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding
The exploration of brain activity and its decoding from fMRI data has been a longstanding pursuit, driven by its potential applications in brain-computer interfaces, medical diagnostics, and virtual reality.
Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation
Furthermore, we propose a multi-view fusion layer based temporal module which is equipped with a set of object slots and interacts with features from different views by attention mechanism to fulfill sufficient object representation completion.
Coarse-to-Fine Amodal Segmentation with Shape Prior
To address this issue, we propose a convolution refine module to inject fine-grained information and provide a more precise amodal object segmentation based on visual features and coarse-predicted segmentation.
Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging task, suffering from two limitations of inferior discriminative features and limited training samples.
Ranked #2 on
Person Re-Identification
on PRCC
(mAP metric)
Rethinking Person Re-identification from a Projection-on-Prototypes Perspective
In this paper, we rethink the role of the classifier in person Re-ID, and advocate a new perspective to conceive the classifier as a projection from image features to class prototypes.
Ranked #3 on
Person Re-Identification
on CUHK03
Pushing the Limits of 3D Shape Generation at Scale
Through the adaptation of the Auto-Regressive model and the utilization of large language models, we have developed a remarkable model with an astounding 3. 6 billion trainable parameters, establishing it as the largest 3D shape generation model to date, named Argus-3D.
Joint fMRI Decoding and Encoding with Latent Embedding Alignment
Our Latent Embedding Alignment (LEA) model concurrently recovers visual stimuli from fMRI signals and predicts brain activity from images within a unified framework.
Learning Versatile 3D Shape Generation with Improved AR Models
Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space.
Co-Attention Aligned Mutual Cross-Attention for Cloth-Changing Person Re-Identification
In this paper, we first design a novel Shape Semantics Embedding (SSE) module to encode body shape semantic information, which is one of the essential clues to distinguish pedestrians when their clothes change.
Ranked #8 on
Person Re-Identification
on LTCC
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction
Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction.
Learning Versatile 3D Shape Generation with Improved Auto-regressive Models
Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space.
DST: Dynamic Substitute Training for Data-free Black-box Attack
With the wide applications of deep neural network models in various computer vision tasks, more and more works study the model vulnerability to adversarial examples.
ImpDet: Exploring Implicit Fields for 3D Object Detection
Conventional 3D object detection approaches concentrate on bounding boxes representation learning with several parameters, i. e., localization, dimension, and orientation.
QS-Craft: Learning to Quantize, Scrabble and Craft for Conditional Human Motion Animation
To this end, this paper proposes a novel model of learning to Quantize, Scrabble, and Craft (QS-Craft) for conditional human motion animation.
M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging
To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic.
A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools
After more than four months study, we found that the confirmed cases of COVID-19 present the consistent ocular pathological symbols; and we propose a new screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19 with very high accuracy.
Long-Term Cloth-Changing Person Re-identification
Specifically, we consider that under cloth-changes, soft-biometrics such as body shape would be more reliable.
When Person Re-identification Meets Changing Clothes
We find that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes.
SCSP: Spectral Clustering Filter Pruning with Soft Self-adaption Manners
In this paper, we proposed a novel filter pruning for convolutional neural networks compression, namely spectral clustering filter pruning with soft self-adaption manners (SCSP).
Pose-Normalized Image Generation for Person Re-identification
Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations.
Multi-scale Deep Learning Architectures for Person Re-identification
Our model is able to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for matching.
