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Found 24 papers, 15 papers with code
UP-Person: Unified Parameter-Efficient Transfer Learning for Text-based Person Retrieval
Text-based Person Retrieval (TPR) as a multi-modal task, which aims to retrieve the target person from a pool of candidate images given a text description, has recently garnered considerable attention due to the progress of contrastive visual-language pre-trained model.
A Survey on Unlearnable Data
Unlearnable data (ULD) has emerged as an innovative defense technique to prevent machine learning models from learning meaningful patterns from specific data, thus protecting data privacy and security.
Limb-Aware Virtual Try-On Network with Progressive Clothing Warping
Finally, we introduce Limb-aware Texture Fusion (LTF) that focuses on generating realistic details in limb regions, where a coarse try-on result is first generated by fusing the warped clothing image with the person image, then limb textures are further fused with the coarse result under limb-aware guidance to refine limb details.
Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation
Recent advancements in post-training methodologies for large language models (LLMs) have highlighted reinforcement learning (RL) as a critical component for enhancing reasoning.
SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition
In our previous work, we propose a novel method to realize seamless adaptation of foundation models to VPR (SelaVPR).
Towards Visual Grounding: A Survey
Finally, we outline the challenges confronting visual grounding and propose valuable directions for future research, which may serve as inspiration for subsequent researchers.
Online Preference-based Reinforcement Learning with Self-augmented Feedback from Large Language Model
RL-SaLLM-F leverages the reflective and discriminative capabilities of LLM to generate self-augmented trajectories and provide preference labels for reward learning.
Transferable Adversarial Face Attack with Text Controlled Attribute
Extensive experiments on two high-resolution face recognition datasets validate that our TCA$^2$ method can generate natural text-guided adversarial impersonation faces with high transferability.
AlignMamba: Enhancing Multimodal Mamba with Local and Global Cross-modal Alignment
Extensive experiments on complete and incomplete multimodal fusion tasks demonstrate the effectiveness and efficiency of the proposed method.
ClickTrack: Towards Real-time Interactive Single Object Tracking
The bounding box will be used as input of single object trackers.
EMMA: Empowering Multi-modal Mamba with Structural and Hierarchical Alignment
In this work, we propose Empowering Multi-modal Mamba with Structural and Hierarchical Alignment (EMMA), which enables the MLLM to extract fine-grained visual information.
OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion
To address these challenges, we propose a novel unified open-vocabulary detection method called OV-DINO, which is pre-trained on diverse large-scale datasets with language-aware selective fusion in a unified framework.
Ranked #5 on
Zero-Shot Object Detection
on MSCOCO
(AP metric, using extra
training data)
CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition
Over the past decade, most methods in visual place recognition (VPR) have used neural networks to produce feature representations.
Deep Homography Estimation for Visual Place Recognition
Moreover, we design a re-projection error of inliers loss to train the DHE network without additional homography labels, which can also be jointly trained with the backbone network to help it extract the features that are more suitable for local matching.
Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition
Experimental results show that our method outperforms the state-of-the-art methods with less training data and training time, and uses about only 3% retrieval runtime of the two-stage VPR methods with RANSAC-based spatial verification.
Ranked #2 on
Visual Place Recognition
on Pittsburgh-250k-test
Strip-MLP: Efficient Token Interaction for Vision MLP
Finally, based on the Strip MLP layer, we propose a novel \textbf{L}ocal \textbf{S}trip \textbf{M}ixing \textbf{M}odule (LSMM) to boost the token interaction power in the local region.
Revisiting Context Aggregation for Image Matting
Furthermore, AEMatter leverages a large image training strategy to assist the network in learning context aggregation from data.
Ranked #1 on
Image Matting
on Composition-1K
Continuous Prediction of Lower-Limb Kinematics From Multi-Modal Biomedical Signals
Challenges of exoskeletons motor intent decoding schemes remain in making a continuous prediction to compensate for the hysteretic response caused by mechanical transmission.
Take More Positives: An Empirical Study of Contrastive Learing in Unsupervised Person Re-Identification
By studying two unsupervised person re-ID methods in a cross-method way, we point out a hard negative problem is handled implicitly by their designs of data augmentations and PK sampler respectively.
Regularized Fine-grained Meta Face Anti-spoofing
Besides, to further enhance the generalization ability of our model, the proposed framework adopts a fine-grained learning strategy that simultaneously conducts meta-learning in a variety of domain shift scenarios in each iteration.
Detection and Tracking of Multiple Mice Using Part Proposal Networks
The study of mouse social behaviours has been increasingly undertaken in neuroscience research.
Robust Anchor Embedding for Unsupervised Video Person Re-Identification in the Wild
After that, a robust and efficient top-k counts label prediction strategy is proposed to predict the labels of unlabeled image sequences.
Ranked #11 on
Person Re-Identification
on PRID2011
Representation Learning
Video-Based Person Re-Identification
Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection
3D mask face presentation attack, as a new challenge in face recognition, has been attracting increasing attention.
Multi-Cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation
The use of multiple features for tracking has been proved as an effective approach because limitation of each feature could be compensated.
