List of papers related to State Space Models (Mamba) in Vision.

[NeurIPS2023] LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning

[Survey@Pattern Recognition] Paper list on Pedestrian Attribute Recognition (PAR) and related tasks (Pattern Recognition 2021)

A Semantic Controllable Self-Supervised Learning Framework to learn general human representations from massive unlabeled human images, which can benefit downstream human-centric tasks to the maximu…

Official PyTorch implementation of UniHCP

【AAAI 2024】An Empirical Study of CLIP for Text-based Person Search

Code for ICCV 2021 paper Pyramid Spatial-Temporal Aggregation for Video-based Person Re-Identification

Pytorch implementation of 'Clothes-Changing Person Re-identification with RGB Modality Only. In CVPR, 2022.'

【AAAI'2023 & IJCV】Transferring Vision-Language Models for Visual Recognition: A Classifier Perspective

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

This Repo. is used for our ACM MM2023 paper: Recurrent Multi-scale Transformer for High-Resolution Salient Object Detection

Official implementation of AAAI 2023 paper "Parameter-efficient Model Adaptation for Vision Transformers"

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

The official code of "PLIP: Language-Image Pre-training for Person Representation Learning"

[CVPR2024] The code of "UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory"

Code for ICCV 2021 paper Pyramid Spatial-Temporal Aggregation for Video-based Person Re-Identification

Official repository for "Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting" [CVPR 2023]

Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV)

Awesome list for research on CLIP (Contrastive Language-Image Pre-Training).

[CVPR 2023] Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners