CLIP-DFGS: A Hard Sample Mining Method for CLIP in Generalizable Person Re-Identification
Recent advancements in pre-trained vision-language models like CLIP have shown promise in person re-identification (ReID) applications. However, their performance in generalizable person re-identification tasks remains suboptimal. The large-scale and diverse image-text pairs used in CLIP's pre-training may lead to a lack or insufficiency of certain fine-grained features. In light of these challenges, we propose a hard sample mining method called DFGS (Depth-First Graph Sampler), based on depth-first search, designed to offer sufficiently challenging samples to enhance CLIP's ability to extract fine-grained features. DFGS can be applied to both the image encoder and the text encoder in CLIP. By leveraging the powerful cross-modal learning capabilities of CLIP, we aim to apply our DFGS method to extract challenging samples and form mini-batches with high discriminative difficulty, providing the image model with more efficient and challenging samples that are difficult to distinguish, thereby enhancing the model's ability to differentiate between individuals. Our results demonstrate significant improvements over other methods, confirming the effectiveness of DFGS in providing challenging samples that enhance CLIP's performance in generalizable person re-identification.
Code (0)
등록된 구현이 없습니다.
Tasks
Generalizable Person Re-identificationPerson Re-IdentificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
No Captions, No Problem: Captionless 3D-CLIP Alignment with Hard Negatives via CLIP Knowledge and LLMs
In this study, we explore an alternative approach to enhance contrastive text-image-3D alignment in the absence of textual descriptions for 3D objects. We introduce two unsupervised methods, $I2I$ and $(I2L)^2$, which le…
3D ClassificationCross-Modal RetrievalImage RetrievalRetrievalFG-CLIP: Fine-Grained Visual and Textual Alignment
Contrastive Language-Image Pre-training (CLIP) excels in multimodal tasks such as image-text retrieval and zero-shot classification but struggles with fine-grained understanding due to its focus on coarse-grained short c…
Image-text Retrievalobject-detectionObject DetectionOpen-vocabulary object detection+5InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection
Sarcasm in social media, often expressed through text-image combinations, poses challenges for sentiment analysis and intention mining. Current multi-modal sarcasm detection methods have been demonstrated to overly rely …
Sarcasm DetectionSentiment AnalysisBalanced Data Sampling for Language Model Training with Clustering
Data plays a fundamental role in the training of Large Language Models (LLMs). While attention has been paid to the collection and composition of datasets, determining the data sampling strategy in training remains an op…
ClusteringLanguage ModelingLanguage ModellingCLIPCleaner: Cleaning Noisy Labels with CLIP
Learning with Noisy labels (LNL) poses a significant challenge for the Machine Learning community. Some of the most widely used approaches that select as clean samples for which the model itself (the in-training model) h…
Learning with noisy labels