Search Results for author:
Found 19 papers, 6 papers with code
MAIR: A Massive Benchmark for Evaluating Instructed Retrieval
Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially generalize to unseen tasks with instructions.
Semi-supervised Text-based Person Search
Text-based person search (TBPS) aims to retrieve images of a specific person from a large image gallery based on a natural language description.
An Empirical Study of Frame Selection for Text-to-Video Retrieval
In this paper, we make the first empirical study of frame selection for TVR.
On the Evaluation and Refinement of Vision-Language Instruction Tuning Datasets
Instead of evaluating the models directly, in this paper, we try to evaluate the Vision-Language Instruction-Tuning (VLIT) datasets.
Unpaired Multi-domain Attribute Translation of 3D Facial Shapes with a Square and Symmetric Geometric Map
We propose a learning framework for 3D facial attribute translation to relieve these limitations.
An Empirical Study of CLIP for Text-based Person Search
TPBS, as a fine-grained cross-modal retrieval task, is also facing the rise of research on the CLIP-based TBPS.
Ranked #6 on
Text based Person Retrieval
on RSTPReid
RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search
RA offsets the overfitting risk by introducing a novel positive relation detection task (i. e., learning to distinguish strong and weak positive pairs).
Ranked #3 on
Text based Person Retrieval
on RSTPReid
Text-based Person Search without Parallel Image-Text Data
Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description.
Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening
The proposed framework equipped with only two embedding layers achieves $O(1)$ querying time complexity, while improving the retrieval efficiency and keeping its performance, when applied prior to the common image-text retrieval methods.
Rethinking Visual Prompt Learning as Masked Visual Token Modeling
To explore prompt learning on the generative pre-trained visual model, as well as keeping the task consistency, we propose Visual Prompt learning as masked visual Token Modeling (VPTM) to transform the downstream visual classification into the pre-trained masked visual token prediction.
M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios
In realistic open-set scenarios where labels of a part of testing data are totally unknown, when vision-language (VL) prompt learning methods encounter inputs related to unknown classes (i. e., not seen during training), they always predict them as one of the training classes.
End-to-End Context-Aided Unicity Matching for Person Re-identification
Based on a natural assumption that images belonging to the same person identity should not match with images belonging to multiple different person identities across views, called the unicity of person matching on the identity level, we propose an end-to-end person unicity matching architecture for learning and refining the person matching relations.
Visual Subtitle Feature Enhanced Video Outline Generation
Furthermore, based on the similarity between video outlines and textual outlines, we use a large number of articles with chapter headings to pretrain our model.
Revising Image-Text Retrieval via Multi-Modal Entailment
While the builders of existing image-text retrieval datasets strive to ensure that the caption matches the linked image, they cannot prevent a caption from fitting other images.
Image-text Retrieval: A Survey on Recent Research and Development
On top of this, the efficiency-focused study on the ITR system is introduced as the third perspective.
Learning Semantic-Aligned Feature Representation for Text-based Person Search
In this paper, we propose a semantic-aligned embedding method for text-based person search, in which the feature alignment across modalities is achieved by automatically learning the semantic-aligned visual features and textual features.
Ranked #11 on
Text based Person Retrieval
on CUHK-PEDES
Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification
Most existing person re-identification methods compute pairwise similarity by extracting robust visual features and learning the discriminative metric.
Key Person Aided Re-identification in Partially Ordered Pedestrian Set
Ideally person re-identification seeks for perfect feature representation and metric model that re-identify all various pedestrians well in non-overlapping views at different locations with different camera configurations, which is very challenging.
Recognition of convolutional neural network based on CUDA Technology
For the problem whether Graphic Processing Unit(GPU), the stream processor with high performance of floating-point computing is applicable to neural networks, this paper proposes the parallel recognition algorithm of Convolutional Neural Networks(CNNs). It adopts Compute Unified Device Architecture(CUDA)technology, definite the parallel data structures, and describes the mapping mechanism for computing tasks on CUDA.
