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- 4,660
- Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)2,522
This paper targets at learning discriminative part-informed features for person retrieval and lays emphasis on the content consistency within each part, resulting in refined parts with enhanced within-part consistency.
- Beyond Part Models: Person Retrieval with Refined Part Pooling.2,442
This paper targets at learning discriminative part-informed features for person retrieval and lays emphasis on the content consistency within each part, resulting in refined parts with enhanced within-part consistency.
- Towards Real-Time Multi-Object Tracking1,148
This work incorporates the appearance embedding model into a single-shot detector, such that the model can simultaneously output detections and the corresponding embeddings, and is formulated as a multi-task learning problem.
- MARS: A Video Benchmark for Large-Scale Person Re-Identification1,088
It is shown that CNN in classification mode can be trained from scratch using the consecutive bounding boxes of each identity, and the learned CNN embedding outperforms other competing methods considerably and has good generalization ability on other video re-id datasets upon fine-tuning.
- SVDNet for Pedestrian Retrieval825
This paper proposes the SVDNet for retrieval problems, with focus on the application of person re-identification (reID), and shows that RRI effectively reduces the correlation among the projection vectors, produces more discriminative FC descriptors, and significantly improves the re-ID accuracy.
- Person Re-identification by Video Ranking642
A novel model to automatically select the most discriminative video fragments from noisy image sequences of people where more reliable space-time features can be extracted, whilst simultaneously to learn a video ranking function for person re-id is presented.
- SegFlow: Joint Learning for Video Object Segmentation and Optical Flow446
This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos, and demonstrates that introducing optical flow improves the performance of segmentation, against the state-of-the-art algorithms.
- Perceive Where to Focus: Learning Visibility-Aware Part-Level Features for Partial Person Re-Identification388
A Visibility-aware Part Model (VPM) is proposed for partial re-ID, which learns to perceive the visibility of regions through self-supervision, which significantly improves the learned feature representation and the achieved accuracy is on par with the state of the art.
- 366
- 317
- Fast and Accurate Online Video Object Segmentation via Tracking Parts275
This paper proposes a fast and accurate video object segmentation algorithm that can immediately start the segmentation process once receiving the images, and performs favorably against state-of-the-art algorithms in accuracy on the DAVIS benchmark dataset, while achieving much faster runtime performance.
- Video Super-Resolution with Recurrent Structure-Detail Network269
A novel recurrent video super-resolution method which is both effective and efficient in exploiting previous frames to super-resolve the current frame by dividing the input into structure and detail components which are fed to a recurrent unit composed of several proposed two-stream structure-detail blocks.
- Person Re-Identification by Discriminative Selection in Video Ranking231
This work presents a novel model to automatically select the most discriminative video fragments from noisy/incomplete image sequences of people from which reliable space-time and appearance features can be computed, whilst simultaneously learning a video ranking function for person ReID.
- A survey of recent advances in visual feature detection229
The recent progress in visual feature detection is presented and future trends as well as challenges are identified and the relations among different kinds of features are covered.
- Packing and Padding: Coupled Multi-index for Accurate Image Retrieval223
A coupled Multi-Index (c-MI) framework to perform feature fusion at indexing level, which improves the retrieval accuracy significantly, while consuming only half of the query time compared to the baseline, and is well complementary to many prior techniques.
- 214
- Linkage Based Face Clustering via Graph Convolution Network200
This paper presents an accurate and scalable approach to the face clustering task, and shows that the proposed method does not need the number of clusters as prior, is aware of noises and outliers, and can be extended to a multi-view version for more accurate clustering accuracy.
- Video Super-Resolution With Temporal Group Attention199
This work proposes a novel method that can effectively incorporate temporal information in a hierarchical way and achieves favorable performance against state-of-the-art methods on several benchmark datasets.
- 152
- Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection145
A novel WS-VAD framework that focuses on efficient temporal modeling and anomaly innerclass discriminability, and a Prompt-Enhanced Learning (PEL) module that incorporates semantic priors using knowledge-based prompts to boost the discrimination of visual features while ensuring separability across anomaly subclasses.
- A2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation145
Attention Aggregation based Feature Pyramid Network (A2-FPN) is proposed, to improve multi-scale feature learning through attention-guided feature aggregation and shows consistent gains on different instance segmentation frameworks.
- VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results133
A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.
- 131
- Applying BERT to Document Retrieval with Birch129
This demonstration focuses on technical challenges in the integration of NLP and IR capabilities, along with the design rationale behind the approach to tightly-coupled integration between Python and Java Virtual Machine.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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