Search Results for author: Tianyu Zhu

Found 11 papers, 7 papers with code

Video Quality Assessment: A Comprehensive Survey

no code implementations4 Dec 2024 Qi Zheng, Yibo Fan, Leilei Huang, Tianyu Zhu, Jiaming Liu, Zhijian Hao, Shuo Xing, Chia-Ju Chen, Xiongkuo Min, Alan C. Bovik, Zhengzhong Tu

Video quality assessment (VQA) is an important processing task, aiming at predicting the quality of videos in a manner highly consistent with human judgments of perceived quality.

Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering

1 code implementation20 Jun 2024 Yihong Wu, Le Zhang, Fengran Mo, Tianyu Zhu, Weizhi Ma, Jian-Yun Nie

By examining the learning dynamics and equilibrium of the contrastive loss, we offer a fresh lens to understand contrastive learning via graph theory, emphasizing its capability to capture high-order connectivity.

Collaborative Filtering Contrastive Learning

Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking

1 code implementation12 Apr 2024 Tianyu Zhu, Myong Chol Jung, Jesse Clark

Contrastive learning has gained widespread adoption for retrieval tasks due to its minimal requirement for manual annotations.

Contrastive Learning Retrieval

History-Aware Conversational Dense Retrieval

1 code implementation30 Jan 2024 Fengran Mo, Chen Qu, Kelong Mao, Tianyu Zhu, Zhan Su, Kaiyu Huang, Jian-Yun Nie

To address the aforementioned issues, we propose a History-Aware Conversational Dense Retrieval (HAConvDR) system, which incorporates two ideas: context-denoised query reformulation and automatic mining of supervision signals based on the actual impact of historical turns.

Conversational Search Information Retrieval +1

Attack and Defense Analysis of Learned Image Compression

no code implementations18 Jan 2024 Tianyu Zhu, Heming Sun, Xiankui Xiong, Xuanpeng Zhu, Yong Gong, Minge jing, Yibo Fan

Our experiments compare the effects of different dimensions such as attack methods, models, qualities, and targets, concluding that in the worst case, there is a 61. 55% decrease in PSNR or a 19. 15 times increase in bpp under the PGD attack.

Image Compression

Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation

1 code implementation2 Nov 2023 Tianyu Zhu, Yansong Shi, Yuan Zhang, Yihong Wu, Fengran Mo, Jian-Yun Nie

Second, we develop a transition-aware embedding distillation module that distills global item-to-item transition patterns into item embeddings, which enables the model to memorize and leverage transitional signals and serves as a calibrator for collaborative signals.

Sequential Recommendation

Knowledge Combination to Learn Rotated Detection Without Rotated Annotation

1 code implementation CVPR 2023 Tianyu Zhu, Bryce Ferenczi, Pulak Purkait, Tom Drummond, Hamid Rezatofighi, Anton Van Den Hengel

Annotating rotated bounding boxes is such a laborious process that they are not provided in many detection datasets where axis-aligned annotations are used instead.

Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning

1 code implementation7 Dec 2021 Rongkai Ma, Pengfei Fang, Gil Avraham, Yan Zuo, Tianyu Zhu, Tom Drummond, Mehrtash Harandi

A principle way of achieving few-shot learning is to realize a model that can rapidly adapt to the context of a given task.

Few-Shot Learning Novel Concepts

Learning Online for Unified Segmentation and Tracking Models

no code implementations12 Nov 2021 Tianyu Zhu, Rongkai Ma, Mehrtash Harandi, Tom Drummond

A segmentation model cannot easily learn from prior information given in the visual tracking scenario.

Meta-Learning Visual Tracking

Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers

1 code implementation27 Mar 2021 Tianyu Zhu, Markus Hiller, Mahsa Ehsanpour, Rongkai Ma, Tom Drummond, Ian Reid, Hamid Rezatofighi

Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field.

Multi-Object Tracking Object +1

Learn to Predict Sets Using Feed-Forward Neural Networks

no code implementations30 Jan 2020 Hamid Rezatofighi, Tianyu Zhu, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Anton Milan, Daniel Cremers, Laura Leal-Taixé, Ian Reid

In our formulation we define a likelihood for a set distribution represented by a) two discrete distributions defining the set cardinally and permutation variables, and b) a joint distribution over set elements with a fixed cardinality.

Multi-Label Image Classification object-detection +1

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