Search Results for author: Yap-Peng Tan

Found 20 papers, 11 papers with code

MMRel: A Relation Understanding Dataset and Benchmark in the MLLM Era

1 code implementation13 Jun 2024 Jiahao Nie, Gongjie Zhang, Wenbin An, Yap-Peng Tan, Alex C. Kot, Shijian Lu

Despite the recent advancements in Multi-modal Large Language Models (MLLMs), understanding inter-object relations, i. e., interactions or associations between distinct objects, remains a major challenge for such models.

Object Relation

Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining

1 code implementation CVPR 2024 Jiahao Nie, Yun Xing, Gongjie Zhang, Pei Yan, Aoran Xiao, Yap-Peng Tan, Alex C. Kot, Shijian Lu

Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars.

Cross-Domain Few-Shot

InteractDiffusion: Interaction Control in Text-to-Image Diffusion Models

1 code implementation CVPR 2024 Jiun Tian Hoe, Xudong Jiang, Chee Seng Chan, Yap-Peng Tan, Weipeng Hu

While recent advancements have introduced control over factors such as object localization, posture, and image contours, a crucial gap remains in our ability to control the interactions between objects in the generated content.

Human-Object Interaction Generation Object

Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger

no code implementations CVPR 2023 Yi Yu, YuFei Wang, Wenhan Yang, Shijian Lu, Yap-Peng Tan, Alex C. Kot

Extensive experiments show that with our trained trigger injection models and simple modification of encoder parameters (of the compression model), the proposed attack can successfully inject several backdoors with corresponding triggers in a single image compression model.

Backdoor Attack Face Recognition +2

Temporal Coherent Test-Time Optimization for Robust Video Classification

no code implementations28 Feb 2023 Chenyu Yi, Siyuan Yang, YuFei Wang, Haoliang Li, Yap-Peng Tan, Alex C. Kot

To exploit information in video with self-supervised learning, TeCo uses global content from video clips and optimizes models for entropy minimization.

Classification Self-Supervised Learning +1

Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond

1 code implementation CVPR 2022 Yi Yu, Wenhan Yang, Yap-Peng Tan, Alex C. Kot

Finally, we examine various types of adversarial attacks that are specific to deraining problems and their effects on both human and machine vision tasks, including 1) rain region attacks, adding perturbations only in the rain regions to make the perturbations in the attacked rain images less visible; 2) object-sensitive attacks, adding perturbations only in regions near the given objects.

Rain Removal

Learning Transferable Human-Object Interaction Detector With Natural Language Supervision

1 code implementation CVPR 2022 Suchen Wang, Yueqi Duan, Henghui Ding, Yap-Peng Tan, Kim-Hui Yap, Junsong Yuan

More specifically, we propose a new HOI visual encoder to detect the interacting humans and objects, and map them to a joint feature space to perform interaction recognition.

Human-Object Interaction Detection

Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions

1 code implementation13 Oct 2021 Chenyu Yi, Siyuan Yang, Haoliang Li, Yap-Peng Tan, Alex Kot

The state-of-the-art deep neural networks are vulnerable to common corruptions (e. g., input data degradations, distortions, and disturbances caused by weather changes, system error, and processing).

Benchmarking Computational Efficiency +1

Attention-Aware Noisy Label Learning for Image Classification

no code implementations30 Sep 2020 Zhenzhen Wang, Chunyan Xu, Yap-Peng Tan, Junsong Yuan

In this paper, the attention-aware noisy label learning approach ($A^2NL$) is proposed to improve the discriminative capability of the network trained on datasets with potential label noise.

Classification General Classification +2

Scaling Object Detection by Transferring Classification Weights

1 code implementation ICCV 2019 Jason Kuen, Federico Perazzi, Zhe Lin, Jianming Zhang, Yap-Peng Tan

Large scale object detection datasets are constantly increasing their size in terms of the number of classes and annotations count.

Classification General Classification +3

Motion-Guided Cascaded Refinement Network for Video Object Segmentation

no code implementations CVPR 2018 Ping Hu, Gang Wang, Xiangfei Kong, Jason Kuen, Yap-Peng Tan

Then, the proposed Cascaded Refinement Network(CRN) takes the coarse segmentation as guidance to generate an accurate segmentation of full resolution.

Object Optical Flow Estimation +4

Cross-Modal Deep Variational Hashing

no code implementations ICCV 2017 Venice Erin Liong, Jiwen Lu, Yap-Peng Tan, Jie zhou

In this paper, we propose a cross-modal deep variational hashing (CMDVH) method to learn compact binary codes for cross-modality multimedia retrieval.

Retrieval

DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information Inflows

1 code implementation17 Nov 2016 Jason Kuen, Xiangfei Kong, Gang Wang, Yap-Peng Tan

Deluge Networks (DelugeNets) are deep neural networks which efficiently facilitate massive cross-layer information inflows from preceding layers to succeeding layers.

General Classification

From Keyframes to Key Objects: Video Summarization by Representative Object Proposal Selection

no code implementations CVPR 2016 Jingjing Meng, Hongxing Wang, Junsong Yuan, Yap-Peng Tan

This representative selection problem is formulated as a sparse dictionary selection problem, i. e., choosing a few representatives object proposals to reconstruct the whole proposal pool.

Object Video Summarization

Deep Transfer Metric Learning

no code implementations CVPR 2015 Junlin Hu, Jiwen Lu, Yap-Peng Tan

Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same.

Face Verification Metric Learning +1

Discriminative Deep Metric Learning for Face Verification in the Wild

no code implementations CVPR 2014 Junlin Hu, Jiwen Lu, Yap-Peng Tan

This paper presents a new discriminative deep metric learning (DDML) method for face verification in the wild.

Face Verification Metric Learning

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