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20212026
most citedPractical No-box Adversarial Attacks with Training-free Hybrid Image Transformation

18 citations · 139 across the 44 of their papers we have counts for

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Showing 2023 · cs.CVShow all

17 papers · 2 filters

cs.CV2023

Make-A-Storyboard: A General Framework for Storyboard with Disentangled and Merged Control

Sitong Su, Litao Guo, Lianli Gao +2

Story Visualization aims to generate images aligned with story prompts, reflecting the coherence of storybooks through visual consistency among characters and scenes.Whereas curren…

cs.CV2023★ 8 cited

F3-Pruning: A Training-Free and Generalized Pruning Strategy towards Faster and Finer Text-to-Video Synthesis

Sitong Su, Jianzhi Liu, Lianli Gao +1

Recently Text-to-Video (T2V) synthesis has undergone a breakthrough by training transformers or diffusion models on large-scale datasets. Nevertheless, inferring such large models…

cs.CV2023

ALF: Adaptive Label Finetuning for Scene Graph Generation

Qishen Chen, Jianzhi Liu, Xinyu Lyu +3

Scene Graph Generation (SGG) endeavors to predict the relationships between subjects and objects in a given image. Nevertheless, the long-tail distribution of relations often leads…

cs.CV2023

ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval

Kaipeng Fang, Jingkuan Song, Lianli Gao +4

The goal of Universal Cross-Domain Retrieval (UCDR) is to achieve robust performance in generalized test scenarios, wherein data may belong to strictly unknown domains and categori…

cs.CV2023

Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection

Tao He, Lianli Gao, Jingkuan Song +1

Scene graph generation (SGG) and human-object interaction (HOI) detection are two important visual tasks aiming at localising and recognising relationships between objects, and int…

cs.CV2023

From Channel Bias to Feature Redundancy: Uncovering the "Less is More" Principle in Few-Shot Learning

Ji Zhang, Xu Luo, Lianli Gao +3

Deep neural networks often fail to adapt representations to novel tasks under distribution shifts, especially when only a few examples are available. This paper identifies a core o…

cs.CV2023

Local-Global Information Interaction Debiasing for Dynamic Scene Graph Generation

Xinyu Lyu, Jingwei Liu, Yuyu Guo +1

The task of dynamic scene graph generation (DynSGG) aims to generate scene graphs for given videos, which involves modeling the spatial-temporal information in the video. However,…

cs.CV2023★ 6 cited

Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval

Hao Li, Jingkuan Song, Lianli Gao +2

Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space. However, the predictions are ofte…

cs.CV2023

DePT: Decoupled Prompt Tuning

Ji Zhang, Shihan Wu, Lianli Gao +2

This work breaks through the Base-New Tradeoff (BNT)dilemma in prompt tuning, i.e., the better the tuned model generalizes to the base (or target) task, the worse it generalizes to…

cs.CV2023★ 1 cited

CIParsing: Unifying Causality Properties into Multiple Human Parsing

Xiaojia Chen, Xuanhan Wang, Lianli Gao +3

Existing methods of multiple human parsing (MHP) apply statistical models to acquire underlying associations between images and labeled body parts. However, acquired associations o…

cs.CV2023

From Global to Local: Multi-scale Out-of-distribution Detection

Ji Zhang, Lianli Gao, Bingguang Hao +3

Out-of-distribution (OOD) detection aims to detect "unknown" data whose labels have not been seen during the in-distribution (ID) training process. Recent progress in representatio…

cs.CV2023★ 2 cited

Informative Scene Graph Generation via Debiasing

Lianli Gao, Xinyu Lyu, Yuyu Guo +5

Scene graph generation aims to detect visual relationship triplets, (subject, predicate, object). Due to biases in data, current models tend to predict common predicates, e.g. "on"…

cs.CV2023★ 3 cited

Generalized Unbiased Scene Graph Generation

Xinyu Lyu, Lianli Gao, Junlin Xie +4

Existing Unbiased Scene Graph Generation (USGG) methods only focus on addressing the predicate-level imbalance that high-frequency classes dominate predictions of rare ones, while…

cs.CV2023

MobileVidFactory: Automatic Diffusion-Based Social Media Video Generation for Mobile Devices from Text

Junchen Zhu, Huan Yang, Wenjing Wang +8

Videos for mobile devices become the most popular access to share and acquire information recently. For the convenience of users' creation, in this paper, we present a system, name…

cs.CV2023★ 5 cited

Prototype-based Embedding Network for Scene Graph Generation

Chaofan Zheng, Xinyu Lyu, Lianli Gao +2

Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs. However, due to the diverse visual appearance of numerous p…

cs.CV2023★ 3 cited

Boosting Adversarial Attacks by Leveraging Decision Boundary Information

Boheng Zeng, LianLi Gao, QiLong Zhang +3

Due to the gap between a substitute model and a victim model, the gradient-based noise generated from a substitute model may have low transferability for a victim model since their…

cs.CV2023★ 2 cited

DETA: Denoised Task Adaptation for Few-Shot Learning

Ji Zhang, Lianli Gao, Xu Luo +2

Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge of the test task, rely only on few-labeled suppo…