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20162024
most citedVideo Question Answering via Attribute-Augmented Attention Network Learning

103 citations · 716 across the 66 of their papers we have counts for

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Showing 2023 · cs.CVShow all

18 papers · 2 filters

cs.CV2023

DECap: Towards Generalized Explicit Caption Editing via Diffusion Mechanism

Zhen Wang, Xinyun Jiang, Jun Xiao +2

Explicit Caption Editing (ECE) -- refining reference image captions through a sequence of explicit edit operations (e.g., KEEP, DETELE) -- has raised significant attention due to i…

cs.CV2023★ 1 cited

Compositional Zero-shot Learning via Progressive Language-based Observations

Lin Li, Guikun Chen, Zhen Wang +2

Compositional zero-shot learning aims to recognize unseen state-object compositions by leveraging known primitives (state and object) during training. However, effectively modeling…

cs.CV2023

Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond

Zhecan Wang, Long Chen, Haoxuan You +6

Vision-language (VL) understanding tasks evaluate models' comprehension of complex visual scenes through multiple-choice questions. However, we have identified two dataset biases t…

cs.CV2023

Video Referring Expression Comprehension via Transformer with Content-conditioned Query

Ji Jiang, Meng Cao, Tengtao Song +3

Video Referring Expression Comprehension (REC) aims to localize a target object in videos based on the queried natural language. Recent improvements in video REC have been made usi…

cs.CV2023

MEDOE: A Multi-Expert Decoder and Output Ensemble Framework for Long-tailed Semantic Segmentation

Junao Shen, Long Chen, Kun Kuang +3

Long-tailed distribution of semantic categories, which has been often ignored in conventional methods, causes unsatisfactory performance in semantic segmentation on tail categories…

cs.CV2023★ 1 cited

UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory

Haiwen Diao, Bo Wan, Ying Zhang +3

Parameter-efficient transfer learning (PETL), i.e., fine-tuning a small portion of parameters, is an effective strategy for adapting pre-trained models to downstream domains. To fu…