activity
20242026
most citedByteNet: Rethinking Multimedia File Fragment Classification through Visual Perspectives

2 citations · 3 across the 11 of their papers we have counts for

collaborators

14 papers

cs.CV2026

BiNar: Bitstream Native Restorer for Bitstream-corrupted Image Restoration

Fangcheng Li, Zhen Yu, Kejun Wu +1

Bitstream-corrupted image restoration (BCIR) aims to reconstruct faithful images from corrupted compressed bitstreams. However, corruption occurs in bitstream-domain may lead to un…

cs.CV2026

Reference-Efficient Multimodal Bitstream-Corrupted Video Recovery via Depth-Conditioned Adaptation

Tianyi Liu, Jiaojiao Wang, Ye Lu +4

Recovering visually faithful content from damaged video bitstreams is important for reliable multimedia communication and storage. Existing methods exploit local residuals and non-…

cs.CV2026

AesCanvas: A Large-Scale Dataset and Benchmark for Aesthetic Critique and Contextual Suitability

Xuanwei Hu, Haoyu Dong, Kejun Wu +2

Recent advances in Multimodal Large Language Models (MLLMs) have extended Image Aesthetic Assessment (IAA) beyond scalar scores toward interpretable critique and guidance. Yet exis…

cs.CV2026

FLM: Frequency-Aware Language Models for Generative Image Compression

Jiarun Chen, Kejun Wu, Li Li +3

Generative models have significantly improved the performance ceiling of image lossy compression at low bitrates by exploiting learned priors. However, the generated textures and s…

cs.CV2026

DriftAD: Visually-Guided Text Drift for Few-Shot Industrial Anomaly Detection

Wenyang Liu, Tianyi Liu, Dongshuo Zhang +2

Few-shot anomaly detection (FSAD) has recently benefited from vision-language models such as CLIP, which enable anomaly de?tection by aligning visual features with text description…

cs.CV2026

Towards Bitstream-corrupted Harsh Visual Understanding: Through Bitstream Language Modeling as Robust Semantic Priors

Chaoran Huang, Fangcheng Li, Tianyi Liu +2

Bitstream-corrupted Harsh Visual Understanding (BcHVU) aims to understand harshly degraded videos originally decoded from a severely corrupted bitstream in real-world multimedia co…

cs.CV2026

Bitstream Action Recognition is Byte Modeling

Fangcheng Li, Chaoran Huang, Tianyi Liu +5

Conventional action recognition typically relies on successful pixel decoding of the bitstream. However, bitstream corruption during storage or transmission may cause severe visual…

cs.CV2026

NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results

Wenbin Zou, Tianyi Liu, Kejun Wu +37

This paper reports on the NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration (BSCVR). The challenge aims to advance research on recovering visually coherent videos from…

cs.CV2025

Building Egocentric Procedural AI Assistant: Methods, Benchmarks, and Challenges

Junlong Li, Huaiyuan Xu, Sijie Cheng +4

Driven by recent advances in vision-language models (VLMs) and egocentric perception research, the emerging topic of an egocentric procedural AI assistant (EgoProceAssist) is intro…

eess.IV2025★ 1 cited

Towards Blind Bitstream-corrupted Video Recovery via a Visual Foundation Model-driven Framework

Tianyi Liu, Kejun Wu, Chen Cai +3

Video signals are vulnerable in multimedia communication and storage systems, as even slight bitstream-domain corruption can lead to significant pixel-domain degradation. To recove…

cs.CV2025

PromptSR: Cascade Prompting for Lightweight Image Super-Resolution

Wenyang Liu, Chen Cai, Jianjun Gao +4

Although the lightweight Vision Transformer has significantly advanced image super-resolution (SR), it faces the inherent challenge of a limited receptive field due to the window-b…

cs.CV2025

From Semantics, Scene to Instance-awareness: Distilling Foundation Model for Grounded Open-vocabulary Situation Recognition

Chen Cai, Tianyi Liu, Jianjun Gao +5

Recent Multimodal Large Language Models (MLLMs) exhibit strong zero-shot abilities but struggle with complex Grounded Situation Recognition (GSR) and are resource-intensive for edg…

cs.CV2024★ 2 cited

ByteNet: Rethinking Multimedia File Fragment Classification through Visual Perspectives

Wenyang Liu, Kejun Wu, Tianyi Liu +3

Multimedia file fragment classification (MFFC) aims to identify file fragment types, e.g., image/video, audio, and text without system metadata. It is of vital importance in multim…

cs.CV2024

Video sentence grounding with temporally global textual knowledge

Cai Chen, Runzhong Zhang, Jianjun Gao +3

Temporal sentence grounding involves the retrieval of a video moment with a natural language query. Many existing works directly incorporate the given video and temporally localize…