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cs.CV2025

DreamActor-H1: High-Fidelity Human-Product Demonstration Video Generation via Motion-designed Diffusion Transformers

Lizhen Wang, Zhurong Xia, Tianshu Hu +6

In e-commerce and digital marketing, generating high-fidelity human-product demonstration videos is important for effective product presentation. However, most existing frameworks…

cs.CV2025

GLAD: Generalizable Tuning for Vision-Language Models

Yuqi Peng, Pengfei Wang, Jianzhuang Liu +1

Pre-trained vision-language models, such as CLIP, show impressive zero-shot recognition ability and can be easily transferred to specific downstream tasks via prompt tuning, even w…

cs.CV2025

Exploring Implicit Visual Misunderstandings in Multimodal Large Language Models through Attention Analysis

Pengfei Wang, Guohai Xu, Weinong Wang +3

Recent advancements have enhanced the capability of Multimodal Large Language Models (MLLMs) to comprehend multi-image information. However, existing benchmarks primarily evaluate…

cs.CV2025

Qwen2.5-VL Technical Report

Shuai Bai, Keqin Chen, Xuejing Liu +24

We introduce Qwen2.5-VL, the latest flagship model of Qwen vision-language series, which demonstrates significant advancements in both foundational capabilities and innovative func…

cs.CV2024

FreqMixFormerV2: Lightweight Frequency-aware Mixed Transformer for Human Skeleton Action Recognition

Wenhan Wu, Pengfei Wang, Chen Chen +1

Transformer-based human skeleton action recognition has been developed for years. However, the complexity and high parameter count demands of these models hinder their practical ap…

cs.CV2024

CC-OCR: A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy

Zhibo Yang, Jun Tang, Zhaohai Li +9

Large Multimodal Models (LMMs) have demonstrated impressive performance in recognizing document images with natural language instructions. However, it remains unclear to what exten…