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20242026
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cs.CV2026

Scaling Multi-Reference Image Generation with Dynamic Reward Optimization

Wenwang Huang, Yusen Fu, Junjie Wang +6

While personalized image generation has achieved remarkable progress, multi-reference image generation (MRIG) remains a challenging task. Most existing benchmarks fail to adequatel…

cs.CV2026

Benchmarking and Evolving Reason-Reflect-Rectify for Reflective Visual Generation

Junjie Wang, Xinghua Lou, Jason Li +8

Text-to-Image (T2I) models and Unified Multimodal Models (UMMs) have achieved remarkable progress in visual generation. However, their reliance on a single-pass generation paradigm…

cs.CV2026

AgentSteerTTS: A Multi-Agent Closed-Loop Framework for Composite-Instruction Text-to-Speech

Bin Kang, Shaoguo Wen, Yang Fan +6

While existing text-to-speech (TTS) models exhibit high expressiveness, fine-grained control over composite instructions remains challenging due to the structural mismatch between…

cs.CV2026

FlashVID: Efficient Video Large Language Models via Training-free Tree-based Spatiotemporal Token Merging

Ziyang Fan, Keyu Chen, Ruilong Xing +3

Although Video Large Language Models (VLLMs) have shown remarkable capabilities in video understanding, they are required to process high volumes of visual tokens, causing signific…

cs.CV2025

Less Is More, but Where? Dynamic Token Compression via LLM-Guided Keyframe Prior

Yulin Li, Haokun Gui, Ziyang Fan +4

Recent advances in Video Large Language Models (VLLMs) have achieved remarkable video understanding capabilities, yet face critical efficiency bottlenecks due to quadratic computat…

cs.CV2025

CalibCLIP: Contextual Calibration of Dominant Semantics for Text-Driven Image Retrieval

Bin Kang, Bin Chen, Junjie Wang +3

Existing Visual Language Models (VLMs) suffer structural limitations where a few low contribution tokens may excessively capture global semantics, dominating the information aggreg…