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Jiedong Zhuang

7 accepted papers

2026

Q Cache: Visual Attention Is Valuable in Less than Half of Decode Layers for Multimodal Large Language Model

AAAI 2026technical

Multimodal large language models (MLLMs) are plagued by exorbitant inference costs attributable to the profusion of visual tokens within the vision encoder. The redundant visual tokens engenders a substantial computational load and key-value (KV) cache footprint bottleneck. Existing approaches focus

Cited by 0SourcePDFScholar
2026

VideoSEG-O3: A Multi-turn Reinforcement Learning Framework for Reasoning Video Object Segmentation

ICML 2026poster

Reasoning Video Object Segmentation (RVOS) demands a sophisticated integration of temporal dynamics, spatial details, and linguistic reasoning to achieve precise pixel-level localization. Existing methods are limited to reasoning over fixed initial inputs and lack the capacity to actively acquire fu…

Cited by 0SourceScholar
2025

Multi-task Visual Grounding with Coarse-to-Fine Consistency Constraints

AAAI 2025technical

Multi-task visual grounding involves the simultaneous execution of localization and segmentation in images based on textual expressions. The majority of advanced methods predominantly focus on transformer-based multimodal fusion, aiming to extract robust multimodal representations. However, ambiguit…

2025

PropVG: End-to-End Proposal-Driven Visual Grounding with Multi-Granularity Discrimination

ICCV 2025poster

Recent advances in visual grounding have largely shifted away from traditional proposal-based two-stage frameworks due to their inefficiency and high computational complexity, favoring end-to-end direct reference paradigms. However, these methods rely exclusively on the referred target for supervisi…

2025

ST3: Accelerating Multimodal Large Language Model by Spatial-Temporal Visual Token Trimming

AAAI 2025technical

Multimodal large language models (MLLMs) enhance their perceptual capabilities by integrating visual and textual information. However, processing the massive number of visual tokens incurs a significant computational cost. Existing analysis of the MLLM attention mechanisms remains shallow, leading t…

Cited by 2SourcePDFScholar
2024

FALIP: Visual Prompt as Foveal Attention Boosts CLIP Zero-Shot Performance

ECCV 2024poster

"CLIP has achieved impressive zero-shot performance after pretraining on a large-scale dataset consisting of paired image-text data. Previous works have utilized CLIP by incorporating manually designed visual prompts like colored circles and blur masks into the images to guide the model’s attention,…

2024

FashionR2R: Texture-preserving Rendered-to-Real Image Translation with Diffusion Models

NeurIPS 2024poster

Modeling and producing lifelike clothed human images has attracted researchers' attention from different areas for decades, with the complexity from highly articulated and structured content. Rendering algorithms decompose and simulate the imaging process of a camera, while are limited by the accura…

Cited by 1SourcePDFScholar