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20242026
most citedACTRESS: Active Retraining for Semi-supervised Visual Grounding

1 citations · 1 across the 8 of their papers we have counts for

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cs.CV2026

Inline Critic Steers Image Editing

Weitai Kang, Xiaohang Zhan, Yizhou Wang +4

Instruction-based image editing exhibits heterogeneous difficulty not only across cases but also across regions of an image, motivating refinement approaches that allocate correcti…

cs.CV2025

VGent: Visual Grounding via Modular Design for Disentangling Reasoning and Prediction

Weitai Kang, Jason Kuen, Mengwei Ren +3

Current visual grounding models are either based on a Multimodal Large Language Model (MLLM) that performs auto-regressive decoding, which is slow and risks hallucinations, or on r…

cs.CV2025

From Particles to Fields: Reframing Photon Mapping with Continuous Gaussian Photon Fields

Jiachen Tao, Benjamin Planche, Van Nguyen Nguyen +13

Accurately modeling light transport is essential for realistic image synthesis. Photon mapping provides physically grounded estimates of complex global illumination effects such as…

cs.CV2025

ExpVG: Investigating the Design Space of Visual Grounding in Multimodal Large Language Model

Weitai Kang, Weiming Zhuang, Zhizhong Li +2

Fine-grained multimodal capability in Multimodal Large Language Models (MLLMs) has emerged as a critical research direction, particularly for tackling the visual grounding (VG) pro…

cs.CV2025

3DResT: A Strong Baseline for Semi-Supervised 3D Referring Expression Segmentation

Wenxin Chen, Mengxue Qu, Weitai Kang +3

3D Referring Expression Segmentation (3D-RES) typically requires extensive instance-level annotations, which are time-consuming and costly. Semi-supervised learning (SSL) mitigates…

cs.CV2024

Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner

Yuzhang Shang, Bingxin Xu, Weitai Kang +7

Advancements in Large Language Models (LLMs) inspire various strategies for integrating video modalities. A key approach is Video-LLMs, which incorporate an optimizable interface l…