9 citations · 10 across the 12 of their papers we have counts for
13 papers · 1 filter
Benchmarking Vision-Language Models for Microscopic Plant Image Understanding
Tianqi Wei, Xin Yu, Zhi Chen +2
Microscopic imaging provides essential visual evidence for studying plant biology and pathology at the cellular and subcellular levels. However, existing benchmarks on vision-langu…
StomataSeg: Semi-Supervised Instance Segmentation for Sorghum Stomatal Components
Zhongtian Huang, Zhi Chen, Zi Huang +8
Sorghum is a globally important cereal grown widely in water-limited and stress-prone regions. Its strong drought tolerance makes it a priority crop for climate-resilient agricultu…
Generative Recall, Dense Reranking: Learning Multi-View Semantic IDs for Efficient Text-to-Video Retrieval
Zecheng Zhao, Zhi Chen, Zi Huang +2
Text-to-Video Retrieval (TVR) is essential in video platforms. Dense retrieval with dual-modality encoders leads in accuracy, but its computation and storage scale poorly with corp…
Cluster-Aware Prompt Ensemble Learning for Few-Shot Vision-Language Model Adaptation
Zhi Chen, Xin Yu, Xiaohui Tao +2
Vision-language models (VLMs) such as CLIP achieve zero-shot transfer across various tasks by pre-training on numerous image-text pairs. These models often benefit from using an en…
Augment to Segment: Tackling Pixel-Level Imbalance in Wheat Disease and Pest Segmentation
Tianqi Wei, Xin Yu, Zhi Chen +2
Accurate segmentation of foliar diseases and insect damage in wheat is crucial for effective crop management and disease control. However, the insect damage typically occupies only…
Are Synthetic Videos Useful? A Benchmark for Retrieval-Centric Evaluation of Synthetic Videos
Zecheng Zhao, Selena Song, Tong Chen +3
Text-to-video (T2V) synthesis has advanced rapidly, yet current evaluation metrics primarily capture visual quality and temporal consistency, offering limited insight into how synt…
Track Any Peppers: Weakly Supervised Sweet Pepper Tracking Using VLMs
Jia Syuen Lim, Yadan Luo, Zhi Chen +3
In the Detection and Multi-Object Tracking of Sweet Peppers Challenge, we present Track Any Peppers (TAP) - a weakly supervised ensemble technique for sweet peppers tracking. TAP l…
CF-PRNet: Coarse-to-Fine Prototype Refining Network for Point Cloud Completion and Reconstruction
Zhi Chen, Tianqi Wei, Zecheng Zhao +6
In modern agriculture, precise monitoring of plants and fruits is crucial for tasks such as high-throughput phenotyping and automated harvesting. This paper addresses the challenge…
PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation
Tianqi Wei, Zhi Chen, Xin Yu +3
Plant diseases pose significant threats to agriculture. It necessitates proper diagnosis and effective treatment to safeguard crop yields. To automate the diagnosis process, image…
Snap and Diagnose: An Advanced Multimodal Retrieval System for Identifying Plant Diseases in the Wild
Tianqi Wei, Zhi Chen, Xin Yu
Plant disease recognition is a critical task that ensures crop health and mitigates the damage caused by diseases. A handy tool that enables farmers to receive a diagnosis based on…
FastEdit: Fast Text-Guided Single-Image Editing via Semantic-Aware Diffusion Fine-Tuning
Zhi Chen, Zecheng Zhao, Yadan Luo +1
Conventional Text-guided single-image editing approaches require a two-step process, including fine-tuning the target text embedding for over 1K iterations and the generative model…
Benchmarking In-the-wild Multimodal Disease Recognition and A Versatile Baseline
Tianqi Wei, Zhi Chen, Zi Huang +1
Existing plant disease classification models have achieved remarkable performance in recognizing in-laboratory diseased images. However, their performance often significantly degra…
DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection
Jia Syuen Lim, Zhuoxiao Chen, Mahsa Baktashmotlagh +4
Class-agnostic object detection (OD) can be a cornerstone or a bottleneck for many downstream vision tasks. Despite considerable advancements in bottom-up and multi-object discover…