arXiv subjects

Zhecheng Shi

Publications and source records attributed to Zhecheng Shi.

5 recordsLinked to original sources

arXiv2026

OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice

The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary management. However, in the domain of food systems, autonomous agents face a unique and persistent challenge: the "Systemic Information Asymmetry" between visual appearance and intrinsic nutritional composition. Existing benchmarks primarily focus on coarse-grained classification tasks, such as food category recognition, which fail to evaluate the intricate reasoning chain required for real-world dietary management -- specifically, the ability to traverse from identifying hidden ingredients to estimating physical mass, and finally synthesizing safety-critical medical advice. In this paper, we introduce OmniFood-Bench, a comprehensive benchmark constructed from the MM-Food-100K dataset. Unlike previous works, OmniFood-Bench evaluates VLMs across three progressive capabilities: Basic Perception (Ingredients & Cooking Methods), Quantitative Reasoning (Portion Size & Nutritional Profiling), and Safety-Critical Advisory (Disease-Specific Recommendations). We evaluate six state-of-the-art VLMs, including gpt-5.1, gemini-3-flash, and qwen3-vl-8B. Our extensive experiments reveal a startling "Semantic-Physical Gap": while models achieve near-human accuracy in naming dishes, they exhibit catastrophic failure in mass estimation and frequently hallucinate benign advice for high-risk diabetic profiles. This work establishes a rigorous standard for trustworthiness in autonomous agents deployed for public health. The code and datasets are available in: https://anonymous.4open.science/r/OmniFood-Bench-7D0B

cs.AI↗

arXiv2026

TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards

Faithful text rendering remains a persistent weakness of large text-to-image generative models, as it requires both semantic instruction following and fine-grained glyph-level structure. Prior methods often improve this ability through architecture-specific modules or encoder modifications, which complicate deployment across foundation models. We study text rendering as a post-training preference-alignment problem and propose TextAlign, a non-invasive framework that keeps the generator architecture unchanged. The key component is a hierarchical vision-language model (VLM)-based reward that decomposes rendering errors into global, word, and glyph levels, then converts binary defect judgments into a scalar preference signal. The resulting signal supports both Group Relative Policy Optimization (GRPO) and Direct Preference Optimization (DPO). Experiments on FLUX.1-dev and Z-Image-Turbo show consistent gains in OCR-based text accuracy without degrading general generation quality. Compared with strong foundation and text-rendering baselines, including SD3.5, Qwen-Image, AnyText, and TextDiffuser, these results indicate that reward design offers a scalable alternative to model redesign for improving text rendering.

cs.CV↗

arXiv2026

UniD-Shift: Towards Unified Semantic Segmentation via Interpretable Share-Private Multimodal Decomposition

Semantic segmentation of large-scale 3D point clouds is crucial for applications such as autonomous driving and urban digital twins. However, the sparse sampling pattern of LiDAR and the view-dependent geometric distortion in image observations complicate cross-modal alignment and hinder stable fusion. Inspired by the fact that 2D images captured by cameras are representations of the 3D world, we recognize that the features learned from 2D and 3D segmentation share some common semantics, while other aspects remain modality-specific. This insight motivates a unified multimodal framework for joint 2D-3D semantic segmentation. We combine a SAM-based vision encoder with a SPTNet-based geometric encoder to extract complementary semantic and geometric representations. The resulting features from both modalities are explicitly decomposed into shared and private subspaces, where the shared components summarize semantic factors common to both domains, and the private components preserve properties that are unique to each modality. A lightweight attention-based fusion module aggregates the shared features into a consistent cross-modal representation, and a regularized training objective ensures both semantic alignment and subspace independence. Experiments on the SemanticKITTI and nuScenes benchmarks demonstrate consistent improvements in segmentation accuracy over representative multimodal baselines, accompanied by competitive computational efficiency. Cross-domain evaluation on nuScenes USA-Singapore shows stable performance under distribution shifts, demonstrating strong generalization. The implementation code is publicly available at: https://github.com/shuaizhang69/UniD-Shift.

cs.CV↗

arXiv2025

Edge-ANN: Storage-Efficient Edge-Based Remote Sensing Feature Retrieval

Meeting real-time constraints for high-performance Approximate Nearest Neighbor (ANN) search remains a critical challenge in remote sensing edge devices, which are essentially fusion systems like micro-satellites and UAVs, largely due to stringent limitations in primary (RAM) and secondary (disk) storage. To address this challenge, we propose Edge-ANN, an innovative ANN framework specifically engineered for storage efficiency. The core innovation of Edge-ANN lies in its departure from traditional tree-based methods that store high-dimensional hyperplanes. Instead, it leverages pairs of existing data items, termed "anchors," to implicitly define spatial partitions. To ensure these partitions are both balanced and effective, we have developed a novel Binary Anchor Optimization algorithm.This architectural shift eliminates the dimension-dependence of the space complexity. Rigorous experiments on three multi-source datasets, MillionAID, High-resolution Urban Complex Dataset, and GlobalUrbanNet Dataset, demonstrate that under simulated edge environments with dual storage constraints, Edge-ANN achieves a 30-40% reduction in secondary storage compared to the baseline, at the cost of a minor 3-5% drop in retrieval accuracy. Furthermore, its overall retrieval performance surpasses that of other mainstream methods in these constrained scenarios. Collectively, these results establish Edge-ANN as a state-of-the-art solution for enabling large-scale, high-performance, real-time remote sensing feature retrieval on edge devices with exceptionally constrained storage. The codes of Edge-ANN are available at https://github.com/huaijiao666/Edge-ANN.

cs.CG↗

arXiv2024

Efficient Reinforcement Learning via Decoupling Exploration and Utilization

Reinforcement Learning (RL), recognized as an efficient learning approach, has achieved remarkable success across multiple fields and applications, including gaming, robotics, and autonomous vehicles. Classical single-agent reinforcement learning grapples with the imbalance of exploration and exploitation as well as limited generalization abilities. This methodology frequently leads to algorithms settling for suboptimal solutions that are tailored only to specific datasets. In this work, our aim is to train agent with efficient learning by decoupling exploration and utilization, so that agent can escaping the conundrum of suboptimal Solutions. In reinforcement learning, the previously imposed pessimistic punitive measures have deprived the model of its exploratory potential, resulting in diminished exploration capabilities. To address this, we have introduced an additional optimistic Actor to enhance the model's exploration ability, while employing a more constrained pessimistic Actor for performance evaluation. The above idea is implemented in the proposed OPARL (Optimistic and Pessimistic Actor Reinforcement Learning) algorithm. This unique amalgamation within the reinforcement learning paradigm fosters a more balanced and efficient approach. It facilitates the optimization of policies that concentrate on high-reward actions via pessimistic exploitation strategies while concurrently ensuring extensive state coverage through optimistic exploration. Empirical and theoretical investigations demonstrate that OPARL enhances agent capabilities in both utilization and exploration. In the most tasks of DMControl benchmark and Mujoco environment, OPARL performed better than state-of-the-art methods. Our code has released on https://github.com/yydsok/OPARL

cs.LG↗