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Works52 from public data
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- CoMatch: Semi-supervised Learning with Contrastive Graph Regularization374
CoMatch is a new semi-supervised learning method that unifies dominant approaches and addresses their limitations, and achieves substantial accuracy improvements on the label-scarce CIFAR-10 and STL-10.
- The Devil Is in Classification: A Simple Framework for Long-Tail Instance Segmentation219
This work systematically investigates performance drop of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the recent long-tail LVIS dataset, and unveils that a major cause is the inaccurate classification of object proposals.
- A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce146
This work revisits GRPO from a reinforce-like algorithm perspective and proposes Reinforce-Rej, a minimal extension of policy gradient that filters both entirely incorrect and entirely correct samples, serving as a lightweight yet effective alternative to more complex RL algorithms.
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- Reward-Guided Speculative Decoding for Efficient LLM Reasoning120
Reward-Guided Speculative Decoding is introduced, a novel framework aimed at improving the efficiency of inference in large language models (LLMs) by incorporating a controlled bias to prioritize high-reward outputs in contrast to existing speculative decoding methods that enforce strict unbiasedness.
- MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers107
MCP-Universe is introduced, the first comprehensive benchmark specifically designed to evaluate LLMs in realistic and hard tasks through interaction with real-world MCP servers, and implements execution-based evaluators, including format evaluators for agent format compliance, static evaluators for time-invariant content matching, and dynamic evaluators that automatically retrieve real-time ground truth for temporally sensitive tasks.
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- Attention Transfer from Web Images for Video Recognition48
This work proposes a novel approach to transfer knowledge from image domain to video domain, and designs a novel Siamese EnergyNet structure to learn energy functions on the attention maps by jointly optimizing two loss functions, such that the attention map corresponding to a ground truth concept would have higher energy.
- Deep Reinforcement Learning in Soft Viscoelastic Actuator of Dielectric Elastomer44
This letter proposes a model-free method for control of DEAs, based on deep reinforcement learning that is robust to achieve accurate control for the DEAs of different configurations, different prestretches, and at different times.
- Scalable Chain of Thoughts via Elastic Reasoning42
This work proposes Elastic Reasoning, a novel framework for scalable chain of thoughts that explicitly separates reasoning into two phases--thinking and solution--with independently allocated budgets, and demonstrates that it performs robustly under strict budget constraints, while incurring significantly lower training cost than baseline methods.
- Removal behavior and key drivers of antibiotic resistance genes in two full-scale leachate treatment plants42
This study provides new insights into the mechanisms of ARGs removal and dissemination behavior in industrial-scale LTPs and indicated that bacterial composition, MGEs and basic water properties were the key drivers shaping ARGs dynamics in the raw leachate, biological system and filtration system, respectively.
- Visual Social Relationship Recognition32
A dual-glance model for social relationship recognition, where the first glance fixates at the person of interest and the second glance deploys attention mechanism to exploit contextual cues, is proposed.
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- Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models21
This survey provides a comprehensive review of synthetic data for TSFMs and TSLLMs, analyzing data generation strategies, their role in model pretraining, fine-tuning, and evaluation, and identifying future research directions.
- Improving Tail-Class Representation with Centroid Contrastive Learning20
Interpolative centroid contrastive learning (ICCL) is proposed to improve long-tailed representation learning and shows a significant accuracy gain of 2.8% on the iNaturalist 2018 dataset with a real-world long-tails distribution.
- Weakly-Supervised Multi-Person Action Recognition in 360° Videos20
This work proposes a weakly-supervised method based on multiinstance multi-label learning, which trains the model to recognize and localize multiple actions in a video using only video-level action labels as supervision.
- VideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation19
VideoAutoArena is introduced, an arena-style benchmark inspired by LMSYS Chatbot Arena’s framework, designed to automatically assess LMMs’ video analysis abilities, and introduces a fault-driven evolution strategy, progressively increasing question complexity to push models toward handling more challenging video analysis scenarios.
- Masked Unsupervised Self-training for Label-free Image Classification18
This paper proposes Masked Unsupervised Self-Training (MUST), a new approach which leverages two different and complimentary sources of supervision: pseudo-labels and raw images, and achieves a zero-shot top-1 accuracy of 77.7% on ImageNet using ViT-B, +9.4% higher than CLIP.
- A review of fiber-optic corrosion sensor in civil engineering18
The mechanism of metal corrosion is introduced, and several common methods for detecting optical fiber corrosion sensors are presented, and the latest progress of optical Fiber corrosion sensors in recent years is described.
- ARVMEC: Adaptive Recommendation of Virtual Machines for IoT in Edge–Cloud Environment16
This article proposes ARVMEC, Adaptive Recommendation of Virtual Machines for IoT in Edge-Cloud Environment, which can always provide users with the best VM recommendation according to their own budget or deadline constraints.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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