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- Rethinking BiSeNet For Real-time Semantic Segmentation784
A novel and efficient structure named Short-Term Dense Concatenate network (STDC network) is proposed by removing structure redundancy by gradually reducing the dimension of feature maps and use the aggregation of them for image representation, which forms the basic module of STDC network.
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- Large Scale Visual Food Recognition255
This paper introduces Food2K, which is the largest food recognition dataset with 2,000 categories and over 1 million images, and proposes a deep progressive region enhancement network for food recognition, which mainly consists of two components, namely progressive local feature learning and region feature enhancement.
- Accurate realtime full-body motion capture using a single depth camera246
The idea is to formulate the registration problem in a Maximum A Posteriori (MAP) framework and iteratively register a 3D articulated human body model with monocular depth cues via linear system solvers.
- PromptDet: Towards Open-Vocabulary Detection Using Uncurated Images245
The goal of this work is to establish a scalable pipeline for expanding an object detector towards novel/unseen categories, using zero manual annotations, and the proposed detector, termed as PromptDet, shows superior performance over existing approaches with fewer additional training images andzero manual annotations whatsoever.
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- ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network162
This work introduces the dataset ISIA Food-500 with 500 categories from the list in the Wikipedia and 399,726 images, a more comprehensive food dataset that surpasses existing popular benchmark datasets by category coverage and data volume, and proposes a stacked global-local attention network, which consists of two sub-networks for food recognition.
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- Towards Accurate Post-Training Quantization for Vision Transformer136
This work proposes a novel Accurate Post-training Quantization framework for Vision Transformer, namely APQ-ViT, which surpasses the existing post-training quantization methods by convincing margins, especially in lower bit-width settings.
- MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices129
This work presents MobileVLM, a competent multimodal vision language model (MMVLM), an amalgamation of a myriad of architectural designs and techniques that are mobile-oriented, which comprises a set of language models at the scale of 1.4B and 2.7B trained from scratch and demonstrates on par performance compared with a few much larger models.
- VideoMocap123
The power and effectiveness of the video-based motion modeling process in an image-based keyframe animation framework is demonstrated by generating a wide variety of physically realistic human actions from uncalibrated monocular video sequences such as sports video footage.
- Realtime human motion control with a small number of inertial sensors110
This paper introduces an approach to performance animation that employs a small number of motion sensors to create an easy-to-use system for an interactive control of a full-body human character.
- Structure Guided Lane Detection100
A top-down vanishing point guided anchoring mechanism is proposed to produce intensive anchors, which efficiently capture various lanes, and multi-level structural constraints are used to improve the perception of lanes.
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- Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning81
This work proposes a novel cross-modal matching strategy to detect OOD samples that substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
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- Physically valid statistical models for human motion generation67
This article shows how statistical motion priors can be combined seamlessly with physical constraints for human motion modeling and generation and effectively utilize the new model to generate a wide range of natural-looking motions that achieve the goals specified by users.
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- Ingredient-Guided Region Discovery and Relationship Modeling for Food Category-Ingredient Prediction41
- Weakly Supervised Semantic Segmentation Via Progressive Patch Learning37
A novel “Progressive Patch Learning” approach is proposed to improve the local details extraction of the classification, producing the CAM better covering the whole object rather than only the most discriminative regions as in CAMs obtained in conventional classification models.
- Two-stage Visual Cues Enhancement Network for Referring Image Segmentation31
Through the two-stage enhancement, the proposed TV-Net enjoys better performances in learning fine-grained matching behaviors between the natural language expression and image, especially when the visual information of the referent is inadequate, thus produces better segmentation results.
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 2026-10-14. 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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