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- Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach900
This work investigates a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems and proposes a new adversarial erasing approach for localizing and expanding object regions progressively.
- Perceptual Generative Adversarial Networks for Small Object Detection888
This work addresses the small object detection problem by developing a single architecture that internally lifts representations of small objects to super-resolved ones, achieving similar characteristics as large objects and thus more discriminative for detection.
- Is Faster R-CNN Doing Well for Pedestrian Detection?869
A very simple but effective baseline for pedestrian detection, using an RPN followed by boosted forests on shared, high-resolution convolutional feature maps, presenting competitive accuracy and good speed.
- Scale-aware Fast R-CNN for Pedestrian Detection843
This paper argues that the issue of large variance in instance scales, which results in undesirable large intracategory variance in features, may severely hurt the performance of modern object instance detection methods, can be substantially alleviated by the divide-and-conquer philosophy.
- Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning634
This work extends Faster /Mask R-CNN by proposing meta-learning over RoI (Region-of-Interest) features instead of a full image feature, and introduces a Predictor-head Remodeling Network (PRN) that shares its main backbone with Faster / Mask R- CNN.
- STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation617
A simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation, which demonstrates the superiority of the proposed STC framework compared with other state-of-the-arts frameworks.
- Voxel Transformer for 3D Object Detection574
Voxel Transformer is presented, a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds that shows consistent improvement over the convolutional baselines while maintaining computational efficiency on the KITTI dataset and the Waymo Open dataset.
- Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning571
This work constructs a new largescale benchmark, Geometry3K, consisting of 3,002 geometry problems with dense annotation in formal language, and proposes a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (InterGPS).
- Look into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing527
A new benchmark Look into Person (LIP) is introduced that makes a significant advance in terms of scalability, diversity and difficulty, and a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into parsing results without resorting to extra supervision.
- Toward Characteristic-Preserving Image-Based Virtual Try-On Network504
A new fully-learnable Characteristic-Preserving Virtual Try-On Network (CP-VTON) for addressing all real-world challenges in this task, and achieves the state-of-the-art virtual try-on performance both qualitatively and quantitatively.
- Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark385
A new benchmark named “Look into Person (LIP)” is introduced that provides a significant advancement in terms of scalability, diversity, and difficulty, which are crucial for future developments in human-centric analysis.
- Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI384
This survey gives a comprehensive exploration of the latest advancements in Embodied AI, exploring the complexities of MLMs in virtual and real embodied agents, highlighting their significance in facilitating interactions in digital and physical environments.
- Instance-Level Human Parsing via Part Grouping Network383
This work makes the first attempt to explore a detection-free Part Grouping Network (PGN) for efficiently parsing multiple people in an image in a single pass and outperforms all state-of-the-art methods on PASCAL-Person-Part dataset.
- Rethinking Knowledge Graph Propagation for Zero-Shot Learning380
This work proposes a Dense Graph Propagation module with carefully designed direct links among distant nodes to exploit the hierarchical graph structure of the knowledge graph through additional connections and outperforms state-of-the-art zero-shot learning approaches.
- Semantic Object Parsing with Graph LSTM379
The Graph Long Short-Term Memory network is proposed, which is the generalization of LSTM from sequential data or multi-dimensional data to general graph-structured data.
- Dual Motion GAN for Future-Flow Embedded Video Prediction377
A dual motion Generative Adversarial Net architecture is developed, which learns to explicitly enforce future-frame predictions to be consistent with the pixel-wise flows in the video through a duallearning mechanism.
- GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning351
This paper proposes a Geometric Question Answering dataset GeoQA, containing 4,998 geometric problems with corresponding annotated programs, which illustrate the solving process of the given problems, and introduces a Neural Geometric Solver (NGS) to address geometric problems by comprehensively parsing multimodal information and generating interpretable programs.
- Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation345
Experiments show that the proposed KERP approach generates structured and robust reports supported with accurate abnormality description and explainable attentive regions, achieving the state-of-the-art results on two medical report benchmarks, with the best medical abnormality and disease classification accuracy and improved human evaluation performance.
- Deep Human Parsing with Active Template Regression323
In this work, the human parsing task, namely decomposing a human image into semantic fashion/body regions, is formulated as an active template regression (ATR) problem, where the normalized mask is expressed as the linear combination of the learned mask templates.
- CIRL: Controllable Imitative Reinforcement Learning for Vision-Based Self-driving318
This work presents a general and principled Controllable Imitative Reinforcement Learning (CIRL) approach which successfully makes the driving agent achieve higher success rates based on only vision inputs in a high-fidelity car simulator, which performs better than supervised imitation learning.
- Vision-Language Navigation With Self-Supervised Auxiliary Reasoning Tasks299
AuxRN is introduced, a framework with four self-supervised auxiliary reasoning tasks to exploit the additional training signals derived from these semantic information that help the agent to acquire knowledge of semantic representations in order to reason about its activities and build a thorough perception of environments.
- Peak-Piloted Deep Network for Facial Expression Recognition299
This work presents a novel peak-piloted deep network (PPDN) that uses a sample with peak expression to supervise the intermediate feature responses for a sample of non-peak expression (hard sample) of the same type and from the same subject.
- DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection290
This paper presents DetCLIP, a paralleled visual-concept pre-training method for open-world detection by resorting to knowledge enrichment from a designed concept dictionary, and designs a concept dictionary from various online sources and detection datasets to provide prior knowledge for each concept.
- Adversarial Geometry-Aware Human Motion Prediction287
This work proposes a novel frame-wise geodesic loss as a geometrically meaningful, more precise distance measurement and presents a new learning procedure to simultaneously validate the sequence-level plausibility of the prediction and its coherence with the input sequence by introducing two global recurrent discriminators.
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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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