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- Tieniu TanSuggested from co-authorship
- Yunhong WangSuggested from co-authorship
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Works328 from public data
- Scale-Aware Trident Networks for Object Detection1,089
A novel Trident Network (TridentNet) aiming to generate scale-specific feature maps with a uniform representational power is proposed and a parallel multi-branch architecture in which each branch shares the same transformation parameters but with different receptive fields is constructed.
- 448
- Driving Into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving426
The Drive-Wm is proposed, the first driving world model compatible with existing end-to-end planning models, that enables driving into multiple futures based on distinct driving maneuvers, and determines the optimal trajectory according to the image-based rewards.
- Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression398
It is empirically show that the proposed direct regression method outperforms keypoint detection and grouping methods and achieves superior bottom-up pose estimation results on two benchmark datasets, COCO and CrowdPose.
- The Devil Is in the Details: Window-based Attention for Image Compression369
This paper finds that combining the local-aware attention mechanism with the global-related feature learning could meet the expectation in image compression, and proposes a novel Symmetrical TransFormer (STF) framework with absolute transformer blocks in the down-sampling encoder and up-sampled decoder.
- Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition337
Benefiting from the strong representational learning capacity in the two-dimensional space, HCNN is efficient in emotion recognition especially on Beta and Gamma waves.
- GIFT: A Real-Time and Scalable 3D Shape Search Engine298
The proposed 3D shape search engine, which combines GPU acceleration and Inverted File (Twice), is named as GIFT, which outperforms the state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks and competitions.
- 285
- RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection277
This paper proposes an anchor-free single-stage LiDAR-based 3D object detector – RangeDet, and designs three components to address two issues overlooked by previous works: the scale variation between nearby and far away objects and the inconsistency between the 2D range image coordinates used in feature extraction and the 3D Cartesian coordinate used in output.
- Sequence Level Semantics Aggregation for Video Object Detection271
This work argues that aggregating features in the full-sequence level will lead to more discriminative and robust features for video object detection, and devise a novel Sequence Level Semantics Aggregate (SELSA) module to achieve this goal.
- SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines266
This work presents SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines and management of a large-scale annotation process, involving over 80 expert annotators and an interactive Human-LLM collaborative system, offering valuable methodological guidance for future research initiatives of comparable scope.
- RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models265
RoleLLM is introduced, a framework to benchmark, elicit, and enhance role-playing abilities in LLMs, and creates RoleBench, the first systematic and fine-grained character-level benchmark dataset forrole-playing with 168,093 samples.
- DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer238
This work introduces a new type of knowledge---cross sample similarities for model compression and acceleration that can be naturally derived from deep metric learning model and brings the "learning to rank" technique intoDeep metric learning formulation.
- CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation237
This paper proposes an end-to-end cross- image affinity module, which exploits pixel-level cross-image relationships with only image-level labels and achieves a new state-of-the-art result by only using image- level labels for weakly supervised semantic segmentation.
- Learning Integral Objects With Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation219
- Sparse Instance Activation for Real-Time Instance Segmentation210
Owing to the simple yet effective designs with instance activation maps, SparseInst has extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO benchmark, which significantly out-performs the counterparts in terms of speed and accuracy.
- Sharpness-Aware Gradient Matching for Domain Generalization185
This paper presents two conditions to ensure that the model could converge to a flat minimum with a small loss, and presents an algorithm, named Sharpness-Aware Gradient Matching (SAGM), to meet the two conditions for improving model generalization capability.
- Spectral Feature Transformation for Person Re-Identification158
Inspired by spectral clustering, a novel module termed Spectral Feature Transformation is developed to facilitate the optimization of group-wise similarities and adds no burden to the inference and can be applied to various scenarios.
- Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization154
A flexible Attribute Localization Module (ALM) is proposed to adaptively discover the most discriminative regions and learns the regional features for each attribute at multiple levels and a feature pyramid architecture is introduced to enhance the attribute-specific localization at low-levels with high-level semantic guidance.
- RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features148
A new method called RefineMask is proposed for high-quality instance segmentation of objects and scenes, which incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner and succeeds in segmenting hard cases such as bent parts of objects that are oversmoothed by most previous methods.
- Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation147
This work designs a graph neural network (GNN) for group-wise semantic segmentation (WSSS), wherein input images are represented as graph nodes, and the underlying relations between a pair of images are char- acterized by an efficient co-attention mechanism.
- Hard-Aware Point-to-Set Deep Metric for Person Re-identification146
In addition to person re-ID, the proposed Hard-Aware Point-to-Set (HAP2S) loss with a soft hard-mining scheme applies to generic deep metric learning benchmarks including CUB-200-2011 and Cars196, and achieves state-of-the-art results.
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- DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving144
DriveVLA-W0 is proposed, a training paradigm that employs world modeling to predict future images and amplifies the data scaling law, showing that performance gains accelerate as the training dataset size increases.
- Implicit Sample Extension for Unsupervised Person Re-Identification144
An Implicit Sample Extension method to generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy, which reduces the “sub and mixed” clustering errors, thus improving the Re-ID performance.
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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