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Works435 from public data
- Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition5,594
A novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data.
- 4,387
- Unsupervised Feature Learning via Non-parametric Instance Discrimination4,044
This work forms this intuition as a non-parametric classification problem at the instance-level, and uses noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes.
- VBench: Comprehensive Benchmark Suite for Video Generative Models2,062
VBench is a comprehensive benchmark suite that dissects “video generation quality” into specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods, and investi-gate the gaps between video and image generation models.
- InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models1,936
InternVL3 incorporates variable visual position encoding (V2PE) to support extended multimodal contexts, employs advanced post-training techniques such as supervised fine-tuning (SFT) and mixed preference optimization (MPO), and adopts test-time scaling strategies alongside an optimized training infrastructure.
- Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling1,813
This work explores the relationship between model scaling and performance, systematically exploring the performance trends in vision encoders, language models, dataset sizes, and test-time configurations and hopes this model contributes to the open-source community by setting new standards for developing and applying multimodal AI systems.
- Libra R-CNN: Towards Balanced Learning for Object Detection1,762
Libra R-CNN is proposed, a simple but effective framework towards balanced learning for object detection that integrates three novel components: IoU-balanced sampling, balanced feature pyramid, and balanced L1 loss, respectively for reducing the imbalance at sample, feature, and objective level.
- AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning1,729
The proposed AnimateDiff is a practical framework for animating personalized T2I models without requiring model-specific tuning and MotionLoRA is a lightweight fine-tuning technique for AnimateDiff that enables a pre-trained motion module to adapt to new motion patterns, such as different shot types, at a low training and data collection cost.
- Hybrid Task Cascade for Instance Segmentation1,565
This work proposes a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background.
- 1,456
- PSANet: Point-wise Spatial Attention Network for Scene Parsing1,191
The point-wise spatial attention network (PSANet) is proposed to relax the local neighborhood constraint and achieves top performance on various competitive scene parsing datasets, including ADE20K, PASCAL VOC 2012 and Cityscapes, demonstrating its effectiveness and generality.
- Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering1,011
This work introduces Scaffold-GS, which uses an-chor points to distribute local 3D Gaussians, and predicts their attributes on-the-fly based on viewing direction and distance within the view frustum, and effectively reduces redun-dant Gaussians while delivering high-quality rendering.
- CARAFE: Content-Aware ReAssembly of FEatures1,010
Comprehensive evaluations on standard benchmarks in object detection, instance/semantic segmentation and inpainting show CARAFE shows consistent and substantial gains across all the tasks, and has great potential to serve as a strong building block for future research.
- Temporal Action Detection with Structured Segment Networks992
This paper addresses an important and challenging task, namely detecting the temporal intervals of actions in untrimmed videos by presenting a framework called structured segment network (SSN), built on temporal proposals of actions.
- Temporal Segment Networks for Action Recognition in Videos989
The proposed TSN framework, called temporal segment network (TSN), aims to model long-range temporal structure with a new segment-based sampling and aggregation scheme and won the video classification track at the ActivityNet challenge 2016 among 24 teams.
- FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection854
The solution achieves 1st place out of all the vision-only methods in the nuScenes 3D detection challenge of NeurIPS 2020 and proposes a general framework FCOS3D, getting rid of any 2D detection or 2D-3D correspondence priors.
- Revisiting Skeleton-based Action Recognition831
PoseConv3D is more effective in learning spatiotemporal features, more robust against pose estimation noises, and generalizes better in cross-dataset settings, and achieves the state-of-the-art on all eight multi-modality action recognition benchmarks.
- Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation756
A new framework for the outdoor LiDAR segmentation is proposed, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern while maintaining these inherent properties.
- Region Proposal by Guided Anchoring685
This paper presents an alternative scheme, named Guided Anchoring, which leverages semantic features to guide the anchoring, and jointly predicts the locations where the center of objects of interest are likely to exist as well as the scales and aspect ratios at different locations.
- 668
- OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation628
OPERA is presented, a novel MLLM decoding method grounded in an Over-trust Penalty and a Retrospection-Allocation strategy, serving as a nearly free lunch to alleviate the hallucination issue without additional data, knowledge, or training.
- VLMEvalKit: An Open-Source ToolKit for Evaluating Large Multi-Modality Models628
The VLMEvalKit toolkit aims to provide a user-friendly and comprehensive framework for researchers and developers to evaluate existing multi-modality models and publish reproducible evaluation results.
- Visual-RFT: Visual Reinforcement Fine-Tuning575
This work introduces Visual Reinforcement Fine-Tuning (Visual-RFT), which further extends the application areas of RFT on visual tasks and represents a paradigm shift in fine-tuning LVLMs, offering a data-efficient, rewarddriven approach that enhances reasoning and adaptability for domain-specific tasks.
- UntrimmedNets for Weakly Supervised Action Recognition and Detection546
This paper presents a new weakly supervised architecture, called UntrimmedNet, which is able to directly learn action recognition models from untrimmed videos without the requirement of temporal annotations of action instances.
- Detecting Visual Relationships with Deep Relational Networks529
The proposed Deep Relational Network is a novel formulation designed specifically for exploiting the statistical dependencies between objects and their relationships and achieves substantial improvement over state-of-the-art on two large data sets.
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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