Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks87 from public data
- Autoregressive Queries for Adaptive Tracking with Spatio-Temporal Transformers230
This work proposes an adaptive tracker with spatio-temporal transformers (named AQA-Track), which adopts simple autoregressive queries to effectively learn spatio-temporal information without many hand-designed components, and designs a novel attention mechanism for the interaction of existing queries to generate a new query in current frame.
- Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation149
The Rotated Multi-Scale In-teraction Network (RMSIN) is introduced, an innovative approach de-signed for the unique demands of RRSIS, surpassing existing state-of-the-art models by a signif-icant margin.
- Aligning and Prompting Everything All at Once for Universal Visual Perception100
APE advances the convergence of detection and grounding by reformulating language-guided grounding as open-vocabulary detection, which efficiently scales up model prompting to thousands of category vocab-ularies and region descriptions while maintaining the ef-fectiveness of cross-modality fusion.
- Towards General Visual-Linguistic Face Forgery Detection67
This work proposes Face Forgery Text Generator (FFTG), a novel annotation pipeline that generates accurate text descriptions by leveraging forgery masks for initial region and type identification, followed by a comprehensive prompting strategy to guide MLLMs in reducing hallucination.
- 63
- 63
- 58
- Exploring Decoupled Spatio-Temporal Consistency Learning and Self-Prompting Evolution for Self-Supervised Tracking51
This work designs an effective weak-to-strong self-supervised training framework that aims to narrow the feature distribution gap between labeled and unlabeled frames, and enables SSTrack++ to not only learn a generalizable tracking representation in a low-annotation self-supervised manner, but also makes it possible to simulate realistic appearance and motion variations of target instances in real-world scenarios.
- Towards Language-Guided Visual Recognition via Dynamic Convolutions34
The experimental results not only show the competitive or better performance of LaConvNet against existing multi-modal networks, but also witness the merits of LaConvNet as an unified structure, including compact network, low computational cost and high generalization ability.
- Flow caching for autoregressive video generation26
FlowCache is presented, the first caching framework specifically designed for autoregressive video generation, which introduces a chunkwise caching strategy that dynamically adapts to the unique denoising characteristics of each chunk, complemented by a joint importance-redundancy optimized KV cache compression mechanism that maintains fixed memory bounds while preserving generation quality.
- SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial Intelligence23
It is found that even the most advanced MLLM still lags behind humans by large margins on SpaCE-10 and a novel hierarchical annotation pipeline is proposed to generate high-quality and diverse question-answer (QA) pairs.
- 23
- UniVST: A Unified Framework for Training-Free Localized Video Style Transfer21
The proposed UniVST adeptly addresses the challenges of preserving the primary object’s style while ensuring temporal consistency and detail preservation, and has been validated to be superior to existing methods in quantitative and qualitative metrics.
- 21
- GraCo: Granularity-Controllable Interactive Segmentation19
An any-granularity mask generator is designed that exploits the semantic property of the pre-trained IS model to automatically gen-erate abundant mask-granularity pairs without requiring additional manual annotation and a granularity-controllable learning strategy that efficiently imparts the granularity controllability to the IS model is proposed.
- Semi-Supervised Counting via Pixel-by-Pixel Density Distribution Modeling16
This paper designs a pixel-wise distribution matching loss to measure the differences in the pixel-wise density distributions between the prediction and the ground-truth, and designs the interleaving consistency self-supervised learning mechanism to learn from unlabeled data efficiently.
- 15
- SocialOmni: Benchmarking Audio-Visual Social Interactivity in Omni Models12
SocialOmni is introduced, an offline diagnostic benchmark that separates three turn-level decisions: identifying who is speaking, deciding when a designated participant should enter at an annotated query time, and determining how that participant should continue the dialogue.
- OBIMD: A Multi-modal Dataset for Contextual Interpretation of Oracle Bone Inscriptions11
The Oracle Bone Inscriptions Multi-modal Dataset (OBIMD), a large-scale, publicly available corpus to provide pixel-aligned rubbing and facsimile images, character-level annotations, and sentence-level transcriptions with corresponding reading sequences, is presented.
- NICE: Improving Panoptic Narrative Detection and Segmentation With Cascading Collaborative Learning11
A unified and effective framework called NICE that can jointly learn these two panoptic narrative recognition tasks, linking PNS and PND in series with the barycenter of segmentation as the anchor, naturally aligns the two tasks and allows them to complement each other for improved performance.
- 9
- Few-Shot Image Quality Assessment via Adaptation of Vision-Language Models8
The Gradient-Regulated MetaPrompt IQA Framework (GRMP-IQA), designed to efficiently adapt the visual-language pre-trained model, CLIP, to IQA tasks, achieving high accuracy even with limited data, is proposed.
- Event-Anchored Frame Selection for Effective Long-Video Understanding7
Event-Anchored Frame Selection (EFS), a hierarchical, event-aware pipeline that partitions the video stream into visually homogeneous temporal segments, which serve as proxies for semantic events and ensures the final keyframe set jointly optimizes for event coverage, query relevance, and visual diversity.
- Knowing Where to Focus: Attention-Guided Alignment for Text-based Person Search7
An Attention-Guided Alignment (AGA) framework featuring two innovative components: Attention-Guided Mask (AGM) Modeling and Text Enrichment Module (TEM), which dynamically masks semantically meaningful words by aggregating the attention weight derived from the text encoding process.
- FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification7
FlexiReID is proposed, a flexible framework that supports seven retrieval modes across four modalities: rgb, infrared, sketches, and text, and introduces an adaptive mixture-of-experts (MoE) mechanism to dynamically integrate diverse modality features and a cross-modal query fusion module to enhance multimodal feature extraction.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.