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- Transformer-Based Visual Segmentation: A Survey342
This survey provides a thorough overview of transformer-based visual segmentation, summarizing recent advancements and presents a meta-architecture that unifies all recent transformer-based approaches.
- Towards Open Vocabulary Learning: A Survey285
This paper thoroughly reviews open vocabulary learning, summarizing and analyzing recent developments in the field, and juxtaposing open vocabulary learning with analogous concepts such as zero-shot learning, open-set recognition, and out-of-distribution detection.
- Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning191
A neural collapse inspired framework for FSCIL that holds the neural collapse optimality and does not break the feature-classifier alignment in an incremental fashion and experiments demonstrate that the proposed framework outperforms the state-of-the-art performances.
- Sa2VA: Marrying SAM2 With MLLM for Dense Grounded Understanding of Images and Videos168
Experiments show that Sa2VA achieves strong performance across multiple tasks, particularly in referring video object segmentation, highlighting its potential for complex real-world applications.
- Open-Vocabulary SAM: Segment and Recognize Twenty-Thousand Classes Interactively114
The Open-Vocabulary SAM is introduced, a SAM-inspired model designed for simultaneous interactive segmentation and recognition, leveraging two unique knowledge transfer modules: SAM2CLIP and CLIP2SAM.
- Multi-Task Learning With Multi-Query Transformer for Dense Prediction94
This work proposes a simple pipeline named Multi-Query Transformer (MQTransformer) that is equipped with multiple queries from different tasks to facilitate the reasoning among multiple tasks and simplify the cross-task interaction pipeline.
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- PolyphonicFormer: Unified Query Learning for Depth-Aware Video Panoptic Segmentation51
PolyphonicFormer, a vision transformer to unify these sub-tasks under the DVPS task and lead to more robust results, achieves state-of-the-art results on two DVPS datasets, and ranks 1st on the ICCV-2021 BMTT Challenge video + depth track.
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- Tube-Link: A Flexible Cross Tube Framework for Universal Video Segmentation37
Tube-Link is a near-online approach that takes a short subclip as input and outputs the corresponding spatial-temporal tube masks, and introduces temporal contrastive learning to instance-wise discriminative features for tube-level association.
- An Empirical Study of GPT-4o Image Generation Capabilities31
An empirical study of GPT-4o's image generation capabilities is conducted, benchmarking it against leading open-source and commercial models and identifying promising directions for future unified generative models, emphasizing the role of architectural design and data scaling.
- On Path to Multimodal Generalist: General-Level and General-Bench29
This project introduces General-Level, an evaluation framework that defines 5-scale levels of MLLM performance and generality, offering a methodology to compare MLLMs and gauge the progress of existing systems towards more robust multimodal generalists and, ultimately, towards AGI.
- Panoptic-PartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation29
The first end-to-end unified framework, Panoptic-PartFormer is designed, designing a meta-architecture that decouples part features and things/stuff features, respectively and proposes a new metric Part-Whole Quality (PWQ), better to measure this task from pixel-region and part-whole perspectives.
- Mamba or RWKV: Exploring High-Quality and High-Efficiency Segment Anything Model25
This work designs a mixed backbone that contains convolution and RWKV operation, which achieves the best for both accuracy and efficiency and designs an efficient decoder to utilize the multiscale tokens to obtain high-quality masks.
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- Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants18
A unified solution to the misalignment dilemma in the three tasks of CIL, LTCIL and FSCIL is offered andoretical analysis indicates that the method holds the neural collapse optimality in an incremental fashion regardless of data imbalance or data scarcity.
- Towards Theoretically Inspired Neural Initialization Optimization15
This paper proposes a differentiable quantity, named GradCosine, with theoretical insights to evaluate the initial state of a neural network, and proposes the neural initialization optimization (NIO) algorithm, able to automatically look for a better initialization with negligible cost compared with the training time.
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- RMP-SAM: Towards Real-Time Multi-Purpose Segment Anything12
This work explores a new real-time segmentation setting, named all-purpose segmentation in real-time, to transfer VFMs in real-time deployment and presents Real-Time All Purpose SAM (RAP-SAM), which contains an efficient encoder and an efficient decoupled decoder to perform prompt-driven decoding.
- Visual Reasoning Tracer: Object-Level Grounded Reasoning Benchmark10
The Visual Reasoning Tracer task is introduced, which requires models to not only localize the target object but also explicitly predict the intermediate objects that form the reasoning path, and reveals that while existing models often produce the correct final output, they struggle to ground their intermediate reasoning.
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- The 1st Solution for 7th LSVOS RVOS Track: SaSaSa2VA7
This paper proposes Segmentation Augmented and Selective Averaged Sa2VA (SaSaSa2VA) to address two key bottlenecks that limit segmentation performance: sparse frame sampling and reliance on a single [SEG] token for an entire video.
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- Monocular Road Planar Parallax Estimation6
This paper proposes Road Planar Parallax Attention Network (RPANet), a new deep neural network for 3D sensing from monocular image sequences based on planar parallax, which takes full advantage of the omnipresent road plane geometry in driving scenes.
Publication data from OpenAlex; 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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