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- Towards Efficient and Scalable Sharpness-Aware Minimization183
A novel algorithm LookSAM is proposed - that only periodically calculates the inner gradient ascent, to significantly reduce the additional training cost of SAM and enjoys comparable computational complexity with first-order optimizers such as SGD or Adam.
- E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context145
This paper proposes E-NeRV, which dramatically expedites NeRV by decomposing the image-wise implicit neural representation into separate spatial and temporal context, and greatly reduces the redundant model parameters, while retaining the representation ability.
- Go Wider Instead of Deeper112
This paper designs WideNet, a parameter-efficient framework that scales along model width by replacing feed-forward network with mixture-of-experts (MoE), and proposes to use individual layernorms to transform various semantic representations in a more parameter- efficient way.
- Designing One Unified Framework for High-Fidelity Face Reenactment and Swapping74
This paper proposes an effective end-to-end unified framework to achieve both face reenactment and swapping, and sufficiently transfers identity and attribute based on learned disentangled representations to generate high-fidelity faces.
- Adaptive Assignment for Geometry Aware Local Feature Matching68
AdaMatcher is in-troduce, which first accomplishes the feature correlation and co-visible area estimation through an elaborate feature interaction module, then performs adaptive assignment on patch-level matching while es-timating the scales between images, and finally refines the co- visible matches through scale alignment and sub-pixel regression module.
- UltraVideo: High-Quality UHD Video Dataset with Comprehensive Captions59
This paper proposes a high-quality open-sourced UHD-4K text-to-video dataset named UltraVideo, and expands Wan to UltraWan-1K/-4K, which can natively generate high-quality 1K/4K videos with more consistent text controllability, demonstrating the effectiveness of the data curation.
- Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition58
This paper proposes SloshNet, a new framework that revisits the spatial and temporal modeling for few-shot action recognition in a finer manner, and introduces a long-term temporal modeling module to model the global temporal relations based on the extracted spatial appearance features.
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- Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment47
Experimental results on three MPG datasets demonstrate that the proposed MedRPG outperforms state-of-the-art visual grounding approaches by a large margin, and the proposed TaCo strategy is effective in enhancing finding localization ability and reducing spurious region-phrase correlations.
- Global Spectral Filter Memory Network for Video Object Segmentation44
This paper proposes Global Spectral Filter Memory network (GSFM), which improves intra-frame interaction through learning long-term spatial dependencies in the spectral domain and proposes Low (High) Frequency Module, which is proposed to fit this circumstance.
- Guide Local Feature Matching by Overlap Estimation41
A novel Overlap Estimation method conditioned on image pairs with TRansformer, named OETR, to constrain local feature matching in the commonly visible region, and can boost state of the art local feature Matching performance substantially.
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- RIFormer: Keep Your Vision Backbone Effective But Removing Token Mixer33
This paper develops an RepIdentityFormer base on the re-parameterizing idea, and explores the improved learning paradigm to break the limitation of simple token mixer free backbone, and summarizes the empirical practice into 5 guidelines.
- TIMotion: Temporal and Interactive Framework for Efficient Human-Human Motion Generation31
This paper introduces TIMotion (Temporal and Interactive Modeling), an efficient and effective framework for human-human motion generation and proposes Causal Interactive Injection to model two separate sequences as a causal sequence leveraging the temporal and causal properties.
- Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent27
TAIRA is designed as an LLM-powered multi-agent system featuring a manager agent that orchestrates recommendation tasks by decomposing user needs and planning subtasks, with its planning capacity strengthened through Thought Pattern Distillation (TPD), a thought-augmentation method that extracts high-level thoughts from the agent's and human experts' experiences.
- Computer vision-based gesture recognition for an augmented reality interface27
This paper presents a computer vision-based gesture interface that is part of an Augmented Reality system that can recognise a 3D pointing gesture, a click gesture, and five static gestures.
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- M2-CLIP: A Multimodal, Multi-task Adapting Framework for Video Action Recognition17
This work designs a multi-task decoder with a rich set of supervisory signals, including the original contrastive learning head, a cross-modal classification head, a cross-modal masked language modeling head, and a visual classification head that adeptly satisfies the need for strong supervised performance within a multimodal framework.
- Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator17
This work derives a novel paradigm free of unstable seesaw-style optimization, resulting in simple, stable, and effective training and inference schemes, and adopts the Diffusion Models.
- Multiple Object Tracking of Drone Videos by a Temporal-Association Network with Separated-Tasks Structure16
By combing the temporal-association structure and separating different function heads, the model’s performance in object detection and tracking tasks is improved and has better tracking performance than the models such as SORT and HDHNet on the UAV video dataset.
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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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