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- AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-Identification317
A novel augmented discriminative clustering (AD-Cluster) technique that estimates and augments person clusters in target domains and enforces the discrimination ability of re-ID models with the augmented clusters.
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- Memory Attention Networks for Skeleton-Based Action Recognition204
A newly temporal-then-spatial recalibration method named memory attention networks (MANs) is proposed and deploy MANs using the temporal attention recalibrration module (TARM) and spatiotemporal convolution module (STCM) to handle skeleton-based action recognition.
- Location Discriminative Vocabulary Coding for Mobile Landmark Search191
A Location Discriminative Vocabulary Coding (LDVC) scheme, which achieves extremely low bit rate query transmission, discriminative landmark description, as well as scalable descriptor delivery in a unified framework is presented.
- CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning173
It is argued that learning by comparing helps identify these hard snip-pets and a Snippet Contrast (SniCo) Loss is proposed to refine the hard snippet representation in feature space, which guides the network to perceive precise temporal boundaries and avoid the temporal interval interruption.
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- VVC In-Loop Filters126
An overview of the technologies for in-loop processing and filtering in the Versatile Video Coding (VVC) standard, which comprise luma mapping with chroma scaling, deblocking filter, sample adaptive offset, adaptive loop filter and cross-component adaptive loopfilter.
- DiffusionRet: Generative Text-Video Retrieval with Diffusion Model113
This work creatively tackles the text-video retrieval task from a generative viewpoint and model the correlation between the text and the video as their joint probability p(candidates,query), which cleverly leverages the strengths of both generative and discriminative methods.
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- Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning99
This paper creatively model video-text as game players with multivariate cooperative game theory to wisely handle the uncertainty during fine-grained semantic interaction with diverse granularity, flexible combination, and vague intensity in order to achieve cooperative games at different semantic levels.
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- ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval86
A novel fusion token based transformer aggregation approach to exchange the necessary scene text information only through the fusion token and concentrate on the most important features in each modality to tackle the modality missing problem of scene text.
- Patterning the Asteraceae Capitulum: Duplications and Differential Expression of the Flower Symmetry CYC2-Like Genes85
The evolutionary history of CYC2-like genes in the Asterales was reconstructed and their expression patterns were examined in species representing different capitulum types and several major Asteraceae lineages, showing that the developmental program of making a ray flower is conserved involving functionally divergent CYC 2- like genes.
- Training-free Transformer Architecture Search68
Experimental results demonstrate that the TF- TAS achieves a competitive performance against the state-of-the-art manually or automatically design ViT architectures, and it promotes the searching efficiency in ViT search space greatly.
- Towards low bit rate mobile visual search with multiple-channel coding55
Quantitative comparisons to the state-of-the-arts demonstrate the significant advantages in descriptor compactness and retrieval mAP in mobile landmark, product, and CD/book cover search.
- BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting52
A large-scale Breast tumor Cell Dataset (BCData) for cell detection and counting is introduced, which contains 1,338 images with 181,074 annotated cells belonging to two categories, i.e., positive and negative tumor cells.
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- Invertible Diffusion Models for Compressed Sensing40
Invertible Diffusion Models (IDM) is proposed, a novel efficient, end-to-end diffusion-based CS method that repurposes a large-scale diffusion sampling process as a reconstruction model, and fine-tunes it end-to-end to recover original images directly from CS measurements, moving beyond the traditional paradigm of one-step noise estimation learning.
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- Weakly-Supervised 3D Spatial Reasoning for Text-Based Visual Question Answering37
3D geometric information is introduced into the spatial reasoning process to capture the contextual knowledge of key objects step-by-step and achieves state-of-the-art performance on TextVQA and ST-VQ a datasets.
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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 2026-10-13. 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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