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- Class-Aware Contrastive Semi-Supervised Learning140
This work proposes a general method named Class-aware Contrastive Semi-Supervised Learning (CCSSL), which is a drop-in helper to improve the pseudo-label quality and enhance the model's robustness in the real-world setting and successfully emphasizes clean label learning and simultaneously reduce noisy label learning.
- Towards Efficient Visual Adaption via Structural Re-parameterization124
It is proved that common adaptation modules can also be seamlessly integrated into most giant vision models via the authors' structural re-parameterization, thereby achieving zero-cost during inference.
- DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation99
The domain-invariant representation is constructed which suppresses the effect of the domain-specific style on the quality and correlation of the features and can enhance the robustness of various backbone networks with little computational cost.
- Image enhancement using the averaging histogram equalization (AVHEQ) approach for contrast improvement and brightness preservation91
A pipelined approach including color channel stretching, histogram averaging and re-mapping is developed, which shows that image brightness is preserved while image contrast is enhanced.
- Histogram equalization and optimal profile compression based approach for colour image enhancement89
Quantitative and qualitative results obtained from experiments on a wide variety of natural scene images demonstrate the effectiveness of the proposed approach over other methods at reducing artefact while increasing image contrast and colourfulness.
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- Comprehensive Regularization in a Bi-directional Predictive Network for Video Anomaly Detection78
A novel bi-directional architecture with three consistency constraints to comprehensively regularize the prediction task from pixel-wise, cross-modal, and temporal-sequence levels to outperforms advanced anomaly detectors and achieves state-of-the-art results.
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- Image contrast enhancement using histogram equalization with maximum intensity coverage74
Results showed that the proposed colour image enhancement technique introduced in this work is able to recover the largest amount of information as compared to other current approaches, and provides satisfactory performances in terms of image contrast, and sharpness.
- OMPQ: Orthogonal Mixed Precision Quantization72
This work proposes to optimize a proxy metric of network orthogonality that can be efficiently solved with linear programming, which proves to be highly correlated with quantized model accuracy and bit-width and achieves high accuracy without compromise on quantization accuracy.
- Intensity and edge based adaptive unsharp masking filter for color image enhancement70
An adaptive gain adjustment method is proposed here aiming at minimizing the number of over-range pixels while maximizing the image sharpness and information content and results have demonstrated that the proposed method outperforms the others with regard to colorfulness, information content, and sharpness.
- Rethinking Reverse Distillation for Multi-Modal Anomaly Detection68
A novel Multi-Modal Reverse Distillation paradigm that consists of a frozen multi-modal teacher encoder to generate distillation targets and a learnable student decoder targeting to restore multi-modal representations from the teacher is presented.
- LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization65
A novel framework built upon the transformer, termed LCTR (Local Continuity TRansformer), which targets at enhancing the local perception capability of global features among long-range feature dependencies and a relational patch-attention module (RPAM), which considers cross-patch information on a global basis.
- Image contrast enhancement with brightness preservation using an optimal gamma correction and weighted sum approach60
The non-linear gamma correction method is adopted to enhance the contrast, while a weighted sum approach is employed for brightness preservation, and results have shown that the proposed method outperforms currently available methods in contrast to enhancement and brightness preservation.
- X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance51
X-Mesh is presented, an innovative text-driven 3D stylization framework that incorporates a novel Text-guided Dynamic Attention Module (TDAM), which dynamically integrates the guidance of the target text by utilizing text-relevant spatial and channel-wise attentions during vertex feature extraction, resulting in more accurate attribute prediction and faster convergence speed.
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- Beat: Bi-directional One-to-Many Embedding Alignment for Text-based Person Retrieval42
A novel bi-directional one-to-many Embedding Alignment (Beat) model is proposed that achieves state-of-the-art performance on three popular TPR datasets and has the potential to be applied to other image-text retrieval tasks.
- Pseudo-label Alignment for Semi-supervised Instance Segmentation40
In PAIS, a dynamic aligning loss (DALoss) that adjusts the weights of semi-supervised loss terms with varying class and mask score pairs is devised that adjusts the weights of semi-supervised loss terms with varying class and mask score pairs.
- InterFormer Real-time Interactive Image Segmentation40
The effectiveness of Inter-Former is demonstrated, which outperforms previous interactive segmentation models in terms of computational efficiency and segmentation quality, achieve real-time high-quality interactive segmentation on CPU-only devices.
- Color image enhancement with brightness preservation using a histogram specification approach32
An approach is proposed to specify a proper histogram profile such that image intensity values are adjusted accordingly and the output brightness is maintained close to the input image.
- Adaptive Selection based Referring Image Segmentation25
An innovative framework for RIS that seeks to overcome challenges with adaptive alignment of vision and language features, termed the Adaptive Selection with Dual Alignment (ASDA), which exceeds state-of-the-art methods on RefCOCO, RefCOCO+ and G-Ref benchmark.
- AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion25
The AnomalyXFusion framework, designed to harness multi-modality information to enhance the quality of synthesized abnormal samples, is presented and a new dataset is proposed, called MVTec Caption, to reveal the multi-modality representational power of AnomalyXFusion.
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- Image contrast enhancement based on intensity expansion-compression24
Experimental results show that the proposed approach based on expanding and compressing the intensity dynamic range outperforms other approaches, which are based on histogram divisions and clippings, in terms of image contrast enhancement.
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- EOV-Seg: Efficient Open-Vocabulary Panoptic Segmentation16
EOV-Seg, a novel single-stage, shared, efficient, and spatialaware framework designed for open-vocabulary panoptic segmentation, is proposed, which runs faster and achieves competitive performance compared with state-of-the-art methods.
- PixelFace+: Towards Controllable Face Generation and Manipulation with Text Descriptions and Segmentation Masks14
This paper proposes a novel face generation model termed PixelFace+, which not only outperforms existing generation methods in both image quality and conditional matching but also shows a much superior controllability of face generation.
- Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision10
This paper builds a unified framework to perform effective noisy-proposal suppression and to interact between global and local features for robust feature learning, and proposes category-aware weak supervision to concentrate on non-existent categories so as to provide deterministic information for local feature learning.
- X-Dreamer: Creating High-quality 3D Content by Bridging the Domain Gap Between Text-to-2D and Text-to-3D Generation9
X-Dreamer is a novel approach for high-quality text-to-3D content creation that effectively bridges the gap between text-to-2D and text-to-3D synthesis and demonstrates the effectiveness of the proposed method compared to existing text-to-3D approaches.
- Rethinking the Metric in Few-shot Learning9
This paper investigates the contributions of different distance metrics, proposes an adaptive fusion scheme, and designs a few-shot classification framework AMTNet, including the AMM and the Global Adaptive Loss, to jointly optimize the few- shot task and auxiliary self-supervised task, making the embedding features more robust.
- FocSAM: Delving Deeply into Focused Objects in Segmenting Anything8
FocSAM augments SAM's interactive segmentation performance to match the existing state-of-the-art method in segmentation quality, requiring only about 5.6% of this method's inference time on CPUs.
- QueryMatch: A Query-based Contrastive Learning Framework for Weakly Supervised Visual Grounding8
A novel query-based one-stage framework for weakly supervised visual grounding, namely QueryMatch, and an innovative strategy for effective weakly supervised learning, namely Active Query Selection (AQS), which aims to enhance the effectiveness of query-based contrastive learning by actively selecting high-quality query features.
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- Prompting to Adapt Foundational Segmentation Models2
This study proposes a novel adaptation paradigm, termed "prompting-to-adapt'', to tackle the inherent non-differentiability of image prompts, and devise an information-estimation-based gradient descent strategy that leverages the information entropy of image processing combinations to optimize the prompter, ensuring effective adaptation.
- Towards Omni-supervised Referring Expression Segmentation2
Omni-RES is a novel learning task called Omni-supervised Referring Expression Segmentation (Omni-RES) which leverages unlabeled, fully labeled, and weakly labeled data, such as referring points or bounding boxes, for efficient RES training.
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- Rethinking 3D Dense Caption and Visual Grounding in A Unified Framework through Prompt-based Localization–
Inspired by DETR, this work proposes a unified framework, 3DGCTR, to jointly solve these two distinct but closely related tasks in an end-to-end fashion, and facilitates simultaneous multi-task training on both tasks, mutually enhancing their performance.
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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.
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