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- Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis1,687
Video-MME is introduced, the first-ever full-spectrum, Multi-Modal Evaluation benchmark of MLLMs in Video analysis, and it is revealed that Gemini 1.5 Pro is the best-performing commercial model, significantly outperforming the open-source models with an average accuracy of 75%, compared to 71.9% for GPT-4o.
- Contrastive Learning for Compact Single Image Dehazing926
This paper proposes a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively and develops a compact dehazing network based on autoencoder-like framework.
- Towards Optimal Structured CNN Pruning via Generative Adversarial Learning585
This paper proposes an effective structured pruning approach that jointly prunes filters as well as other structures in an end-to-end manner and effectively solves the optimization problem by generative adversarial learning (GAL), which learns a sparse soft mask in a label-free and an end to end manner.
- Accelerating Convolutional Networks via Global & Dynamic Filter Pruning274
This paper proposes a novel global & dynamic pruning (GDP) scheme to prune redundant filters for CNN acceleration that achieves superior performance to accelerate several cutting-edge CNNs on the ILSVRC 2012 benchmark.
- Fusion-Mamba for Cross-Modality Object Detection190
This work investigates a novel cross-modality fusion approach by associating cross-modal features in a hidden state space based on an improved Mamba with a gating attention mechanism, and establishes a new baseline for cross-modality object detection.
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- Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification172
A new strategy, Variational Self-Distillation (VSD), is presented, which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it, and highlights the need to rethink the way to estimate mutual information.
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- Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression142
KSE is capable of simultaneously compressing each layer in an efficient way, which is significantly faster compared to previous data-driven feature map pruning methods, and significantly outperforms state-of-the-art methods.
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- Towards Compact CNNs via Collaborative Compression105
A Collaborative Compression scheme, which joints channel pruning and tensor decomposition to compress CNN models by simultaneously learning the model sparsity and low-rankness and proposes multi-step heuristic compression to remove redundant compression units step-by-step.
- PAMS: Quantized Super-Resolution via Parameterized Max Scale104
A new quantization scheme termed PArameterized Max Scale (PAMS), which applies the trainable truncated parameter to explore the upper bound of the quantization range adaptively and a structured knowledge transfer (SKT) loss is introduced to fine-tune the quantized network.
- 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.
- The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report91
This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes, and gauge the state-of-the-art in efficient single-image super-resolution.
- AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning78
This paper proposes a non-incremental learner, named AttriCLIP, to incrementally extract knowledge of new classes or tasks, built upon the pre-trained visual-language model CLIP and compares it with CLIP-based and previous state-of-the-art continual learning methods in realistic settings with domain-shift and long-sequence learning.
- 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.
- A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise68
A preliminary exploration of Gemini Pro's visual understanding proficiency, which comprehensively covers four domains: fundamental perception, advanced cognition, challenging vision tasks, and various expert capacities, demonstrates the potential of Gemini to be a strong challenger to GPT-4V.
- MISSU: 3D Medical Image Segmentation via Self-Distilling TransUNet64
This work proposes to self-distill a Transformer-based UNet for medical image segmentation, which simultaneously learns global semantic information and local spatial-detailed features and achieves the best performance over previous state-of-the-art methods.
- Towards Compact Single Image Super-Resolution via Contrastive Self-distillation61
A novel contrastive self-distillation (CSD) framework to simultaneously compress and accelerate various off-the-shelf SR models to improve the quality of SR images and PSNR/SSIM via explicit knowledge transfer is proposed.
- Masked face detection via a modified LeNet54
A modified LeNet, termed MLeNet, is presented, which modifies the number of units in output layer of LeNet to suit for a specific classification, which reduces overfitting and improves the performance with a small quantity of training samples.
- Towards convolutional neural networks compression via global error reconstruction47
A global error reconstruction method termed GER is presented, which firstly leverages an SVD-based low-rank approximation to coarsely compress the parameters in the fully connected layers in a layerwise manner, and jointly optimized in a global perspective via back-propagation.
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- WaveMamba: Wavelet-Driven Mamba Fusion for RGB-Infrared Object Detection45
WaveMamba is proposed, a cross-modality fusion method that efficiently integrates the unique and complementary frequency features of RGB and IR decomposed by Discrete Wavelet Transform (DWT).
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- DisCo: Remedying Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning34
It is found that the final inherent embedding of the mainstream SSL methods contains the most important information, and it is proposed to distill the final embedding to maximally transmit a teacher’s knowledge to a lightweight model by constraining the lastEmbedding of the student to be consistent with that of the teacher.
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 2026-10-15. 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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