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- Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling1,232
Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT to 3D point cloud, is presented and it is shown that a pure Transformer architecture attains 93.8% accuracy on ModelNet40 and 83.1% accuracy in the hardest setting of ScanObjectNN, surpassing carefully designed point cloud models with much fewer hand-made designs.
- EVA: Exploring the Limits of Masked Visual Representation Learning at Scale1,054
Initializing the vision tower of a giant CLIP from EVA can greatly stabilize the training and outperform the training from scratch counterpart with much fewer samples and less compute, providing a new direction for scaling up and accelerating the costly training of multi-modal foundation models.
- Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks821
It is shown that incorporating learnable membrane time constants can make the network less sensitive to initial values and can speed up learning, and reevaluate the pooling methods in SNNs and find that max-pooling will not lead to significant information loss and have the advantage of low computation cost and binary compatibility.
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- EVA-02: A visual representation for neon genesis608
EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling, is launched, demonstrating superior performance compared to prior state-of-the-art approaches across various representative vision tasks, while utilizing significantly fewer parameters and compute budgets.
- Generative Multimodal Models are In-Context Learners545
This work demonstrates that the task-agnostic in-context learning capabilities of large multimodal models can be significantly enhanced by effective scaling-up, and introduces Emu2, a generative multimodal model with 37 billion parameters, trained on large-scale multimodal sequences with a unified autoregressive objective.
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- Images Speak in Images: A Generalist Painter for In-Context Visual Learning412
This work presents Painter, a generalist model which addresses obstacles with an “image”-centric solution, that is, to redefine the output of core vision tasks as images, and specify task prompts as also images.
- Bi-Directional Cascade Network for Perceptual Edge Detection362
A Bi-Directional Cascade Network (BDCN) structure is proposed, where an individual layer is supervised by labeled edges at its specific scale, rather than directly applying the same supervision to all CNN outputs.
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- Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks316
This paper theoretically analyzes ANN-SNN conversion error, and proposes the quantization clip-floor-shift activation function to replace the ReLU activation function in source ANNs, which can better approximate the activation function of SNNs.
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- Learning Open Set Network with Discriminative Reciprocal Points294
A new concept, Reciprocal Point, which is the potential representation of the extra-class space corresponding to each known category, which can indirectly introduce the unknown information into the learner with only known classes, so as to learn more compact and discriminative representations.
- SegGPT: Segmenting Everything In Context291
SegGPT is presented, a generalist model for segmenting everything in context that accommodates different kinds of segmentation data by transforming them into the same format of images and shows strong capabilities in segmenting in-domain and out-of-domain targets.
- Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding288
This work proposes Video-XL, a novel approach that leverages MLLMs’ inherent key-value (KV) sparsification capacity to condense the visual input, and introduces a new special token, the Visual Summarization Token (VST), for each interval of the video, which summarizes the visual information within the interval as its associated KV.
- Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics271
The definition, formulation, and paradigm of VCM are given, and the preliminary results have demonstrated the performance and efficiency gains, and state-of-the-art techniques in video compression and feature compression are systematically reviewed from the unique perspective of MPEG standardization.
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- Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks208
This paper theoretically analyze ANN-SNN conversion and derive sufficient conditions of the optimal conversion, and proposes Rate Norm Layer to replace the ReLU activation function in source ANN training, enabling direct conversion from a trained ANN to an SNN.
- Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning197
A Transductive Episodic-wise Adaptive Metric (TEAM) framework for few-shot learning is proposed, by integrating the meta-learning paradigm with both deep metric learning and transductive inference and leverages an attention-based bi-directional similarity strategy for extracting the more robust relationship between queries and prototypes.
- Multi-Scale 3D Convolution Network for Video Based Person Re-Identification187
Evaluations on three widely used benchmarks datasets, i.e., MARS, PRID2011, and iLIDS-VID demonstrate the substantial advantages of the proposed two-stream convolution network over existing 3D convolution networks and state-of-art methods.
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- Probabilistic Multi-Task Learning for Visual Saliency Estimation in Video155
This work proposes a multi-task learning algorithm to learn the task-related “stimulus-saliency” mapping functions for each scene, which learns various fusion strategies, which are used to integrate the stimulus-driven and task- related components to obtain the visual saliency.
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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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