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- A large-scale car dataset for fine-grained categorization and verification882
This paper presents an on-going effort in collecting a large-scale dataset, “CompCars”, that covers not only different car views, but also their different internal and external parts, and rich attributes, and demonstrates a few important applications exploiting the dataset.
- Video Instance Segmentation663
The first time that the image instance segmentation problem is extended to the video domain, and a novel algorithm called MaskTrack R-CNN is proposed for this task, which is simultaneous detection, segmentation and tracking of instances in videos.
- YouTube-VOS: Sequence-to-Sequence Video Object Segmentation598
This work builds a new large-scale video object segmentation dataset called YouTube Video Object Segmentation dataset (YouTube-VOS) and proposes a novel sequence-to-sequence network to fully exploit long-term spatial-temporal information in videos for segmentation.
- Efficient Video Object Segmentation via Network Modulation373
This work proposes a novel approach that uses a single forward pass to adapt the segmentation model to the appearance of a specific object and is 70× faster than fine-tuning approaches and achieves similar accuracy.
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- Robust High-Resolution Video Matting with Temporal Guidance226
A robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance and proposes a novel training strategy that enforces the network on both matting and segmentation objectives, which significantly improves the model’s robustness.
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- Dense Captioning with Joint Inference and Visual Context179
A new model pipeline based on two novel ideas, joint inference and context fusion, is proposed, which achieves state-of-the-art accuracy on Visual Genome for dense captioning with a relative gain of 73% compared to the previous best algorithm.
- $R^{2}$ Former: Unified Retrieval and Reranking Transformer for Place Recognition171
A unified place recognition framework that handles both retrieval and reranking with a novel transformer model, named R^{2}$ Former, which significantly outperforms state-of-the-art methods on major VPR datasets with much less inference time and memory consumption.
- Streamlined Dense Video Captioning171
A novel dense video captioning framework is proposed, which models temporal dependency across events in a video explicitly and leverages visual and linguistic context from prior events for coherent storytelling.
- AdaBits: Neural Network Quantization With Adaptive Bit-Widths155
It is demonstrated that bit-width of weights and activations is a new option for adaptively executable deep neural networks, offering a distinct opportunity for improved accuracy-efficiency trade-off as well as instant adaptation according to the platform constraints in real-world applications.
- FracBits: Mixed Precision Quantization via Fractional Bit-Widths102
A novel learning-based algorithm is proposed to derive mixed precision models end-to-end under target computation constraints and model sizes under different resource constraints on ImageNet dataset.
- Dual Diffusion for Unified Image Generation and Understanding91
A large-scale and fully end-to-end diffusion model for multi-modal understanding and generation that significantly improves on existing diffusion-based multimodal models, and is the first of its kind to support the full suite of vision-language modeling capabilities.
- HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers82
This work proposes a novel NAS method, called HR-NAS, which is able to find efficient and accurate networks for different tasks, by effectively encoding multiscale contextual information while maintaining high-resolution representations.
- CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation80
This work proposes a novel comprehensive feature aggregation approach (CompFeat) to refine features at both frame-level and object-level with temporal and spatial context information to eliminate ambiguities introduced by only using single-frame features.
- Progressive Temporal Feature Alignment Network for Video Inpainting72
This work proposes a ‘Progressive Temporal Feature Alignment Network’, which progressively enriches features extracted from the current frame with the feature warped from neighbouring frames using optical flow, greatly improving visual quality and temporal consistency of the inpainted videos.
- Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model67
This work presents Seedream 2.0, a native Chinese-English bilingual image generation foundation model that excels across diverse dimensions, which adeptly manages text prompt in both Chinese and English, supporting bilingual image generation and text rendering.
- Neural Architecture Search for Lightweight Non-Local Networks55
This work proposes a Lightweight Non-Local (LightNL) block by squeezing the transformation operations and incorporating compact features, and proposes an efficient neural architecture search algorithm to learn an optimal configuration of LightNL blocks in an end-to-end manner.
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- Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?35
It is demonstrated that noisy zero-shot predictions from CLIP can be used to tune its own prompt, significantly enhancing prediction accuracy in the unsupervised setting.
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- Context-Aware Zero-Shot Recognition32
This work aims to understand the identity of the novel objects in an image surrounded by the known objects using the inter-object relation prior, and leverage the visual context and the geometric relationships between all pairs of objects in a single image, and capture the information useful to infer unseen categories.
- Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters31
This work proposes a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs), which outperforms predominant filtering methods and achieves significant improvements over CLIPScore on popular foundation models and various downstream tasks.
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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