349 citations · 519 across the 27 of their papers we have counts for
26 papers · 1 filter
Report of the 8th LSVOS Challenge: Complex and Multimodal Video Object Segmentation
Chang Liu, Henghui Ding, Lingyi Hong +36
This report summarizes the 8th Large-scale Video Object Segmentation (LSVOS) Challenge, held in conjunction with ECCV 2026. The challenge evaluates video segmentation in three comp…
Autoregressive Pretraining with Mamba in Vision
Sucheng Ren, Xianhang Li, Haoqin Tu +9
The vision community has started to build with the recently developed state space model, Mamba, as the new backbone for a range of tasks. This paper shows that Mamba's visual capab…
Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters
Weizhi Wang, Khalil Mrini, Linjie Yang +4
We propose a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs). Our approach outperforms predominant filtering methods (e.g.,…
Video Recognition in Portrait Mode
Mingfei Han, Linjie Yang, Xiaojie Jin +3
The creation of new datasets often presents new challenges for video recognition and can inspire novel ideas while addressing these challenges. While existing datasets mainly compr…
Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos
Mingfei Han, Linjie Yang, Xiaojun Chang +2
A short clip of video may contain progression of multiple events and an interesting story line. A human need to capture both the event in every shot and associate them together to…
Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling
Haogeng Liu, Qihang Fan, Tingkai Liu +5
This paper proposes Video-Teller, a video-language foundation model that leverages multi-modal fusion and fine-grained modality alignment to significantly enhance the video-to-text…
Selective Feature Adapter for Dense Vision Transformers
Xueqing Deng, Qi Fan, Xiaojie Jin +2
Fine-tuning pre-trained transformer models, e.g., Swin Transformer, are successful in numerous downstream for dense prediction vision tasks. However, one major issue is the cost/st…
The Devil is in the Details: A Deep Dive into the Rabbit Hole of Data Filtering
Haichao Yu, Yu Tian, Sateesh Kumar +2
The quality of pre-training data plays a critical role in the performance of foundation models. Popular foundation models often design their own recipe for data filtering, which ma…
Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?
Cheng-En Wu, Yu Tian, Haichao Yu +4
Vision-language models such as CLIP learn a generic text-image embedding from large-scale training data. A vision-language model can be adapted to a new classification task through…
Exploring the Role of Audio in Video Captioning
Yuhan Shen, Linjie Yang, Longyin Wen +3
Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for…
Former: Unified etrieval and eranking Transformer for Place Recognition
Sijie Zhu, Linjie Yang, Chen Chen +3
Visual Place Recognition (VPR) estimates the location of query images by matching them with images in a reference database. Conventional methods generally adopt aggregated CNN feat…
Revisiting Training-free NAS Metrics: An Efficient Training-based Method
Taojiannan Yang, Linjie Yang, Xiaojie Jin +1
Recent neural architecture search (NAS) works proposed training-free metrics to rank networks which largely reduced the search cost in NAS. In this paper, we revisit these training…
Robust High-Resolution Video Matting with Temporal Guidance
Shanchuan Lin, Linjie Yang, Imran Saleemi +1
We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance. Our method is much lighter than previous approaches and…
HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers
Mingyu Ding, Xiaochen Lian, Linjie Yang +4
High-resolution representations (HR) are essential for dense prediction tasks such as segmentation, detection, and pose estimation. Learning HR representations is typically ignored…
DeepViT: Towards Deeper Vision Transformer
Daquan Zhou, Bingyi Kang, Xiaojie Jin +5
Vision transformers (ViTs) have been successfully applied in image classification tasks recently. In this paper, we show that, unlike convolution neural networks (CNNs)that can be…
Progressive Temporal Feature Alignment Network for Video Inpainting
Xueyan Zou, Linjie Yang, Ding Liu +1
Video inpainting aims to fill spatio-temporal "corrupted" regions with plausible content. To achieve this goal, it is necessary to find correspondences from neighbouring frames to…
AutoSpace: Neural Architecture Search with Less Human Interference
Daquan Zhou, Xiaojie Jin, Xiaochen Lian +4
Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction. In this paper, we consider automati…
Learning Versatile Neural Architectures by Propagating Network Codes
Mingyu Ding, Yuqi Huo, Haoyu Lu +5
This work explores how to design a single neural network capable of adapting to multiple heterogeneous vision tasks, such as image segmentation, 3D detection, and video recognition…
CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation
Yang Fu, Linjie Yang, Ding Liu +2
Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video. Previous approaches only utilize single-frame features…
FracBits: Mixed Precision Quantization via Fractional Bit-Widths
Linjie Yang, Qing Jin
Model quantization helps to reduce model size and latency of deep neural networks. Mixed precision quantization is favorable with customized hardwares supporting arithmetic operati…
Neural Architecture Search for Lightweight Non-Local Networks
Yingwei Li, Xiaojie Jin, Jieru Mei +7
Non-Local (NL) blocks have been widely studied in various vision tasks. However, it has been rarely explored to embed the NL blocks in mobile neural networks, mainly due to the fol…
Towards Efficient Training for Neural Network Quantization
Qing Jin, Linjie Yang, Zhenyu Liao
Quantization reduces computation costs of neural networks but suffers from performance degeneration. Is this accuracy drop due to the reduced capacity, or inefficient training duri…
AdaBits: Neural Network Quantization with Adaptive Bit-Widths
Qing Jin, Linjie Yang, Zhenyu Liao
Deep neural networks with adaptive configurations have gained increasing attention due to the instant and flexible deployment of these models on platforms with different resource b…
AtomNAS: Fine-Grained End-to-End Neural Architecture Search
Jieru Mei, Yingwei Li, Xiaochen Lian +4
Search space design is very critical to neural architecture search (NAS) algorithms. We propose a fine-grained search space comprised of atomic blocks, a minimal search unit that i…