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Generalized Decoding for Pixel, Image, and Language

Generalized Decoding for Pixel, Image, and Language

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zou, Xueyan Dou, Zi-Yi Yang, Jianwei Gan, Zhe Li, Linjie Li, Chunyuan Dai, Xiyang Behl, Harkirat Wang, Jianfeng Yuan, Lu Peng, Nanyun Wang, Lijuan Lee, Yong Jae Gao, Jianfeng Univ Wisconsin Madison Madison WI 53706 USA UCLA Los Angeles CA 90024 USA Microsoft Res Redmond Redmond WA USA Microsoft Cloud & AI Redmond WA USA

ISBN: (纸本)9798350301298

We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decoder takes as input two types of queries: (i) generic non-semantic queries and (ii) semantic queries induced from text inputs, to decode different pixel-level and token-level outputs in the same semantic space. With such a novel design, X-Decoder is the first work that provides a unified way to support all types of image segmentation and a variety of vision-language (VL) tasks. Without any pseudo-labeling, our design enables seamless interactions across tasks at different granularities and brings mutual benefits by learning a common and rich pixel-level understanding. After pretraining on a mixed set of a limited amount of segmentation data and millions of image-text pairs, X-Decoder exhibits strong transferability to a wide range of downstream tasks in both zero-shot and finetuning settings. Notably, it achieves (1) state-of-the-art results on open-vocabulary segmentation and referring segmentation on seven datasets;(2) better or competitive finetuned performance to other generalist and specialist models on segmentation and VL tasks;and (3) flexibility for efficient finetuning and novel task composition (e.g., referring captioning and image editing shown in Fig. 1). Code, demo, video and visualization are available at: https://***.

关键词： and reasoning language vision

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3D Concept Learning and Reasoning from Multi-View Images

3D Concept Learning and Reasoning from Multi-View Images

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hong, Yining Lin, Chunru Du, Yilun Chen, Zhenfang Tenenbaum, Joshua B. Gan, Chuang UCLA Los Angeles CA 90095 USA Shanghai Jiao Tong Univ Shanghai Peoples R China MIT CSAIL Cambridge MA USA UMass Amherst Amherst MA USA MIT IBM Watson Lab Cambridge MA USA

ISBN: (纸本)9798350301298

Humans are able to accurately reason in 3D by gathering multi-view observations of the surrounding world. Inspired by this insight, we introduce a new large-scale benchmark for 3D multi-view visual question answering (3DMV-VQA). This dataset is collected by an embodied agent actively moving and capturing RGB images in an environment using the Habitat simulator. In total, it consists of approximately 5k scenes, 600k images, paired with 50k questions. We evaluate various state-of-the-art models for visual reasoning on our benchmark and find that they all perform poorly. We suggest that a principled approach for 3D reasoning from multi-view images should be to infer a compact 3D representation of the world from the multi-view images, which is further grounded on open-vocabulary semantic concepts, and then to execute reasoning on these 3D representations. As the first step towards this approach, we propose a novel 3D concept learning and reasoning (3D-CLR) framework that seamlessly combines these components via neural fields, 2D pre-trained vision-language models, and neural reasoning operators. Experimental results suggest that our framework outperforms baseline models by a large margin, but the challenge remains largely unsolved. We further perform an in-depth analysis of the challenges and highlight potential future directions.

关键词： and reasoning language vision

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Uni-Perceiver v2: A Generalist Model for Large-Scale vision and vision-Language Tasks

Uni-Perceiver v2: A Generalist Model for Large-Scale Vision ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Hao Zhu, Jinguo Jiang, Xiaohu Zhu, Xizhou Li, Hongsheng Yuan, Chun Wang, Xiaohua Qiao, Yu Wang, Xiaogang Wang, Wenhai Dai, Jifeng Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Xi An Jiao Tong Univ Xian Peoples R China Tsinghua Univ SIGS Beijing Peoples R China SenseTime Res Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Despite the remarkable success of foundation models, their task-specific fine-tuning paradigm makes them inconsistent with the goal of general perception modeling. The key to eliminating this inconsistency is to use generalist models for general task modeling. However, existing attempts at generalist models are inadequate in both versatility and performance. In this paper, we propose UniPerceiver v2, which is the first generalist model capable of handling major large-scale vision and vision-language tasks with competitive performance. Specifically, images are encoded as general region proposals, while texts are encoded via a Transformer-based language model. The encoded representations are transformed by a task-agnostic decoder. Different tasks are formulated as a unified maximum likelihood estimation problem. We further propose an effective optimization technique named Task-Balanced Gradient Normalization to ensure stable multi-task learning with an unmixed sampling strategy, which is helpful for tasks requiring large batch-size training. After being jointly trained on various tasks, Uni-Perceiver v2 is capable of directly handling downstream tasks without any task-specific adaptation. Results show that Uni-Perceiver v2 outperforms all existing generalist models in both versatility and performance. Meanwhile, compared with the commonly-recognized strong baselines that require tasks-specific finetuning, Uni-Perceiver v2 achieves competitive performance on a broad range of vision and vision-language tasks.

关键词： detection recognition: Categorization retrieval

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Hierarchical Semantic Correspondence Networks for Video Paragraph Grounding

Hierarchical Semantic Correspondence Networks for Video Para...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tan, Chaolei Lin, Zihang Hu, Jian-Fang Zheng, Wei-Shi Lai, Jianhuang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (纸本)9798350301298

Video Paragraph Grounding (VPG) is an essential yet challenging task in vision-language understanding, which aims to jointly localize multiple events from an untrimmed video with a paragraph query description. One of the critical challenges in addressing this problem is to comprehend the complex semantic relations between visual and textual modalities. Previous methods focus on modeling the contextual information between the video and text from a single-level perspective (i.e., the sentence level), ignoring rich visual-textual correspondence relations at different semantic levels, e.g., the video-word and video-paragraph correspondence. To this end, we propose a novel Hierarchical Semantic Correspondence Network (HSCNet), which explores multi-level visual-textual correspondence by learning hierarchical semantic alignment and utilizes dense supervision by grounding diverse levels of queries. Specifically, we develop a hierarchical encoder that encodes the multi-modal inputs into semantics-aligned representations at different levels. To exploit the hierarchical semantic correspondence learned in the encoder for multi-level supervision, we further design a hierarchical decoder that progressively performs finer grounding for lower-level queries conditioned on higher-level semantics. Extensive experiments demonstrate the effectiveness of HSCNet and our method significantly outstrips the state-of-the-arts on two challenging benchmarks, i.e., ActivityNet-Captions and TACoS.

关键词： and reasoning language vision

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Position-guided Text Prompt for vision-Language Pre-training

Position-guided Text Prompt for Vision-Language Pre-training

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Jinpeng Zhou, Pan Shou, Mike Zheng Yan, Shuicheng Sea AI Lab Singapore Singapore Natl Univ Singapore Show Lab Singapore Singapore

ISBN: (纸本)9798350301298

vision-Language Pre-Training (VLP) has shown promising capabilities to align image and text pairs, facilitating a broad variety of cross-modal learning tasks. However, we observe that VLP models often lack the visual grounding/localization capability which is critical for many downstream tasks such as visual reasoning. In this work, we propose a novel Position-guided Text Prompt (PTP) paradigm to enhance the visual grounding ability of cross-modal models trained with VLP. Specifically, in the VLP phase, PTP divides the image into N x N blocks, and identifies the objects in each block through the widely used object detector in VLP. It then reformulates the visual grounding task into a fill-in-the-blank problem given a PTP by encouraging the model to predict the objects in the given blocks or regress the blocks of a given object, e.g. filling "[P]" or "[O]" in a PTP "The block [P] has a [O]". This mechanism improves the visual grounding capability of VLP models and thus helps them better handle various downstream tasks. By introducing PTP into several state-of-the-art VLP frameworks, we observe consistently significant improvements across representative cross-modal learning model architectures and several benchmarks, e.g. zero-shot Flickr30K Retrieval (+4.8 in average recall@1) for ViLT [16] baseline, and COCO Captioning (+5.3 in CIDEr) for SOTA BLIP [19] baseline. Moreover, PTP achieves comparable results with object-detector based methods [8, 23, 45], and much faster inference speed since PTP discards its object detector for inference while the later cannot.

关键词： Multi-modal learning

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Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking

Observation-Centric SORT: Rethinking SORT for Robust Multi-O...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cao, Jinkun Pang, Jiangmiao Weng, Xinshuo Khirodkar, Rawal Kitani, Kris Carnegie Mellon Univ Pittsburgh PA 15213 USA Shanghai AI Lab Shanghai Peoples R China Nvidia Santa Clara CA USA

ISBN: (纸本)9798350301298

Kalman filter (KF) based methods for multi-object tracking (MOT) make an assumption that objects move linearly. While this assumption is acceptable for very short periods of occlusion, linear estimates of motion for prolonged time can be highly inaccurate. Moreover, when there is no measurement available to update Kalman filter parameters, the standard convention is to trust the priori state estimations for posteriori update. This leads to the accumulation of errors during a period of occlusion. The error causes significant motion direction variance in practice. In this work, we show that a basic Kalman filter can still obtain state-of-the-art tracking performance if proper care is taken to fix the noise accumulated during occlusion. Instead of relying only on the linear state estimate (i.e., estimation-centric approach), we use object observations (i.e., the measurements by object detector) to compute a virtual trajectory over the occlusion period to fix the error accumulation of filter parameters. This allows more time steps to correct errors accumulated during occlusion. We name our method Observation-Centric SORT (OC-SORT). It remains Simple, Online, and Real-Time but improves robustness during occlusion and non-linear motion. Given off-the-shelf detections as input, OC-SORT runs at 700+ FPS on a single CPU. It achieves state-of-the-art on multiple datasets, including MOT17, MOT20, KITTI, head tracking, and especially DanceTrack where the object motion is highly non-linear. The code and models are available at https://***/noahcao/OC_SORT.

关键词： vision applications and systems

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BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud Registration

BUFFER: Balancing Accuracy, Efficiency, and Generalizability...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ao, Sheng Hu, Qingyong Wang, Hanyun Xu, Kai Guo, Yulan Sun Yat Sen Univ Shenzhen Campus Shenzhen Peoples R China Univ Oxford Oxford England Informat Engn Univ Zhengzhou Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9798350301298

An ideal point cloud registration framework should have superior accuracy, acceptable efficiency, and strong generalizability. However, this is highly challenging since existing registration techniques are either not accurate enough, far from efficient, or generalized poorly. It remains an open question that how to achieve a satisfying balance between this three key elements. In this paper, we propose BUFFER, a point cloud registration method for balancing accuracy, efficiency, and generalizability. The key to our approach is to take advantage of both point-wise and patch-wise techniques, while overcoming the inherent drawbacks simultaneously. Different from a simple combination of existing methods, each component of our network has been carefully crafted to tackle specific issues. Specifically, a Point-wise Learner is first introduced to enhance computational efficiency by predicting keypoints and improving the representation capacity of features by estimating point orientations, a Patch-wise Embedder which leverages a lightweight local feature learner is then deployed to extract efficient and general patch features. Additionally, an Inliers Generator which combines simple neural layers and general features is presented to search inlier correspondences. Extensive experiments on real-world scenarios demonstrate that our method achieves the best of both worlds in accuracy, efficiency, and generalization. In particular, our method not only reaches the highest success rate on unseen domains, but also is almost 30 times faster than the strong baselines specializing in generalization. Code is available at https://***/aosheng1996/BUFFER.

关键词： language reasoning vision

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A Unified HDR Imaging Method with Pixel and Patch Level

A Unified HDR Imaging Method with Pixel and Patch Level

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yan, Qingsen Chen, Weiye Zhang, Song Zhu, Yu Sun, Jinqiu Zhang, Yanning Northwestern Polytech Univ Xian Peoples R China Xidian Univ Xian Peoples R China

ISBN: (纸本)9798350301298

Mapping Low Dynamic Range (LDR) images with different exposures to High Dynamic Range (HDR) remains non-trivial and challenging on dynamic scenes due to ghosting caused by object motion or camera jitting. With the success of Deep Neural Networks (DNNs), several DNNs-based methods have been proposed to alleviate ghosting, they cannot generate approving results when motion and saturation occur. To generate visually pleasing HDR images in various cases, we propose a hybrid HDR deghosting network, called HyHDRNet, to learn the complicated relationship between reference and non-reference images. The proposed HyHDRNet consists of a content alignment subnetwork and a Transformer-based fusion subnetwork. Specifically, to effectively avoid ghosting from the source, the content alignment subnetwork uses patch aggregation and ghost attention to integrate similar content from other non-reference images with patch level and suppress undesired components with pixel level. To achieve mutual guidance between patch-level and pixel-level, we leverage a gating module to sufficiently swap useful information both in ghosted and saturated regions. Furthermore, to obtain a high-quality HDR image, the Transformer-based fusion subnetwork uses a Residual Deformable Transformer Block (RDTB) to adaptively merge information for different exposed regions. We examined the proposed method on four widely used public HDR image deghosting datasets. Experiments demonstrate that HyHDRNet outperforms state-of-the-art methods both quantitatively and qualitatively, achieving appealing HDR visualization with unified textures and colors.

关键词： Low-level vision

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Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning

Vid2Seq: Large-Scale Pretraining of a Visual Language Model ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Antoine Nagrani, Arsha Seo, Paul Hongsuck Miech, Antoine Pont-Tuset, Jordi Laptev, Ivan Sivic, Josef Schmid, Cordelia Google Res Mountain View CA USA Inria Paris Paris France PSL Res Univ CNRS Dept Informat ENS Paris France DeepMind London England Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Google Mountain View CA USA

ISBN: (纸本)9798350301298

In this work, we introduce Vid2Seq, a multi-modal single-stage dense event captioning model pretrained on narrated videos which are readily-available at scale. The Vid2Seq architecture augments a language model with special time tokens, allowing it to seamlessly predict event boundaries and textual descriptions in the same output sequence. Such a unified model requires large-scale training data, which is not available in current annotated datasets. We show that it is possible to leverage unlabeled narrated videos for dense video captioning, by reformulating sentence boundaries of transcribed speech as pseudo event boundaries, and using the transcribed speech sentences as pseudo event captions. The resulting Vid2Seq model pretrained on the YT-Temporal-1B dataset improves the state of the art on a variety of dense video captioning benchmarks including YouCook2, ViTT and ActivityNet Captions. Vid2Seq also generalizes well to the tasks of video paragraph captioning and video clip captioning, and to few-shot settings. Our code is publicly available at [1].

关键词： language reasoning vision

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FCC: Feature Clusters Compression for Long-Tailed Visual recognition

FCC: Feature Clusters Compression for Long-Tailed Visual Rec...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Jian Meng, Ziyao Shi, Daqian Song, Rui Diao, Xiaolei Wang, Jingwen Xu, Hao Jilin Univ Changchun Peoples R China Univ Minho Braga Portugal Univ Toronto Toronto ON Canada

ISBN: (纸本)9798350301298

Deep Neural Networks (DNNs) are rather restrictive in long-tailed data, since they commonly exhibit an under-representation for minority classes. Various remedies have been proposed to tackle this problem from different perspectives, but they ignore the impact of the density of Backbone Features (BFs) on this issue. Through representation learning, DNNs can map BFs into dense clusters in feature space, while the features of minority classes often show sparse clusters. In practical applications, these features are discretely mapped or even cross the decision boundary resulting in misclassification. Inspired by this observation, we propose a simple and generic method, namely Feature Clusters Compression (FCC), to increase the density of BFs by compressing backbone feature clusters. The proposed FCC can be easily achieved by only multiplying original BFs by a scaling factor in training phase, which establishes a linear compression relationship between the original and multiplied features, and forces DNNs to map the former into denser clusters. In test phase, we directly feed original features without multiplying the factor to the classifier, such that BFs of test samples are mapped closer together and do not easily cross the decision boundary. Meanwhile, FCC can be friendly combined with existing long-tailed methods and further boost them. We apply FCC to numerous state-of-the-art methods and evaluate them on widely used longtailed benchmark datasets. Extensive experiments fully verify the effectiveness and generality of our method. Code is available at https://***/lijian16/FCC.

关键词： detection recognition: Categorization retrieval

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