pwc-archive/papers-with-abstracts huggingface dataset:
Making LLaMA SEE and Draw with SEED Tokenizer
Authors: Yuying Ge, Sijie Zhao, Ziyun Zeng, Yixiao Ge, Chen Li, Xintao Wang, Ying Shan
Date: 2023-10-02 00:00:00
Tasks: multimodal generation

The great success of Large Language Models (LLMs) has expanded the potential of multimodality, contributing to the gradual evolution of General Artificial Intelligence (AGI). A true AGI agent should not only possess the capability to perform predefined multi-tasks but also exhibit emergent abilities in an open-world context. However, despite the considerable advancements made by recent multimodal LLMs, they still fall short in effectively unifying comprehension and generation tasks, let alone open-world emergent abilities. We contend that the key to overcoming the present impasse lies in enabling text and images to be represented and processed interchangeably within a unified autoregressive Transformer. To this end, we introduce SEED, an elaborate image tokenizer that empowers LLMs with the ability to SEE and Draw at the same time. We identify two crucial design principles: (1) Image tokens should be independent of 2D physical patch positions and instead be produced with a 1D causal dependency, exhibiting intrinsic interdependence that aligns with the left-to-right autoregressive prediction mechanism in LLMs. (2) Image tokens should capture high-level semantics consistent with the degree of semantic abstraction in words, and be optimized for both discriminativeness and reconstruction during the tokenizer training phase. With SEED tokens, LLM is able to perform scalable multimodal autoregression under its original training recipe, i.e., next-word prediction. SEED-LLaMA is therefore produced by large-scale pretraining and instruction tuning on the interleaved textual and visual data, demonstrating impressive performance on a broad range of multimodal comprehension and generation tasks. More importantly, SEED-LLaMA has exhibited compositional emergent abilities such as multi-turn in-context multimodal generation, acting like your AI assistant.
Reconstructing 3D Human Pose from RGB-D Data with Occlusions
Authors: Bowen Dang, Xi Zhao, BoWen Zhang, He Wang
Date: 2023-10-02 00:00:00

We propose a new method to reconstruct the 3D human body from RGB-D images with occlusions. The foremost challenge is the incompleteness of the RGB-D data due to occlusions between the body and the environment, leading to implausible reconstructions that suffer from severe human-scene penetration. To reconstruct a semantically and physically plausible human body, we propose to reduce the solution space based on scene information and prior knowledge. Our key idea is to constrain the solution space of the human body by considering the occluded body parts and visible body parts separately: modeling all plausible poses where the occluded body parts do not penetrate the scene, and constraining the visible body parts using depth data. Specifically, the first component is realized by a neural network that estimates the candidate region named the "free zone", a region carved out of the open space within which it is safe to search for poses of the invisible body parts without concern for penetration. The second component constrains the visible body parts using the "truncated shadow volume" of the scanned body point cloud. Furthermore, we propose to use a volume matching strategy, which yields better performance than surface matching, to match the human body with the confined region. We conducted experiments on the PROX dataset, and the results demonstrate that our method produces more accurate and plausible results compared with other methods.
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks
Authors: Meng Zhou, Matthias W Wagner, Uri Tabori, Cynthia Hawkins, Birgit B Ertl-Wagner, Farzad Khalvati
Date: 2023-10-02 00:00:00
Tasks: Brain Tumor Classification, Brain Tumor Segmentation, Medical Image Analysis, Quantization, Tumor Segmentation

Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. However, the effectiveness of such approaches is often limited by the amount of available data in clinical settings. Additionally, the common GAN-based approach is to generate entire image volumes, rather than solely the region of interest (ROI). Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes. In this work, we present a novel framework that uses vector-quantization GAN and a transformer incorporating masked token modeling to generate high-resolution and diverse 3D brain tumor ROIs that can be directly used as augmented data for the classification of brain tumor ROI. We apply our method to two imbalanced datasets where we augment the minority class: (1) the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019 dataset to generate new low-grade glioma (LGG) ROIs to balance with high-grade glioma (HGG) class; (2) the internal pediatric LGG (pLGG) dataset tumor ROIs with BRAF V600E Mutation genetic marker to balance with BRAF Fusion genetic marker class. We show that the proposed method outperforms various baseline models in both qualitative and quantitative measurements. The generated data was used to balance the data in the brain tumor types classification task. Using the augmented data, our approach surpasses baseline models by 6.4% in AUC on the BraTS 2019 dataset and 4.3% in AUC on our internal pLGG dataset. The results indicate the generated tumor ROIs can effectively address the imbalanced data problem. Our proposed method has the potential to facilitate an accurate diagnosis of rare brain tumors using MRI scans.
Offline Tracking with Object Permanence
Authors: Xianzhong Liu, Holger Caesar
Date: 2023-10-02 00:00:00
Tasks: 3D Multi-Object Tracking, Autonomous Driving, Multi-Object Tracking, Object, Object Tracking

To reduce the expensive labor cost for manual labeling autonomous driving datasets, an alternative is to automatically label the datasets using an offline perception system. However, objects might be temporally occluded. Such occlusion scenarios in the datasets are common yet underexplored in offline auto labeling. In this work, we propose an offline tracking model that focuses on occluded object tracks. It leverages the concept of object permanence which means objects continue to exist even if they are not observed anymore. The model contains three parts: a standard online tracker, a re-identification (Re-ID) module that associates tracklets before and after occlusion, and a track completion module that completes the fragmented tracks. The Re-ID module and the track completion module use the vectorized map as one of the inputs to refine the tracking results with occlusion. The model can effectively recover the occluded object trajectories. It achieves state-of-the-art performance in 3D multi-object tracking by significantly improving the original online tracking result, showing its potential to be applied in offline auto labeling as a useful plugin to improve tracking by recovering occlusions.
3DHR-Co: A Collaborative Test-time Refinement Framework for In-the-Wild 3D Human-Body Reconstruction Task
Authors: Jonathan Samuel Lumentut, Kyoung Mu Lee
Date: 2023-10-02 00:00:00
Tasks: 3D Human Pose Estimation, Test-time Adaptation

The field of 3D human-body reconstruction (abbreviated as 3DHR) that utilizes parametric pose and shape representations has witnessed significant advancements in recent years. However, the application of 3DHR techniques to handle real-world, diverse scenes, known as in-the-wild data, still faces limitations. The primary challenge arises as curating accurate 3D human pose ground truth (GT) for in-the-wild scenes is still difficult to obtain due to various factors. Recent test-time refinement approaches on 3DHR leverage initial 2D off-the-shelf human keypoints information to support the lack of 3D supervision on in-the-wild data. However, we observed that additional 2D supervision alone could cause the overfitting issue on common 3DHR backbones, making the 3DHR test-time refinement task seem intractable. We answer this challenge by proposing a strategy that complements 3DHR test-time refinement work under a collaborative approach. Specifically, we initially apply a pre-adaptation approach that works by collaborating various 3DHR models in a single framework to directly improve their initial outputs. This approach is then further combined with the test-time adaptation work under specific settings that minimize the overfitting issue to further boost the 3DHR performance. The whole framework is termed as 3DHR-Co, and on the experiment sides, we showed that the proposed work can significantly enhance the scores of common classic 3DHR backbones up to -34 mm pose error suppression, putting them among the top list on the in-the-wild benchmark data. Such achievement shows that our approach helps unveil the true potential of the common classic 3DHR backbones. Based on these findings, we further investigate various settings on the proposed framework to better elaborate the capability of our collaborative approach in the 3DHR task.
Efficient Remote Sensing Segmentation With Generative Adversarial Transformer
Authors: Luyi Qiu, Dayu Yu, Xiaofeng Zhang, Chenxiao Zhang
Date: 2023-10-02 00:00:00
Tasks: Segmentation, Semantic Segmentation

Most deep learning methods that achieve high segmentation accuracy require deep network architectures that are too heavy and complex to run on embedded devices with limited storage and memory space. To address this issue, this paper proposes an efficient Generative Adversarial Transfomer (GATrans) for achieving high-precision semantic segmentation while maintaining an extremely efficient size. The framework utilizes a Global Transformer Network (GTNet) as the generator, efficiently extracting multi-level features through residual connections. GTNet employs global transformer blocks with progressively linear computational complexity to reassign global features based on a learnable similarity function. To focus on object-level and pixel-level information, the GATrans optimizes the objective function by combining structural similarity losses. We validate the effectiveness of our approach through extensive experiments on the Vaihingen dataset, achieving an average F1 score of 90.17% and an overall accuracy of 91.92%.
Color and Texture Dual Pipeline Lightweight Style Transfer
Authors: Shiqi Jiang
Date: 2023-10-02 00:00:00
Tasks: Style Transfer, Texture Synthesis

Style transfer methods typically generate a single stylized output of color and texture coupling for reference styles, and color transfer schemes may introduce distortion or artifacts when processing reference images with duplicate textures. To solve the problem, we propose a Color and Texture Dual Pipeline Lightweight Style Transfer CTDP method, which employs a dual pipeline method to simultaneously output the results of color and texture transfer. Furthermore, we designed a masked total variation loss to suppress artifacts and small texture representations in color transfer results without affecting the semantic part of the content. More importantly, we are able to add texture structures with controllable intensity to color transfer results for the first time. Finally, we conducted feature visualization analysis on the texture generation mechanism of the framework and found that smoothing the input image can almost completely eliminate this texture structure. In comparative experiments, the color and texture transfer results generated by CTDP both achieve state-of-the-art performance. Additionally, the weight of the color transfer branch model size is as low as 20k, which is 100-1500 times smaller than that of other state-of-the-art models.
ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video
Authors: Xinhao Li, Yuhan Zhu, LiMin Wang
Date: 2023-10-02 00:00:00
Tasks: Action Classification, Action Recognition, Video Recognition

Adapting image models to the video domain has emerged as an efficient paradigm for solving video recognition tasks. Due to the huge number of parameters and effective transferability of image models, performing full fine-tuning is less efficient and even unnecessary. Thus, recent research is shifting its focus toward parameter-efficient image-to-video adaptation. However, these adaptation strategies inevitably introduce extra computational costs to deal with the domain gap and temporal modeling in videos. In this paper, we present a new adaptation paradigm (ZeroI2V) to transfer the image transformers to video recognition tasks (i.e., introduce zero extra cost to the original models during inference). To achieve this goal, we present two core designs. First, to capture the dynamics in videos and reduce the difficulty of image-to-video adaptation, we exploit the flexibility of self-attention and introduce spatial-temporal dual-headed attention (STDHA). This approach efficiently endows the image transformers with temporal modeling capability at zero extra parameters and computation. Second, to handle the domain gap between images and videos, we propose a linear adaption strategy that utilizes lightweight densely placed linear adapters to fully transfer the frozen image models to video recognition. Thanks to the customized linear design, all newly added adapters could be easily merged with the original modules through structural reparameterization after training, enabling zero extra cost during inference. Extensive experiments on representative fully-supervised and few-shot video recognition benchmarks showcase that ZeroI2V can match or even outperform previous state-of-the-art methods while enjoying superior parameter and inference efficiency.
Towards reporting bias in visual-language datasets: bimodal augmentation by decoupling object-attribute association
Authors: Qiyu Wu, Mengjie Zhao, Yutong He, Lang Huang, Junya Ono, Hiromi Wakaki, Yuki Mitsufuji
Date: 2023-10-02 00:00:00
Tasks: Attribute, Object

Reporting bias arises when people assume that some knowledge is universally understood and hence, do not necessitate explicit elaboration. In this paper, we focus on the wide existence of reporting bias in visual-language datasets, embodied as the object-attribute association, which can subsequentially degrade models trained on them. To mitigate this bias, we propose a bimodal augmentation (BiAug) approach through object-attribute decoupling to flexibly synthesize visual-language examples with a rich array of object-attribute pairing and construct cross-modal hard negatives. We employ large language models (LLMs) in conjunction with a grounding object detector to extract target objects. Subsequently, the LLM generates a detailed attribute description for each object and produces a corresponding hard negative counterpart. An inpainting model is then used to create images based on these detailed object descriptions. By doing so, the synthesized examples explicitly complement omitted objects and attributes to learn, and the hard negative pairs steer the model to distinguish object attributes. Our experiments demonstrated that BiAug is superior in object-attribute understanding. In addition, BiAug also improves the performance on zero-shot retrieval tasks on general benchmarks like MSCOCO and Flickr30K. BiAug refines the way of collecting text-image datasets. Mitigating the reporting bias helps models achieve a deeper understanding of visual-language phenomena, expanding beyond mere frequent patterns to encompass the richness and diversity of real-world scenarios.
Streaming Motion Forecasting for Autonomous Driving
Authors: Ziqi Pang, Deva Ramanan, Mengtian Li, Yu-Xiong Wang
Date: 2023-10-02 00:00:00
Tasks: Autonomous Driving, Autonomous Navigation, Motion Forecasting, Trajectory Forecasting

Trajectory forecasting is a widely-studied problem for autonomous navigation. However, existing benchmarks evaluate forecasting based on independent snapshots of trajectories, which are not representative of real-world applications that operate on a continuous stream of data. To bridge this gap, we introduce a benchmark that continuously queries future trajectories on streaming data and we refer to it as "streaming forecasting." Our benchmark inherently captures the disappearance and re-appearance of agents, presenting the emergent challenge of forecasting for occluded agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks. Moreover, forecasting in the context of continuous timestamps naturally asks for temporal coherence between predictions from adjacent timestamps. Based on this benchmark, we further provide solutions and analysis for streaming forecasting. We propose a plug-and-play meta-algorithm called "Predictive Streamer" that can adapt any snapshot-based forecaster into a streaming forecaster. Our algorithm estimates the states of occluded agents by propagating their positions with multi-modal trajectories, and leverages differentiable filters to ensure temporal consistency. Both occlusion reasoning and temporal coherence strategies significantly improve forecasting quality, resulting in 25% smaller endpoint errors for occluded agents and 10-20% smaller fluctuations of trajectories. Our work is intended to generate interest within the community by highlighting the importance of addressing motion forecasting in its intrinsic streaming setting. Code is available at https://github.com/ziqipang/StreamingForecasting.
Less is More: Toward Zero-Shot Local Scene Graph Generation via Foundation Models
Authors: Shu Zhao, Huijuan Xu
Date: 2023-10-02 00:00:00
Tasks: Graph Generation, Scene Graph Generation

Humans inherently recognize objects via selective visual perception, transform specific regions from the visual field into structured symbolic knowledge, and reason their relationships among regions based on the allocation of limited attention resources in line with humans' goals. While it is intuitive for humans, contemporary perception systems falter in extracting structural information due to the intricate cognitive abilities and commonsense knowledge required. To fill this gap, we present a new task called Local Scene Graph Generation. Distinct from the conventional scene graph generation task, which encompasses generating all objects and relationships in an image, our proposed task aims to abstract pertinent structural information with partial objects and their relationships for boosting downstream tasks that demand advanced comprehension and reasoning capabilities. Correspondingly, we introduce zEro-shot Local scEne GrAph geNeraTion (ELEGANT), a framework harnessing foundation models renowned for their powerful perception and commonsense reasoning, where collaboration and information communication among foundation models yield superior outcomes and realize zero-shot local scene graph generation without requiring labeled supervision. Furthermore, we propose a novel open-ended evaluation metric, Entity-level CLIPScorE (ECLIPSE), surpassing previous closed-set evaluation metrics by transcending their limited label space, offering a broader assessment. Experiment results show that our approach markedly outperforms baselines in the open-ended evaluation setting, and it also achieves a significant performance boost of up to 24.58% over prior methods in the close-set setting, demonstrating the effectiveness and powerful reasoning ability of our proposed framework.
NEUCORE: Neural Concept Reasoning for Composed Image Retrieval
Authors: Shu Zhao, Huijuan Xu
Date: 2023-10-02 00:00:00
Tasks: Concept Alignment, Image Retrieval, Multiple Instance Learning, Retrieval, Sentence

Composed image retrieval which combines a reference image and a text modifier to identify the desired target image is a challenging task, and requires the model to comprehend both vision and language modalities and their interactions. Existing approaches focus on holistic multi-modal interaction modeling, and ignore the composed and complimentary property between the reference image and text modifier. In order to better utilize the complementarity of multi-modal inputs for effective information fusion and retrieval, we move the multi-modal understanding to fine-granularity at concept-level, and learn the multi-modal concept alignment to identify the visual location in reference or target images corresponding to text modifier. Toward the end, we propose a NEUral COncept REasoning (NEUCORE) model which incorporates multi-modal concept alignment and progressive multimodal fusion over aligned concepts. Specifically, considering that text modifier may refer to semantic concepts not existing in the reference image and requiring to be added into the target image, we learn the multi-modal concept alignment between the text modifier and the concatenation of reference and target images, under multiple-instance learning framework with image and sentence level weak supervision. Furthermore, based on aligned concepts, to form discriminative fusion features of the input modalities for accurate target image retrieval, we propose a progressive fusion strategy with unified execution architecture instantiated by the attended language semantic concepts. Our proposed approach is evaluated on three datasets and achieves state-of-the-art results.
DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection
Authors: Shilin Xu, Xiangtai Li, Size Wu, Wenwei Zhang, Yunhai Tong, Chen Change Loy
Date: 2023-10-02 00:00:00
Tasks: Novel Object Detection, Object, object-detection, Object Detection, Open-vocabulary object detection, Open Vocabulary Object Detection, Pseudo Label, Region Proposal, zero-shot-classification, Zero-Shot Learning

Open-vocabulary object detection (OVOD) aims to detect the objects beyond the set of classes observed during training. This work introduces a straightforward and efficient strategy that utilizes pre-trained vision-language models (VLM), like CLIP, to identify potential novel classes through zero-shot classification. Previous methods use a class-agnostic region proposal network to detect object proposals and consider the proposals that do not match the ground truth as background. Unlike these methods, our method will select a subset of proposals that will be considered as background during the training. Then, we treat them as novel classes during training. We refer to this approach as the self-training strategy, which enhances recall and accuracy for novel classes without requiring extra annotations, datasets, and re-training. Compared to previous pseudo methods, our approach does not require re-training and offline labeling processing, which is more efficient and effective in one-shot training. Empirical evaluations on three datasets, including LVIS, V3Det, and COCO, demonstrate significant improvements over the baseline performance without incurring additional parameters or computational costs during inference. In addition, we also apply our method to various baselines. In particular, compared with the previous method, F-VLM, our method achieves a 1.7% improvement on the LVIS dataset. Combined with the recent method CLIPSelf, our method also achieves 46.7 novel class AP on COCO without introducing extra data for pertaining. We also achieve over 6.5% improvement over the F-VLM baseline in the recent challenging V3Det dataset. We release our code and models at https://github.com/xushilin1/dst-det.