GenAI4ELab/papercli-papers huggingface dataset:
Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning
Authors: Antoine Yang; Arsha Nagrani; Paul Hongsuck Seo; Antoine Miech; Jordi Pont-Tuset; Ivan Laptev; Josef Sivic; Cordelia Schmid
Venue: CVPR 2023 (main)

Abstract: 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 https://antoyang.github.io/vid2seq.html.
Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection
Authors: Xiaolin Song; Binghui Chen; Pengyu Li; Jun-Yan He; Biao Wang; Yifeng Geng; Xuansong Xie; Honggang Zhang
Venue: CVPR 2023 (main)

Abstract: End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing. Though a few methods have been explored, most of them still suffer from longer training time and more complex deployment, which cannot be deployed in the actual industrial applications. In this paper, we intend to bridge this gap and propose an Optimal Proposal Learning (OPL) framework for deployable end-to-end pedestrian detection. Specifically, we achieve this goal by using CNN-based light detector and introducing two novel modules, including a Coarse-to-Fine (C2F) learning strategy for proposing precise positive proposals for the Ground-Truth (GT) instances by reducing the ambiguity of sample assignment/output in training/testing respectively, and a Completed Proposal Network (CPN) for producing extra information compensation to further recall the hard pedestrian samples. Extensive experiments are conducted on CrowdHuman, TJU-Ped and Caltech, and the results show that our proposed OPL method significantly outperforms the competing methods.
Discovering the Real Association: Multimodal Causal Reasoning in Video Question Answering
Authors: Chuanqi Zang; Hanqing Wang; Mingtao Pei; Wei Liang
Venue: CVPR 2023 (main)

Abstract: Video Question Answering (VideoQA) is challenging as it requires capturing accurate correlations between modalities from redundant information. Recent methods focus on the explicit challenges of the task, e.g. multimodal feature extraction, video-text alignment and fusion. Their frameworks reason the answer relying on statistical evidence causes, which ignores potential bias in the multimodal data. In our work, we investigate relational structure from a causal representation perspective on multimodal data and propose a novel inference framework. For visual data, question-irrelevant objects may establish simple matching associations with the answer. For textual data, the model prefers the local phrase semantics which may deviate from the global semantics in long sentences. Therefore, to enhance the generalization of the model, we discover the real association by explicitly capturing visual features that are causally related to the question semantics and weakening the impact of local language semantics on question answering. The experimental results on two large causal VideoQA datasets verify that our proposed framework 1) improves the accuracy of the existing VideoQA backbone, 2) demonstrates robustness on complex scenes and questions.
Temporal Interpolation Is All You Need for Dynamic Neural Radiance Fields
Authors: Sungheon Park; Minjung Son; Seokhwan Jang; Young Chun Ahn; Ji-Yeon Kim; Nahyup Kang
Venue: CVPR 2023 (main)

Abstract: Temporal interpolation often plays a crucial role to learn meaningful representations in dynamic scenes. In this paper, we propose a novel method to train spatiotemporal neural radiance fields of dynamic scenes based on temporal interpolation of feature vectors. Two feature interpolation methods are suggested depending on underlying representations, neural networks or grids. In the neural representation, we extract features from space-time inputs via multiple neural network modules and interpolate them based on time frames. The proposed multi-level feature interpolation network effectively captures features of both short-term and long-term time ranges. In the grid representation, space-time features are learned via four-dimensional hash grids, which remarkably reduces training time. The grid representation shows more than 100 times faster training speed than the previous neural-net-based methods while maintaining the rendering quality. Concatenating static and dynamic features and adding a simple smoothness term further improve the performance of our proposed models. Despite the simplicity of the model architectures, our method achieved state-of-the-art performance both in rendering quality for the neural representation and in training speed for the grid representation.
Graph Transformer GANs for Graph-Constrained House Generation
Authors: Hao Tang; Zhenyu Zhang; Humphrey Shi; Bo Li; Ling Shao; Nicu Sebe; Radu Timofte; Luc Van Gool
Venue: CVPR 2023 (main)

Abstract: We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolutions and self-attentions in a Transformer to model both local and global interactions across connected and non-connected graph nodes. Specifically, the proposed connected node attention (CNA) and non-connected node attention (NNA) aim to capture the global relations across connected nodes and non-connected nodes in the input graph, respectively. The proposed graph modeling block (GMB) aims to exploit local vertex interactions based on a house layout topology. Moreover, we propose a new node classification-based discriminator to preserve the high-level semantic and discriminative node features for different house components. Finally, we propose a novel graph-based cycle-consistency loss that aims at maintaining the relative spatial relationships between ground truth and predicted graphs. Experiments on two challenging graph-constrained house generation tasks (i.e., house layout and roof generation) with two public datasets demonstrate the effectiveness of GTGAN in terms of objective quantitative scores and subjective visual realism. New state-of-the-art results are established by large margins on both tasks.
On the Benefits of 3D Pose and Tracking for Human Action Recognition
Authors: Jathushan Rajasegaran; Georgios Pavlakos; Angjoo Kanazawa; Christoph Feichtenhofer; Jitendra Malik
Venue: CVPR 2023 (main)

Abstract: In this work we study the benefits of using tracking and 3D poses for action recognition. To achieve this, we take the Lagrangian view on analysing actions over a trajectory of human motion rather than at a fixed point in space. Taking this stand allows us to use the tracklets of people to predict their actions. In this spirit, first we show the benefits of using 3D pose to infer actions, and study person-person interactions. Subsequently, we propose a Lagrangian Action Recognition model by fusing 3D pose and contextualized appearance over tracklets. To this end, our method achieves state-of-the-art performance on the AVA v2.2 dataset on both pose only settings and on standard benchmark settings. When reasoning about the action using only pose cues, our pose model achieves +10.0 mAP gain over the corresponding state-of-the-art while our fused model has a gain of +2.8 mAP over the best state-of-the-art model. Code and results are available at: https://brjathu.github.io/LART
How to Backdoor Diffusion Models?
Authors: Sheng-Yen Chou; Pin-Yu Chen; Tsung-Yi Ho
Venue: CVPR 2023 (main)

Abstract: Diffusion models are state-of-the-art deep learning empowered generative models that are trained based on the principle of learning forward and reverse diffusion processes via progressive noise-addition and denoising. To gain a better understanding of the limitations and potential risks, this paper presents the first study on the robustness of diffusion models against backdoor attacks. Specifically, we propose BadDiffusion, a novel attack framework that engineers compromised diffusion processes during model training for backdoor implantation. At the inference stage, the backdoored diffusion model will behave just like an untampered generator for regular data inputs, while falsely generating some targeted outcome designed by the bad actor upon receiving the implanted trigger signal. Such a critical risk can be dreadful for downstream tasks and applications built upon the problematic model. Our extensive experiments on various backdoor attack settings show that BadDiffusion can consistently lead to compromised diffusion models with high utility and target specificity. Even worse, BadDiffusion can be made cost-effective by simply finetuning a clean pre-trained diffusion model to implant backdoors. We also explore some possible countermeasures for risk mitigation. Our results call attention to potential risks and possible misuse of diffusion models.
ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model With Knowledge-Enhanced Mixture-of-Denoising-Experts
Authors: Zhida Feng; Zhenyu Zhang; Xintong Yu; Yewei Fang; Lanxin Li; Xuyi Chen; Yuxiang Lu; Jiaxiang Liu; Weichong Yin; Shikun Feng; Yu Sun; Li Chen; Hao Tian; Hua Wu; Haifeng Wang
Venue: CVPR 2023 (main)

Abstract: Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution images with text conditions, there are still several open problems to be solved, which limits the further improvement of image fidelity and text relevancy. In this paper, we propose ERNIE-ViLG 2.0, a large-scale Chinese text-to-image diffusion model, to progressively upgrade the quality of generated images by: (1) incorporating fine-grained textual and visual knowledge of key elements in the scene, and (2) utilizing different denoising experts at different denoising stages. With the proposed mechanisms, ERNIE-ViLG 2.0 not only achieves a new state-of-the-art on MS-COCO with zero-shot FID-30k score of 6.75, but also significantly outperforms recent models in terms of image fidelity and image-text alignment, with side-by-side human evaluation on the bilingual prompt set ViLG-300.
PACO: Parts and Attributes of Common Objects
Authors: Vignesh Ramanathan; Anmol Kalia; Vladan Petrovic; Yi Wen; Baixue Zheng; Baishan Guo; Rui Wang; Aaron Marquez; Rama Kovvuri; Abhishek Kadian; Amir Mousavi; Yiwen Song; Abhimanyu Dubey; Dhruv Mahajan
Venue: CVPR 2023 (main)

Abstract: Object models are gradually progressing from predicting just category labels to providing detailed descriptions of object instances. This motivates the need for large datasets which go beyond traditional object masks and provide richer annotations such as part masks and attributes. Hence, we introduce PACO: Parts and Attributes of Common Objects. It spans 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Ego4D) datasets. We provide 641K part masks annotated across 260K object boxes, with roughly half of them exhaustively annotated with attributes as well. We design evaluation metrics and provide benchmark results for three tasks on the dataset: part mask segmentation, object and part attribute prediction and zero-shot instance detection. Dataset, models, and code are open-sourced at https://github.com/facebookresearch/paco.
Learning Transformations To Reduce the Geometric Shift in Object Detection
Authors: Vidit Vidit; Martin Engilberge; Mathieu Salzmann
Venue: CVPR 2023 (main)

Abstract: The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between synthetic and real images. Here, by contrast, we tackle geometric shifts emerging from variations in the image capture process, or due to the constraints of the environment causing differences in the apparent geometry of the content itself. We introduce a self-training approach that learns a set of geometric transformations to minimize these shifts without leveraging any labeled data in the new domain, nor any information about the cameras. We evaluate our method on two different shifts, i.e., a camera's field of view (FoV) change and a viewpoint change. Our results evidence that learning geometric transformations helps detectors to perform better in the target domains.
OReX: Object Reconstruction From Planar Cross-Sections Using Neural Fields
Authors: Haim Sawdayee; Amir Vaxman; Amit H. Bermano
Venue: CVPR 2023 (main)

Abstract: Reconstructing 3D shapes from planar cross-sections is a challenge inspired by downstream applications like medical imaging and geographic informatics. The input is an in/out indicator function fully defined on a sparse collection of planes in space, and the output is an interpolation of the indicator function to the entire volume. Previous works addressing this sparse and ill-posed problem either produce low quality results, or rely on additional priors such as target topology, appearance information, or input normal directions. In this paper, we present OReX, a method for 3D shape reconstruction from slices alone, featuring a Neural Field as the interpolation prior. A modest neural network is trained on the input planes to return an inside/outside estimate for a given 3D coordinate, yielding a powerful prior that induces smoothness and self-similarities. The main challenge for this approach is high-frequency details, as the neural prior is overly smoothing. To alleviate this, we offer an iterative estimation architecture and a hierarchical input sampling scheme that encourage coarse-to-fine training, allowing the training process to focus on high frequencies at later stages. In addition, we identify and analyze a ripple-like effect stemming from the mesh extraction step. We mitigate it by regularizing the spatial gradients of the indicator function around input in/out boundaries during network training, tackling the problem at the root. Through extensive qualitative and quantitative experimentation, we demonstrate our method is robust, accurate, and scales well with the size of the input. We report state-of-the-art results compared to previous approaches and recent potential solutions, and demonstrate the benefit of our individual contributions through analysis and ablation studies.
SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting With Neural Radiance Fields
Authors: Ashkan Mirzaei; Tristan Aumentado-Armstrong; Konstantinos G. Derpanis; Jonathan Kelly; Marcus A. Brubaker; Igor Gilitschenski; Alex Levinshtein
Venue: CVPR 2023 (main)

Abstract: Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel view synthesis. While NeRFs are quickly being adapted for a wider set of applications, intuitively editing NeRF scenes is still an open challenge. One important editing task is the removal of unwanted objects from a 3D scene, such that the replaced region is visually plausible and consistent with its context. We refer to this task as 3D inpainting. In 3D, solutions must be both consistent across multiple views and geometrically valid. In this paper, we propose a novel 3D inpainting method that addresses these challenges. Given a small set of posed images and sparse annotations in a single input image, our framework first rapidly obtains a 3D segmentation mask for a target object. Using the mask, a perceptual optimization-based approach is then introduced that leverages learned 2D image inpainters, distilling their information into 3D space, while ensuring view consistency. We also address the lack of a diverse benchmark for evaluating 3D scene inpainting methods by introducing a dataset comprised of challenging real-world scenes. In particular, our dataset contains views of the same scene with and without a target object, enabling more principled benchmarking of the 3D inpainting task. We first demonstrate the superiority of our approach on multiview segmentation, comparing to NeRF-based methods and 2D segmentation approaches. We then evaluate on the task of 3D inpainting, establishing state-of-the-art performance against other NeRF manipulation algorithms, as well as a strong 2D image inpainter baseline.
Revisiting the Stack-Based Inverse Tone Mapping
Authors: Ning Zhang; Yuyao Ye; Yang Zhao; Ronggang Wang
Venue: CVPR 2023 (main)

Abstract: Current stack-based inverse tone mapping (ITM) methods can recover high dynamic range (HDR) radiance by predicting a set of multi-exposure images from a single low dynamic range image. However, there are still some limitations. On the one hand, these methods estimate a fixed number of images (e.g., three exposure-up and three exposure-down), which may introduce unnecessary computational cost or reconstruct incorrect results. On the other hand, they neglect the connections between the up-exposure and down-exposure models and thus fail to fully excavate effective features. In this paper, we revisit the stack-based ITM approaches and propose a novel method to reconstruct HDR radiance from a single image, which only needs to estimate two exposure images. At first, we design the exposure adaptive block that can adaptively adjust the exposure based on the luminance distribution of the input image. Secondly, we devise the cross-model attention block to connect the exposure adjustment models. Thirdly, we propose an end-to-end ITM pipeline by incorporating the multi-exposure fusion model. Furthermore, we propose and open a multi-exposure dataset that indicates the optimal exposure-up/down levels. Experimental results show that the proposed method outperforms some state-of-the-art methods.
Revisiting Rotation Averaging: Uncertainties and Robust Losses
Authors: Ganlin Zhang; Viktor Larsson; Daniel Barath
Venue: CVPR 2023 (main)

Abstract: In this paper, we revisit the rotation averaging problem applied in global Structure-from-Motion pipelines. We argue that the main problem of current methods is the minimized cost function that is only weakly connected with the input data via the estimated epipolar geometries. We propose to better model the underlying noise distributions by directly propagating the uncertainty from the point correspondences into the rotation averaging. Such uncertainties are obtained for free by considering the Jacobians of two-view refinements. Moreover, we explore integrating a variant of the MAGSAC loss into the rotation averaging problem, instead of using classical robust losses employed in current frameworks. The proposed method leads to results superior to baselines, in terms of accuracy, on large-scale public benchmarks. The code is public. https://github.com/zhangganlin/GlobalSfMpy
Continuous Sign Language Recognition With Correlation Network
Authors: Lianyu Hu; Liqing Gao; Zekang Liu; Wei Feng
Venue: CVPR 2023 (main)

Abstract: Human body trajectories are a salient cue to identify actions in video. Such body trajectories are mainly conveyed by hands and face across consecutive frames in sign language. However, current methods in continuous sign language recognition(CSLR) usually process frames independently to capture frame-wise features, thus failing to capture cross-frame trajectories to effectively identify a sign. To handle this limitation, we propose correlation network (CorrNet) to explicitly leverage body trajectories across frames to identify signs. In specific, an identification module is first presented to emphasize informative regions in each frame that are beneficial in expressing a sign. A correlation module is then proposed to dynamically compute correlation maps between current frame and adjacent neighbors to capture cross-frame trajectories. As a result, the generated features are able to gain an overview of local temporal movements to identify a sign. Thanks to its special attention on body trajectories, CorrNet achieves new state-of-the-art accuracy on four large-scale datasets, PHOENIX14, PHOENIX14-T, CSL-Daily, and CSL. A comprehensive comparison between CorrNet and previous spatial-temporal reasoning methods verifies its effectiveness. Visualizations are given to demonstrate the effects of CorrNet on emphasizing human body trajectories across adjacent frames.
A Simple Framework for Text-Supervised Semantic Segmentation
Authors: Muyang Yi; Quan Cui; Hao Wu; Cheng Yang; Osamu Yoshie; Hongtao Lu
Venue: CVPR 2023 (main)

Abstract: Text-supervised semantic segmentation is a novel research topic that allows semantic segments to emerge with image-text contrasting. However, pioneering methods could be subject to specifically designed network architectures. This paper shows that a vanilla contrastive language-image pre-training (CLIP) model is an effective text-supervised semantic segmentor by itself. First, we reveal that a vanilla CLIP is inferior to localization and segmentation due to its optimization being driven by densely aligning visual and language representations. Second, we propose the locality-driven alignment (LoDA) to address the problem, where CLIP optimization is driven by sparsely aligning local representations. Third, we propose a simple segmentation (SimSeg) framework. LoDA and SimSeg jointly ameliorate a vanilla CLIP to produce impressive semantic segmentation results. Our method outperforms previous state-of-the-art methods on PASCAL VOC 2012, PASCAL Context and COCO datasets by large margins. Code and models are available at github.com/muyangyi/SimSeg.
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection
Authors: Chen Zhang; Guorong Li; Yuankai Qi; Shuhui Wang; Laiyun Qing; Qingming Huang; Ming-Hsuan Yang
Venue: CVPR 2023 (main)

Abstract: Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Recently, two-stage self-training methods have achieved significant improvements by self-generating pseudo labels and self-refining anomaly scores with these labels. As the pseudo labels play a crucial role, we propose an enhancement framework by exploiting completeness and uncertainty properties for effective self-training. Specifically, we first design a multi-head classification module (each head serves as a classifier) with a diversity loss to maximize the distribution differences of predicted pseudo labels across heads. This encourages the generated pseudo labels to cover as many abnormal events as possible. We then devise an iterative uncertainty pseudo label refinement strategy, which improves not only the initial pseudo labels but also the updated ones obtained by the desired classifier in the second stage. Extensive experimental results demonstrate the proposed method performs favorably against state-of-the-art approaches on the UCF-Crime, TAD, and XD-Violence benchmark datasets.