GenAI4ELab/papercli-papers huggingface dataset:
Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation
Authors: Pengcheng Xu; Boyu Wang; Charles Ling
Venue: AAAI 2023 (main)

Abstract: Current methods of blended targets domain adaptation (BTDA) usually infer or consider domain label information but underemphasize hybrid categorical feature structures of targets, which yields limited performance, especially under the label distribution shift. We demonstrate that domain labels are not directly necessary for BTDA if categorical distributions of various domains are sufficiently aligned even facing the imbalance of domains and the label distribution shift of classes. However, we observe that the cluster assumption in BTDA does not comprehensively hold. The hybrid categorical feature space hinders the modeling of categorical distributions and the generation of reliable pseudo labels for categorical alignment. To address these, we propose a categorical domain discriminator guided by uncertainty to explicitly model and directly align categorical distributions P(Z|Y). Simultaneously, we utilize the low-level features to augment the single source features with diverse target styles to rectify the biased classifier P(Y|Z) among diverse targets. Such a mutual conditional alignment of P(Z|Y) and P(Y|Z) forms a mutual reinforced mechanism. Our approach outperforms the state-of-the-art in BTDA even compared with methods utilizing domain labels, especially under the label distribution shift, and in single target DA on DomainNet.
Self Correspondence Distillation for End-to-End Weakly-Supervised Semantic Segmentation
Authors: Rongtao Xu; Changwei Wang; Jiaxi Sun; Shibiao Xu; Weiliang Meng; Xiaopeng Zhang
Venue: AAAI 2023 (main)

Abstract: Efficiently training accurate deep models for weakly supervised semantic segmentation (WSSS) with image-level labels is challenging and important. Recently, end-to-end WSSS methods have become the focus of research due to their high training efficiency. However, current methods suffer from insufficient extraction of comprehensive semantic information, resulting in low-quality pseudo-labels and sub-optimal solutions for end-to-end WSSS. To this end, we propose a simple and novel Self Correspondence Distillation (SCD) method to refine pseudo-labels without introducing external supervision. Our SCD enables the network to utilize feature correspondence derived from itself as a distillation target, which can enhance the network's feature learning process by complementing semantic information. In addition, to further improve the segmentation accuracy, we design a Variation-aware Refine Module to enhance the local consistency of pseudo-labels by computing pixel-level variation. Finally, we present an efficient end-to-end Transformer-based framework (TSCD) via SCD and Variation-aware Refine Module for the accurate WSSS task. Extensive experiments on the PASCAL VOC 2012 and MS COCO 2014 datasets demonstrate that our method significantly outperforms other state-of-the-art methods.  Our code is available at https://github.com/Rongtao-Xu/RepresentationLearning/tree/main/SCD-AAAI2023.
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution
Authors: Xiaogang Xu; Ruixing Wang; Chi-Wing Fu; Jiaya Jia
Venue: AAAI 2023 (main)

Abstract: Despite the quality improvement brought by the recent methods, video super-resolution (SR) is still very challenging, especially for videos that are low-light and noisy. The current best solution is to subsequently employ best models of video SR, denoising, and illumination enhancement, but doing so often lowers the image quality, due to the inconsistency between the models. This paper presents a new parametric representation called the Deep Parametric 3D Filters (DP3DF), which incorporates local spatiotemporal information to enable simultaneous denoising, illumination enhancement, and SR efficiently in a single encoder-and-decoder network. Also, a dynamic residual frame is jointly learned with the DP3DF via a shared backbone to further boost the SR quality. We performed extensive experiments, including a large-scale user study, to show our method's effectiveness. Our method consistently surpasses the best state-of-the-art methods on all the challenging real datasets with top PSNR and user ratings, yet having a very fast run time. The code is available at https://github.com/xiaogang00/DP3DF.
Inter-image Contrastive Consistency for Multi-Person Pose Estimation
Authors: Xixia Xu; Yingguo Gao; Xingjia Pan; Ke Yan; Xiaoyu Chen; Qi Zou
Venue: AAAI 2023 (main)

Abstract: Multi-person pose estimation (MPPE) has achieved impressive progress in recent years. However, due to the large variance of appearances among images or occlusions, the model can hardly learn consistent patterns enough, which leads to severe location jitter and missing issues. In this study, we propose a novel framework, termed Inter-image Contrastive consistency (ICON), to strengthen the keypoint consistency among images for MPPE. Concretely, we consider two-fold consistency constraints, which include single keypoint contrastive consistency (SKCC) and pair relation contrastive consistency (PRCC). The SKCC learns to strengthen the consistency of individual keypoints across images in the same category to improve the category-specific robustness. Only with SKCC, the model can effectively reduce location errors caused by large appearance variations, but remains challenging with extreme postures (e.g., occlusions) due to lack of relational guidance. Therefore, PRCC is proposed to strengthen the consistency of pair-wise joint relation between images to preserve the instructive relation. Cooperating with SKCC, PRCC further improves structure aware robustness in handling extreme postures. Extensive experiments on kinds of architectures across three datasets (i.e., MS-COCO, MPII, CrowdPose) show the proposed ICON achieves substantial improvements over baselines. Furthermore, ICON under the semi-supervised setup can obtain comparable results with the fully-supervised methods using only 30% labeled data.
DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense Prediction
Authors: Yangyang Xu; Yibo Yang; Lefei Zhang
Venue: AAAI 2023 (main)

Abstract: Convolution neural networks (CNNs) and Transformers have their own advantages and both have been widely used for dense prediction in multi-task learning (MTL). Most of the current studies on MTL solely rely on CNN or Transformer. In this work, we present a novel MTL model by combining both merits of deformable CNN and query-based Transformer for multi-task learning of dense prediction. Our method, named DeMT, is based on a simple and effective encoder-decoder architecture (i.e., deformable mixer encoder and task-aware transformer decoder). First, the deformable mixer encoder contains two types of operators: the channel-aware mixing operator leveraged to allow communication among different channels (i.e., efficient channel location mixing), and the spatial-aware deformable operator with deformable convolution applied to efficiently sample more informative spatial locations (i.e., deformed features). Second, the task-aware transformer decoder consists of the task interaction block and task query block. The former is applied to capture task interaction features via self-attention. The latter leverages the deformed features and task-interacted features to generate the corresponding task-specific feature through a query-based Transformer for corresponding task predictions. Extensive experiments on two dense image prediction datasets, NYUD-v2 and PASCAL-Context, demonstrate that our model uses fewer GFLOPs and significantly outperforms current Transformer- and CNN-based competitive models on a variety of metrics. The code is available at https://github.com/yangyangxu0/DeMT.
VLTinT: Visual-Linguistic Transformer-in-Transformer for Coherent Video Paragraph Captioning
Authors: Kashu Yamazaki; Khoa Vo; Quang Sang Truong; Bhiksha Raj; Ngan Le
Venue: AAAI 2023 (main)

Abstract: Video Paragraph Captioning aims to generate a multi-sentence description of an untrimmed video with multiple temporal event locations in a coherent storytelling. 
Following the human perception process, where the scene is effectively understood by decomposing it into visual (e.g. human, animal) and non-visual components (e.g. action, relations) under the mutual influence of vision and language, we first propose a visual-linguistic (VL) feature. In the proposed VL feature, the scene is modeled by three modalities including (i) a global visual environment; (ii) local visual main agents; (iii) linguistic scene elements. We then introduce an autoregressive Transformer-in-Transformer (TinT) to simultaneously capture the semantic coherence of intra- and inter-event contents within a video. Finally, we present a new VL contrastive loss function to guarantee the learnt embedding features are consistent with the captions semantics. Comprehensive experiments and extensive ablation studies on the ActivityNet Captions and YouCookII datasets show that the proposed Visual-Linguistic Transformer-in-Transform (VLTinT) outperforms previous state-of-the-art methods in terms of accuracy and diversity. The source code is made publicly available at: https://github.com/UARK-AICV/VLTinT.
Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity
Authors: Qingsong Yan; Qiang Wang; Kaiyong Zhao; Bo Li; Xiaowen Chu; Fei Deng
Venue: AAAI 2023 (main)

Abstract: Existing learning-based multi-view stereo (MVS) methods rely on the depth range to build the 3D cost volume and may fail when the range is too large or unreliable. To address this problem, we propose a disparity-based MVS method based on the epipolar disparity flow (E-flow), called DispMVS, which infers the depth information from the pixel movement between two views. The core of DispMVS is to construct a 2D cost volume on the image plane along the epipolar line between each pair (between the reference image and several source images) for pixel matching and fuse uncountable depths triangulated from each pair by multi-view geometry to ensure multi-view consistency. To be robust, DispMVS starts from a randomly initialized depth map and iteratively refines the depth map with the help of the coarse-to-fine strategy. Experiments on DTUMVS and Tanks\&Temple datasets show that DispMVS is not sensitive to the depth range and achieves state-of-the-art results with lower GPU memory.
Video-Text Pre-training with Learned Regions for Retrieval
Authors: Rui Yan; Mike Zheng Shou; Yixiao Ge; Jinpeng Wang; Xudong Lin; Guanyu Cai; Jinhui Tang
Venue: AAAI 2023 (main)

Abstract: Video-Text pre-training aims at learning transferable representations from large-scale video-text pairs via aligning the semantics between visual and textual information. State-of-the-art approaches extract visual features from raw pixels in an end-to-end fashion. However, these methods operate at frame-level directly and thus overlook the spatio-temporal structure of objects in video, which yet has a strong synergy with nouns in textual descriptions. In this work, we propose a simple yet effective module for video-text representation learning, namely RegionLearner, which can take into account the structure of objects during pre-training on large-scale video-text pairs. Given a video, our module (1) first quantizes continuous visual features via clustering patch-features into the same cluster according to content similarity, then (2) generates learnable masks to aggregate fragmentary features into regions with complete semantics, and finally (3) models the spatio-temporal dependencies between different semantic regions. In contrast to using off-the-shelf object detectors, our proposed module does not require explicit supervision and is much more computationally efficient. We pre-train the proposed approach on the public WebVid2M and CC3M datasets. Extensive evaluations on four downstream video-text retrieval benchmarks clearly demonstrate the effectiveness of our RegionLearner.
DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion
Authors: Zhiqiang Yan; Kun Wang; Xiang Li; Zhenyu Zhang; Jun Li; Jian Yang
Venue: AAAI 2023 (main)

Abstract: Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation. Although depth measurement obtained from LiDAR is usually sparse, it contains valid and real distance information, i.e., scale-consistent absolute depth values. Meanwhile, scale-agnostic counterparts seek to estimate relative depth and have achieved impressive performance. To leverage both the inherent characteristics, we thus suggest to model scale-consistent depth upon unsupervised scale-agnostic frameworks. Specifically, we propose the decomposed scale-consistent learning (DSCL) strategy, which disintegrates the absolute depth into relative depth prediction and global scale estimation, contributing to individual learning benefits. But unfortunately, most existing unsupervised scale-agnostic frameworks heavily suffer from depth holes due to the extremely sparse depth input and weak supervisory signal. To tackle this issue, we introduce the global depth guidance (GDG) module, which attentively propagates dense depth reference into the sparse target via novel dense-to-sparse attention. Extensive experiments show the superiority of our method on outdoor KITTI, ranking 1st and outperforming the best KBNet more than 12% in RMSE. Additionally, our approach achieves state-of-the-art performance on indoor NYUv2 benchmark as well.
Self-Supervised Video Representation Learning via Latent Time Navigation
Authors: Di Yang; Yaohui Wang; Quan Kong; Antitza Dantcheva; Lorenzo Garattoni; Gianpiero Francesca; François Brémond
Venue: AAAI 2023 (main)

Abstract: Self-supervised video representation learning aimed at maximizing similarity between different temporal segments of one video, in order to enforce feature persistence over time. This leads to loss of pertinent information related to temporal relationships, rendering actions such as `enter' and `leave' to be indistinguishable. To mitigate this limitation, we propose Latent Time Navigation (LTN), a time parameterized contrastive learning strategy that is streamlined to capture fine-grained motions. Specifically, we maximize the representation similarity between different video segments from one video, while maintaining their representations time-aware along a subspace of the latent representation code including an orthogonal basis to represent temporal changes. Our extensive experimental analysis suggests that learning video representations by LTN consistently improves performance of action classification in fine-grained and human-oriented tasks (e.g., on Toyota Smarthome dataset). In addition, we demonstrate that our proposed model, when pre-trained on Kinetics-400, generalizes well onto the unseen real world video benchmark datasets UCF101 and HMDB51, achieving state-of-the-art performance in action recognition.
One-Shot Replay: Boosting Incremental Object Detection via Retrospecting One Object
Authors: Dongbao Yang; Yu Zhou; Xiaopeng Hong; Aoting Zhang; Weiping Wang
Venue: AAAI 2023 (main)

Abstract: Modern object detectors are ill-equipped to incrementally learn new emerging object classes over time due to the well-known phenomenon of catastrophic forgetting. Due to data privacy or limited storage, few or no images of the old data can be stored for replay. In this paper, we design a novel One-Shot Replay (OSR) method for incremental object detection, which is an augmentation-based method. Rather than storing original images, only one object-level sample for each old class is stored to reduce memory usage significantly, and we find that copy-paste is a harmonious way to replay for incremental object detection. In the incremental learning procedure, diverse augmented samples with co-occurrence of old and new objects to existing training data are generated. To introduce more variants for objects of old classes, we propose two augmentation modules. The object augmentation module aims to enhance the ability of the detector to perceive potential unknown objects. The feature augmentation module explores the relations between old and new classes and augments the feature space via analogy. Extensive experimental results on VOC2007 and COCO demonstrate that OSR can outperform the state-of-the-art incremental object detection methods without using extra wild data.
Video Event Extraction via Tracking Visual States of Arguments
Authors: Guang Yang; Manling Li; Jiajie Zhang; Xudong Lin; Heng Ji; Shih-Fu Chang
Venue: AAAI 2023 (main)

Abstract: Video event extraction aims to detect salient events from a video and identify the arguments for each event as well as their semantic roles. Existing methods focus on capturing the overall visual scene of each frame, ignoring fine-grained argument-level information. Inspired by the definition of events as changes of  states, we propose a novel framework to detect video events by tracking the changes in the visual states of all involved arguments, which are expected to provide the most informative evidence for the extraction of video events. In order to capture the visual state changes of arguments, we decompose them into changes in pixels within objects, displacements of objects, and interactions among multiple arguments. We further propose Object State Embedding, Object Motion-aware Embedding and Argument Interaction Embedding to encode and track these changes respectively. Experiments on various video event extraction tasks demonstrate significant improvements compared to state-of-the-art models. In particular, on verb classification, we achieve 3.49% absolute gains (19.53% relative gains) in F1@5 on Video Situation Recognition. Our Code is publicly available at https://github.com/Shinetism/VStates for research purposes.
CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets
Authors: Jiange Yang; Sheng Guo; Gangshan Wu; Limin Wang
Venue: AAAI 2023 (main)

Abstract: Current RGB-D scene recognition approaches often train two standalone backbones for RGB and depth modalities with the same Places or ImageNet pre-training. However, the pre-trained depth network is still biased by RGB-based models which may result in a suboptimal solution. In this paper, we present a single-model self-supervised hybrid pre-training framework for RGB and depth modalities, termed as CoMAE. Our CoMAE presents a curriculum learning strategy to unify the two popular self-supervised representation learning algorithms: contrastive learning and masked image modeling.  Specifically, we first build a patch-level alignment task to pre-train a single encoder shared by two modalities via cross-modal contrastive learning. Then, the pre-trained contrastive encoder is passed to a multi-modal masked autoencoder to capture the finer context features from a generative perspective.  In addition, our single-model design without requirement of fusion module is very flexible and robust to generalize to unimodal scenario in both training and testing phases.  Extensive experiments on SUN RGB-D and NYUDv2 datasets demonstrate the effectiveness of our CoMAE for RGB and depth representation learning.  In addition, our experiment results reveal that CoMAE is a data-efficient representation learner. Although we only use the small-scale and unlabeled training set for pre-training, our CoMAE pre-trained models are still competitive to the state-of-the-art methods with extra large-scale and supervised RGB dataset pre-training. Code will be released at https://github.com/MCG-NJU/CoMAE.
Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling
Authors: Jinhai Yang; Mengxi Guo; Shijie Zhao; Junlin Li; Li Zhang
Venue: AAAI 2023 (main)

Abstract: High-resolution (HR) images are usually downscaled to low-resolution (LR) ones for better display and afterward upscaled back to the original size to recover details. Recent work in image rescaling formulates downscaling and upscaling as a unified task and learns a bijective mapping between HR and LR via invertible networks. However, in real-world applications (e.g., social media), most images are compressed for transmission. Lossy compression will lead to irreversible information loss on LR images, hence damaging the inverse upscaling procedure and degrading the reconstruction accuracy. In this paper, we propose the Self-Asymmetric Invertible Network (SAIN) for compression-aware image rescaling. To tackle the distribution shift, we first develop an end-to-end asymmetric framework with two separate bijective mappings for high-quality and compressed LR images, respectively. Then, based on empirical analysis of this framework, we model the distribution of the lost information (including downscaling and compression) using isotropic Gaussian mixtures and propose the Enhanced Invertible Block to derive high-quality/compressed LR images in one forward pass. Besides, we design a set of losses to regularize the learned LR images and enhance the invertibility. Extensive experiments demonstrate the consistent improvements of SAIN across various image rescaling datasets in terms of both quantitative and qualitative evaluation under standard image compression formats (i.e., JPEG and WebP). Code is available at https://github.com/yang-jin-hai/SAIN.
Stop-Gradient Softmax Loss for Deep Metric Learning
Authors: Lu Yang; Peng Wang; Yanning Zhang
Venue: AAAI 2023 (main)

Abstract: Deep metric learning aims to learn a feature space that models the similarity between images, and feature normalization is a critical step for boosting performance. However directly optimizing L2-normalized softmax loss cause the network to fail to converge. Therefore some SOTA approaches appends a scale layer after the inner product to relieve the convergence problem, but it incurs a new problem that it's difficult to learn the best scaling parameters. In this letter, we look into the characteristic of softmax-based approaches and propose a novel learning objective function Stop-Gradient Softmax Loss (SGSL) to solve the convergence problem in softmax-based deep metric learning with L2-normalization. In addition, we found a useful trick named Remove the last BN-ReLU (RBR). It removes the last BN-ReLU in the backbone to reduce the learning burden of the model.
Experimental results on four fine-grained image retrieval benchmarks show that our proposed approach outperforms most existing approaches, i.e., our approach achieves 75.9% on CUB-200-2011, 94.7% on CARS196 and 83.1% on SOP which outperforms other approaches at least 1.7%, 2.9% and 1.7% on Recall@1.
Local Path Integration for Attribution
Authors: Peiyu Yang; Naveed Akhtar; Zeyi Wen; Ajmal Mian
Venue: AAAI 2023 (main)

Abstract: Path attribution methods are a popular tool to interpret a visual model's prediction on an input. They integrate model gradients for the input features over a path defined between the input and a reference, thereby satisfying certain desirable theoretical properties. However, their reliability hinges on the choice of the reference. Moreover, they do not exhibit weak dependence on the input, which leads to counter-intuitive feature attribution mapping. We show that path-based attribution can account for the weak dependence property by choosing the reference from the local distribution of the input. We devise a method to identify the local input distribution and propose a technique to stochastically integrate the model gradients over the paths defined by the references sampled from that distribution. Our local path integration (LPI) method is found to consistently outperform existing path attribution techniques when evaluated on deep visual models. Contributing to the ongoing search of reliable evaluation metrics for the interpretation methods, we also introduce DiffID metric that uses the relative difference between insertion and deletion games to alleviate the distribution shift problem faced by existing metrics. Our code is available at https://github.com/ypeiyu/LPI.