Learning Part Segmentation from Synthetic Animals

Semantic part segmentation provides an intricate and interpretable understanding of an object, thereby benefiting numerous downstream tasks. However, the need for exhaustive annotations impedes its usage across diverse object types. This paper focuses on learning part segmentation from synthetic animals, leveraging the Skinned Multi-Animal Linear (SMAL) models to scale up existing synthetic data generated by computer-aided design (CAD) animal models. Compared to CAD models, SMAL models generate data with a wider range of poses observed in real-world scenarios. As a result, our first contribution is to construct a synthetic animal dataset of tigers and horses with more pose diversity, termed Synthetic Animal Parts (SAP). We then benchmark Syn-to-Real animal part segmentation from SAP to PartImageNet, namely SynRealPart, with existing semantic segmentation domain adaptation methods and further improve them as our second contribution. Concretely, we examine three Syn-to-Real adaptation methods but observe relative performance drop due to the innate difference between the two tasks. To address this, we propose a simple yet effective method called Class-Balanced Fourier Data Mixing (CB-FDM). Fourier Data Mixing aligns the spectral amplitudes of synthetic images with real images, thereby making the mixed images have more similar frequency content to real images. We further use Class-Balanced Pseudo-Label Re-Weighting to alleviate the imbalanced class distribution. We demonstrate the efficacy of CB-FDM on SynRealPart over previous methods with significant performance improvements. Remarkably, our third contribution is to reveal that the learned parts from synthetic tiger and horse are transferable across all quadrupeds in PartImageNet, further underscoring the utility and potential applications of animal part segmentation.

MaXTron: Mask Transformer with Trajectory Attention for Video Panoptic Segmentation

Video panoptic segmentation requires consistently segmenting (for both `thing' and `stuff' classes) and tracking objects in a video over time. In this work, we present MaXTron, a general framework that exploits Mask XFormer with Trajectory Attention to tackle the task. MaXTron enriches an off-the-shelf mask transformer by leveraging trajectory attention. The deployed mask transformer takes as input a short clip consisting of only a few frames and predicts the clip-level segmentation. To enhance the temporal consistency, MaXTron employs within-clip and cross-clip tracking modules, efficiently utilizing trajectory attention. Originally designed for video classification, trajectory attention learns to model the temporal correspondences between neighboring frames and aggregates information along the estimated motion paths. However, it is nontrivial to directly extend trajectory attention to the per-pixel dense prediction tasks due to its quadratic dependency on input size. To alleviate the issue, we propose to adapt the trajectory attention for both the dense pixel features and object queries, aiming to improve the short-term and long-term tracking results, respectively. Particularly, in our within-clip tracking module, we propose axial-trajectory attention that effectively computes the trajectory attention for tracking dense pixels sequentially along the height- and width-axes. The axial decomposition significantly reduces the computational complexity for dense pixel features. In our cross-clip tracking module, since the object queries in mask transformer are learned to encode the object information, we are able to capture the long-term temporal connections by applying trajectory attention to object queries, which learns to track each object across different clips. Without bells and whistles, MaXTron demonstrates state-of-the-art performances on video segmentation benchmarks.

Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP

Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing objects from an open set of categories. One way to address this challenge is to leverage multi-modal models, such as CLIP, to provide image and text features in a shared embedding space, which bridges the gap between closed-vocabulary and open-vocabulary recognition. Hence, existing methods often adopt a two-stage framework to tackle the problem, where the inputs first go through a mask generator and then through the CLIP model along with the predicted masks. This process involves extracting features from images multiple times, which can be ineffective and inefficient. By contrast, we propose to build everything into a single-stage framework using a shared Frozen Convolutional CLIP backbone, which not only significantly simplifies the current two-stage pipeline, but also remarkably yields a better accuracy-cost trade-off. The proposed FC-CLIP, benefits from the following observations: the frozen CLIP backbone maintains the ability of open-vocabulary classification and can also serve as a strong mask generator, and the convolutional CLIP generalizes well to a larger input resolution than the one used during contrastive image-text pretraining. When training on COCO panoptic data only and testing in a zero-shot manner, FC-CLIP achieve 26.8 PQ, 16.8 AP, and 34.1 mIoU on ADE20K, 18.2 PQ, 27.9 mIoU on Mapillary Vistas, 44.0 PQ, 26.8 AP, 56.2 mIoU on Cityscapes, outperforming the prior art by +4.2 PQ, +2.4 AP, +4.2 mIoU on ADE20K, +4.0 PQ on Mapillary Vistas and +20.1 PQ on Cityscapes, respectively. Additionally, the training and testing time of FC-CLIP is 7.5x and 6.6x significantly faster than the same prior art, while using 5.9x fewer parameters. FC-CLIP also sets a new state-of-the-art performance across various open-vocabulary semantic segmentation datasets. Code at https://github.com/bytedance/fc-clip

Compositor: Bottom-up Clustering and Compositing for Robust Part and Object Segmentation

In this work, we present a robust approach for joint part and object segmentation. Specifically, we reformulate object and part segmentation as an optimization problem and build a hierarchical feature representation including pixel, part, and object-level embeddings to solve it in a bottom-up clustering manner. Pixels are grouped into several clusters where the part-level embeddings serve as cluster centers. Afterwards, object masks are obtained by compositing the part proposals. This bottom-up interaction is shown to be effective in integrating information from lower semantic levels to higher semantic levels. Based on that, our novel approach Compositor produces part and object segmentation masks simultaneously while improving the mask quality. Compositor achieves state-of-the-art performance on PartImageNet and Pascal-Part by outperforming previous methods by around 0.9% and 1.3% on PartImageNet, 0.4% and 1.7% on Pascal-Part in terms of part and object mIoU and demonstrates better robustness against occlusion by around 4.4% and 7.1% on part and object respectively. Code will be available at https://github.com/TACJu/Compositor.

Learning from Temporal Gradient for Semi-supervised Action Recognition

Semi-supervised video action recognition tends to enable deep neural networks to achieve remarkable performance even with very limited labeled data. However, existing methods are mainly transferred from current image-based methods (e.g., FixMatch). Without specifically utilizing the temporal dynamics and inherent multimodal attributes, their results could be suboptimal. To better leverage the encoded temporal information in videos, we introduce temporal gradient as an additional modality for more attentive feature extraction in this paper. To be specific, our method explicitly distills the fine-grained motion representations from temporal gradient (TG) and imposes consistency across different modalities (i.e., RGB and TG). The performance of semi-supervised action recognition is significantly improved without additional computation or parameters during inference. Our method achieves the state-of-the-art performance on three video action recognition benchmarks (i.e., Kinetics-400, UCF-101, and HMDB-51) under several typical semi-supervised settings (i.e., different ratios of labeled data).

ROBIN : A Benchmark for Robustness to Individual Nuisances in Real-World Out-of-Distribution Shifts

Enhancing the robustness in real-world scenarios has been proven very challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or they simply measure robustness as generalization between datasets and hence ignore the effects of individual nuisance factors. In this work, we introduce ROBIN, a benchmark dataset for diagnosing the robustness of vision algorithms to individual nuisances in real-world images. ROBIN builds on 10 rigid categories from the PASCAL VOC 2012 and ImageNet datasets and includes out-of-distribution examples of the objects 3D pose, shape, texture, context and weather conditions. ROBIN is richly annotated to enable benchmark models for image classification, object detection, and 3D pose estimation. We provide results for a number of popular baselines and make several interesting observations: 1. Some nuisance factors have a much stronger negative effect on the performance compared to others. Moreover, the negative effect of an OODnuisance depends on the downstream vision task. 2. Current approaches to enhance OOD robustness using strong data augmentation have only marginal effects in real-world OOD scenarios, and sometimes even reduce the OOD performance. 3. We do not observe any significant differences between convolutional and transformer architectures in terms of OOD robustness. We believe our dataset provides a rich testbed to study the OOD robustness of vision algorithms and will help to significantly push forward research in this area.

PartImageNet: A Large, High-Quality Dataset of Parts

A part-based object understanding facilitates efficient compositional learning and knowledge transfer, robustness to occlusion, and has the potential to increase the performance on general recognition and localization tasks. However, research on part-based models is hindered due to the lack of datasets with part annotations, which is caused by the extreme difficulty and high cost of annotating object parts in images. In this paper, we propose PartImageNet, a large, high-quality dataset with part segmentation annotations. It consists of 158 classes from ImageNet with approximately 24000 images. PartImageNet is unique because it offers part-level annotations on a general set of classes with non-rigid, articulated objects, while having an order of magnitude larger size compared to existing datasets. It can be utilized in multiple vision tasks including but not limited to: Part Discovery, Semantic Segmentation, Few-shot Learning. Comprehensive experiments are conducted to set up a set of baselines on PartImageNet and we find that existing works on part discovery can not always produce satisfactory results during complex variations. The exploit of parts on downstream tasks also remains insufficient. We believe that our PartImageNet will greatly facilitate the research on part-based models and their applications. The dataset and scripts will soon be released at https://github.com/TACJu/PartImageNet.

TransMix: Attend to Mix for Vision Transformers

Mixup-based augmentation has been found to be effective for generalizing models during training, especially for Vision Transformers (ViTs) since they can easily overfit. However, previous mixup-based methods have an underlying prior knowledge that the linearly interpolated ratio of targets should be kept the same as the ratio proposed in input interpolation. This may lead to a strange phenomenon that sometimes there is no valid object in the mixed image due to the random process in augmentation but there is still response in the label space. To bridge such gap between the input and label spaces, we propose TransMix, which mixes labels based on the attention maps of Vision Transformers. The confidence of the label will be larger if the corresponding input image is weighted higher by the attention map. TransMix is embarrassingly simple and can be implemented in just a few lines of code without introducing any extra parameters and FLOPs to ViT-based models. Experimental results show that our method can consistently improve various ViT-based models at scales on ImageNet classification. After pre-trained with TransMix on ImageNet, the ViT-based models also demonstrate better transferability to semantic segmentation, object detection and instance segmentation. TransMix also exhibits to be more robust when evaluating on 4 different benchmarks. Code will be made publicly available at https://github.com/Beckschen/TransMix.

Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning

Semi-Supervised Learning (SSL) has shown its strong ability in utilizing unlabeled data when labeled data is scarce. However, most SSL algorithms work under the assumption that the class distributions are balanced in both training and test sets. In this work, we consider the problem of SSL on class-imbalanced data, which better reflects real-world situations but has only received limited attention so far. In particular, we decouple the training of the representation and the classifier, and systematically investigate the effects of different data re-sampling techniques when training the whole network including a classifier as well as fine-tuning the feature extractor only. We find that data re-sampling is of critical importance to learn a good classifier as it increases the accuracy of the pseudo-labels, in particular for the minority classes in the unlabeled data. Interestingly, we find that accurate pseudo-labels do not help when training the feature extractor, rather contrariwise, data re-sampling harms the training of the feature extractor. This finding is against the general intuition that wrong pseudo-labels always harm the model performance in SSL. Based on these findings, we suggest to re-think the current paradigm of having a single data re-sampling strategy and develop a simple yet highly effective Bi-Sampling (BiS) strategy for SSL on class-imbalanced data. BiS implements two different re-sampling strategies for training the feature extractor and the classifier and integrates this decoupled training into an end-to-end framework... Code will be released at https://github.com/TACJu/Bi-Sampling.