CVPR 2024 Papers

Unsupervised fine-grained image hashing aims to learn compact binary hash codes in unsupervised settings addressing challenges posed by large-scale datasets and dependence on supervision. In this paper we first identify a granularity gap between generic and fine-grained datasets for unsupervised hashing methods highlighting the inadequacy of conventional self-supervised learning for fine-grained visual objects. To bridge this gap we propose the Asymmetric Augmented Self-Supervised Learning (A^2-SSL) method comprising three modules. The asymmetric augmented SSL module employs suitable augmentation strategies for positive/negative views preventing fine-grained category confusion inherent in conventional SSL. Part-oriented dense contrastive learning utilizes the Fisher Vector framework to capture and model fine-grained object parts enhancing unsupervised representations through part-level dense contrastive learning. Self-consistent hash code learning introduces a reconstruction task aligned with the self-consistency principle guiding the model to emphasize comprehensive features particularly fine-grained patterns. Experimental results on five benchmark datasets demonstrate the superiority of A^2-SSL over existing methods affirming its efficacy in unsupervised fine-grained image hashing.

Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden of time-consuming pixel-level manual labeling which leverages limited labeled data along with larger amounts of unlabeled data. Current state-of-the-art methods train the labeled data with ground truths and unlabeled data with pseudo labels. However the two training flows are separate which allows labeled data to dominate the training process resulting in low-quality pseudo labels and consequently sub-optimal results. To alleviate this issue we present AllSpark which reborns the labeled features from unlabeled ones with the channel-wise cross-attention mechanism. We further introduce a Semantic Memory along with a Channel Semantic Grouping strategy to ensure that unlabeled features adequately represent labeled features. The AllSpark shed new light on the architecture level designs of SSSS rather than framework level which avoids increasingly complicated training pipeline designs. It can also be regarded as a flexible bottleneck module that can be seamlessly integrated into a general transformer-based segmentation model. The proposed AllSpark outperforms existing methods across all evaluation protocols on Pascal Cityscapes and COCO benchmarks without bells-and-whistles. Code and model weights are available at: https://github.com/xmed-lab/AllSpark.

Nonlinearities are decisive in neural representation learning. Traditional Activation (Act) functions impose fixed inductive biases on neural networks with oriented biological intuitions. Recent methods leverage self-gated curves to compensate for the rigid traditional Act paradigms in fitting flexibility. However substantial improvements are still impeded by the norm-induced mismatched feature re-calibrations (see Section 1) i.e. the actual importance of a feature can be inconsistent with its explicit intensity such that violates the basic intention of a direct self-gated feature re-weighting. To address this problem we propose to learn discriminative neural feature Act with a novel prototype namely AdaShift which enhances typical self-gated Act by incorporating an adaptive shift factor into the re-weighting function of Act. AdaShift casts dynamic translations on the inputs of a re-weighting function by exploiting comprehensive feature-filter context cues of different ranges in a simple yet effective manner. We obtain the new intuitions of AdaShift by rethinking the feature-filter relationships from a common Softmax-based classification and by generalizing the new observations to a common learning layer that encodes features with updatable filters. Our practical AdaShifts built upon the new Act prototype demonstrate significant improvements to the popular/SOTA Act functions on different vision benchmarks. By simply replacing ReLU with AdaShifts ResNets can match advanced Transformer counterparts (e.g. ResNet-50 vs. Swin-T) with lower cost and fewer parameters.

This work proposes the first online asymmetric semi-supervised framework namely A-Teacher for LiDAR-based 3D object detection. Our motivation stems from the observation that 1) existing symmetric teacher-student methods for semi-supervised 3D object detection have characterized simplicity but impede the distillation performance between teacher and student because of the demand for an identical model structure and input data format. 2) The offline asymmetric methods with a complex teacher model constructed differently can generate more precise pseudo-labels but is challenging to jointly optimize the teacher and student model. Consequently in this paper we devise a different path from the conventional paradigm which can harness the capacity of a strong teacher while preserving the advantages of online teacher model updates. The essence is the proposed attention-based refinement model that can be seamlessly integrated into a vanilla teacher. The refinement model works in the divide-and-conquer manner that respectively handles three challenging scenarios including 1) objects detected in the current timestamp but with suboptimal box quality 2) objects are missed in the current timestamp but are detected in past or future frames 3) objects are neglected in all frames. It is worth noting that even while tackling these complex cases our model retains the efficiency of the online teacher-student semi-supervised framework. Experimental results on Waymo show that our method outperforms previous state-of-the-art HSSDA for 4.7 on mAP (L1) while consuming fewer training resources.