Contrastive learning is a powerful learning paradigm that can fully exploit cheap, self-supervised learning signals in the data. Crucial to its success is the choice of the positive and negative pairs, yet they are typically based on simple heuristics, and may generate low-quality or even harmful supervision. How to identify beneficial and harmful contrastive pairs? Most of the existing works make the decision based on the input samples directly, which is a static view and ignores how the learning process would respond to such learning constraints with varying level of quality and feasibility (or difficulty). In this paper, we propose instead to use the solution path of the contrastive learning process itself as a more dynamic guidance. Specifically, we use a step-wise reweighting on the contrastive loss of each sample pair, based on their representations that are being optimized in each step of the back-propagation as an instant feedback. We show that, when the re-weighting function is inversely proportional to the updated sample similarities, we can achieve a dynamic denoising of the contrastive loss, so that those "infeasible" and "accomplished" sample pairs are naturally discounted, while those "achievable yet unfinished" ones naturally emphasized. Our reweighting scheme, which we call DynaDeno (Dynamic Denoising), is a unified framework for continuously improving the allocation of the learning resources in contrastive learning, which takes into account both the different levels of uncertainty with positive and negative sample pairs, as well as how the learning process responds to such constraints. When applied to GNN-based node classification, our approach has demonstrated promising results on widely used benchmark datasets.

Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years. However, existing studies dominantly request prior knowledge of the tracking target (eg, pedestrians), and hence may not generalize well to unseen categories. In contrast, Generic Multiple Object Tracking (GMOT), which requires little prior information about the target, is largely under-explored. In this paper, we make contributions to boost the study of GMOT in three aspects. First, we construct the first publicly available dense GMOT dataset, dubbed GMOT-40, which contains 40 carefully annotated sequences evenly distributed among 10 object categories. In addition, two tracking protocols are adopted to evaluate different characteristics of tracking algorithms. Second, by noting the lack of devoted tracking algorithms, we have designed a series of baseline GMOT algorithms. Third, we perform a thorough evaluations on GMOT-40, involving popular MOT algorithms (with necessary modifications) and the proposed baselines. The GMOT-40 benchmark is publicly available at https://github.com/Spritea/GMOT40.

In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking. LaSOT consists of 1,400 sequences with more than 3.5M frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box, making LaSOT the largest, to the best of our knowledge, densely annotated tracking benchmark. The average video length of LaSOT is more than 2,500 frames, and each sequence comprises various challenges deriving from the wild where target objects may disappear and re-appear again in the view. By releasing LaSOT, we expect to provide the community with a large-scale dedicated benchmark with high quality for both the training of deep trackers and the veritable evaluation of tracking algorithms. Moreover, considering the close connections of visual appearance and natural language, we enrich LaSOT by providing additional language specification, aiming at encouraging the exploration of natural linguistic feature for tracking. A thorough experimental evaluation of 35 tracking algorithms on LaSOT is presented with detailed analysis, and the results demonstrate that there is still a big room for improvements.