Tackling multi-object tracking and segmentation (MOTS) can be attributed to a multi-task learning task, i.e., performing Segmentation and Identity Embedding jointly (SIEJ). Unfortunately, achieving optimal SIEJ is non-trivial, as it relies on different spatiotemporal features of objects. Besides, the lack of labeled data raises the difficulty of balancing the two objectives in SIEJ. Empowered by the Segment Anything Model (SAM), we propose an Anchor-Prompt-based Segmentation and Embedding Model (APSEM) towards optimal SIEJ by introducing redundant anchors and designing an embedding decoder. On the one hand, our APSEM allows redundant anchors for the same object to identify occluded objects and refine the pixel-wise edge mask. On the other hand, our proposed embedding decoder is designed to address identity learning for redundant anchors by minimizing differences between the same identities in redundant prompts. We also create a parameter-efficient fine-tuning strategy, which helps combine the embedding module into the foundation model through a bit of data. Experimental results on MOTSChallenge datasets validate the effectiveness of the proposed APSEM method for MOTS tasks. Such results also demonstrate that each module improves segmentation and identity embedding performance through joint training.

Recently, the bottleneck of multi-object tracking is shifting from detection performance to association performance. However, research on association algorithms requires a large number of identity labels, which are more expensive than detection labels. To circumvent the need for identity labels, we propose a Self-supervised Re-identification module for online joint Multi-Object Tracking (SR-MOT). Specifically, we design an appearance discriminator to judge identities based solely on detection hypotheses and then associate the same identity with the final trajectory. To train the discriminator without using identity labels, we construct negative pairs by the detections that appear in the same video frame, as they definitely belong to different identities. Positive pairs are naturally constructed through several useful data augmentation strategies at the box level. In addition, our proposed method balances conflicting detection and re-ID tasks by using different output features and dynamically adjusts detection and re-ID loss weights based on the information content of the loss distribution to promote balance between the two tasks from the feature level and optimization methods. In our evaluation on the MOT Challenge benchmark, we show that our SR-MOT performs comparably to supervised methods and is significantly superior to other unsupervised methods. Our proposed method provides a practical solution for multi-object tracking without the need for identity labels, making it more accessible for real-world applications.

Visual Question Answering (VQA) models often prioritize language cues over visual knowledge, leading to the "language prior" phenomenon. To address this, researchers have proposed methods to balance language and image information during training and inference. However, these approaches often struggle to capture important linguistic components due to the excessive exclusion of language information. Inspired by causal inference, we introduce a novel approach called the SyMmetrically Balanced Causal framework (SMBC) that rebalances visual and textual information in VQA tasks. This framework allows for an equal contribution of knowledge from both modalities to inference results. Experimental evaluation shows that SMBC: 1) applies to prevalent VQA models, including those with data augmentation, and 2) consistently improves performance on established benchmarks.

Energy efficient architecture is essential to improve both the performance and power consumption of a computer system. However, modern computers suffer from the severe “memory wall” problem due to the significant performance gap between the processor technology and the memory technology. Thus, the computer architecture community is evolving from compute-centric to memory-centric designs to reduce the data movement overhead. This paper presents a comprehensive survey of the main challenges and recent advances in memory-centric energy efficient computer architecture. We summarize two research directions: improving the memory technology and processing closer to memory. The former focuses on optimizing the conventional memory technology and exploiting emerging non-volatile memory (NVM) technology. The latter talks about currently popular processing in memory (PIM) technology, including near-memory processing (NMP) and in-memory processing (IMP). Moreover, some other topics like hardware for machine learning (ML), ML for hardware, security, privacy, and reliability are gaining increasing attention and should be considered seriously in the design phase of a computer system. The community is facing various challenges and opportunities simultaneously, requiring researchers to have a more comprehensive understanding of this field which is also the goal of this paper.